3
Design and Analysis of Experiments
http://sms.cam.ac.uk/collection/125
Design of experiments was born as a result of an unlikely, but true anecdote: a lady claimed before R.A. Fisher that she was able to ascertain whether milk was poured before or after tea in her cuppa. Fisher devised a study to verify her claim and, in turn, this gave birth to Experimental Design. Agricultural experiments formed the core around which the theory evolved in its origins in the 1930s. The theory of Design of Experiments has since blossomed into many different approaches, ranging from optimal designs for dynamical models in pharmacokynetic studies, and designs for industrial experimentation, to designs of simulation experiments in climate change, to name but a few. The mathematical techniques currently used in different branches of Design are vast, for example Galois theory, non linear optimization, algebraic geometry and association schemes.
This media collection contains seminars from the following INI programmes:
Design and Analysis of Experiments 2011: http://www.newton.ac.uk/programmes/DAE/
Design of Experiments 2008: http://www.newton.ac.uk/programmes/DOE/
1440
2015
Thu, 23 Jul 2015 17:46:05 +0100
Mon, 08 Sep 2008 17:37:23 +0100
en
smssupport@ucs.cam.ac.uk
Design and Analysis of Experiments
http://sms.cam.ac.uk/collection/125
http://rss.sms.cam.ac.uk/itunesimage/1393641.jpg
http://video.search.yahoo.com/mrss
Design and Analysis of Experiments
Design of experiments was born as a result of an unlikely, but true anecdote: a lady claimed before R.A. Fisher that she was able to ascertain whether milk was poured before or after tea in her cuppa. Fisher devised a study to verify her claim and, in turn, this gave birth to Experimental Design. Agricultural experiments formed the core around which the theory evolved in its origins in the 1930s. The theory of Design of Experiments has since blossomed into many different approaches, ranging from optimal designs for dynamical models in pharmacokynetic studies, and designs for industrial experimentation, to designs of simulation experiments in climate change, to name but a few. The mathematical techniques currently used in different branches of Design are vast, for example Galois theory, non linear optimization, algebraic geometry and association schemes.
This media collection contains seminars from the following INI programmes:
Design and Analysis of Experiments 2011: http://www.newton.ac.uk/programmes/DAE/
Design of Experiments 2008: http://www.newton.ac.uk/programmes/DOE/
Design and Analysis of Experiments
Design of experiments was born as a result of an unlikely, but true anecdote: a lady claimed before R.A. Fisher that she was able to ascertain whether milk was poured before or after tea in her cuppa. Fisher devised a study to verify her claim and, in turn, this gave birth to Experimental Design. Agricultural experiments formed the core around which the theory evolved in its origins in the 1930s. The theory of Design of Experiments has since blossomed into many different approaches, ranging from optimal designs for dynamical models in pharmacokynetic studies, and designs for industrial experimentation, to designs of simulation experiments in climate change, to name but a few. The mathematical techniques currently used in different branches of Design are vast, for example Galois theory, non linear optimization, algebraic geometry and association schemes.
This media collection contains seminars from the following INI programmes:
Design and Analysis of Experiments 2011: http://www.newton.ac.uk/programmes/DAE/
Design of Experiments 2008: http://www.newton.ac.uk/programmes/DOE/
Cambridge University
Steve Greenham
http://sms.cam.ac.uk/collection/125
Design and Analysis of Experiments
20080908T17:37:23+01:00
INIMS
101092
no

"To estimate or to predict"  implications on the design for linear mixed models
ucs_sms_125_1162221
http://sms.cam.ac.uk/media/1162221
"To estimate or to predict"  implications on the design for linear mixed models
Schwabe, R (Otto Von Guericke)
Tuesday 09 August 2011, 11:0011:45
Wed, 10 Aug 2011 13:37:09 +0100
108
108100
Schwabe, R
Steve Greenham
Isaac Newton Institute
Schwabe, R
ec9b732cf3ac5503c5b9773f12c7fc9d
debd42f13d4059bfdeb0645a46049049
03639a2157861bf010c05a5af469ebf0
d450b15d1114e6c737718bca4bb5130a
836d94ad1931f3817ccd41bb13bac558
Schwabe, R (Otto Von Guericke)
Tuesday 09 August 2011, 11:0011:45
Schwabe, R (Otto Von Guericke)
Tuesday 09 August 2011, 11:0011:45
Cambridge University
2935
http://sms.cam.ac.uk/media/1162221
"To estimate or to predict"  implications on the design for linear mixed models
Schwabe, R (Otto Von Guericke)
Tuesday 09 August 2011, 11:0011:45
During the last years mixed models have attracted an increasing popularity in many fields of applications due to advanced computer facilities. Although the main theme of the present workshop is devoted to optimal design of experiments for nonlinear mixed models, it may be illustrative to elaborate the specific features of mixed models already in the linear case: Besides the estimation of population (location) parameters for the mean behaviour of the individuals a prediction of the response for the specific individuals under investigation may be of prior interest, for example in oncology studies to determine the further treatment of the patients investigated. While there have been some recent developments in optimal design for estimating the population parameters, the problem of optimal design for prediction has been considered as completely solved since the seminal paper by Gladitz and Pilz (1982). However, the optimal designs obtained there require the population parameters to be known or may be considered as an approximation, if the number of individuals is large. The latter may be inadequate, when the resulting "optimal design" fails to allow for estimation of the population parameters. Therefore we will develop the theory and solutions for finite numbers of individuals. Finally we will illustrate the tradeoff in optimal designs caused by the two competing aims of estimation and prediction by a simple example. Gladitz, J. and J. Pilz (1982): Construction of optimal designs in random coefficient regression models. Statistics 13, 371385.
20140203T12:56:34+00:00
2935
1162221
true
16x9
false
no

10 years of progress in population design methodology and applications
ucs_sms_125_2025459
http://sms.cam.ac.uk/media/2025459
10 years of progress in population design methodology and applications
Mentré, F ([INSERM, Paris])
Tuesday 7th July 2015, 10:05  10:30
Fri, 10 Jul 2015 12:46:22 +0100
Isaac Newton Institute
Mentré, F
41293be00bcc8f9f63ff5970c5a2189a
37cc098f548d353c69f46fbfccc76c59
28c81f38849ae78e6645511f854dc292
0ad47f6a8572020881a1865510e4f6a5
Mentré, F ([INSERM, Paris])
Tuesday 7th July 2015, 10:05  10:30
Mentré, F ([INSERM, Paris])
Tuesday 7th July 2015, 10:05  10:30
Cambridge University
1898
http://sms.cam.ac.uk/media/2025459
10 years of progress in population design methodology and applications
Mentré, F ([INSERM, Paris])
Tuesday 7th July 2015, 10:05  10:30
20150710T12:46:23+01:00
1898
2025459
true
16x9
false
no

A Bayesian Adaptive Design with Biomarkers for Targeted Therapies and Some Commentary on Adaptive Designs
ucs_sms_125_1166554
http://sms.cam.ac.uk/media/1166554
A Bayesian Adaptive Design with Biomarkers for Targeted Therapies and Some Commentary on Adaptive Designs
Kim, K (Wisconsin)
Thursday 18 August 2011, 14:4515:30
Tue, 23 Aug 2011 18:18:35 +0100
108
Isaac Newton Institute
Kim, K
fdeb290e8942b3804747d998d1406b18
3184f8746112dae6800bd550cc9c3a28
4907ea4c9fd64e85b56c44ce266d0952
14b48688db352e787f1612adae1840db
eb700ffcc197870ed5df8a9afb2e297e
Kim, K (Wisconsin)
Thursday 18 August 2011, 14:4515:30
Kim, K (Wisconsin)
Thursday 18 August 2011, 14:4515:30
Cambridge University
2937
http://sms.cam.ac.uk/media/1166554
A Bayesian Adaptive Design with Biomarkers for Targeted Therapies and Some Commentary on Adaptive Designs
Kim, K (Wisconsin)
Thursday 18 August 2011, 14:4515:30
Pharmacogenomic biomarkers are considered an important component of targeted therapies as they can potentially be used to identify patients who are more likely to benefit from them. New study designs may be helpful which can evaluate both the prognosis based on the biomarkers and the response from targeted therapies. In this talk I will present a recently developed Bayesian responseadaptive design. The design utilizes individual pharmacogenomic profiles and clinical outcomes as they become available during the course of the trial to assign most effective treatment to patients. I will present simulation studies of the proposed design. In closing I will share my perspectives on adaptive designs in general.
20140203T11:48:40+00:00
2937
1166554
true
16x9
false
no

A blocking strategy for orthogonal arrays of strength 2
ucs_sms_125_1446
http://sms.cam.ac.uk/media/1446
A blocking strategy for orthogonal arrays of strength 2
Schoen, ED (Antwerp and Delft)
Tuesday 12 August 2008, 09:3010:00
Thu, 11 Sep 2008 10:14:12 +0100
108
Steve Greenham
Schoen, ED
Isaac Newton Institute
Schoen, ED
66e55bb1e38af9b7232cb965151d657e
f96a8aeb63ac2a063e240555655bb37d
89503ddfbaf013f135ba2cb5160f5eb9
5623f9ca51877917502b0b9045cf16bf
a8501794ca2de9bd766ac31669468c75
f4d6a4ff1b27d3c59af9a08b547c82d5
04c8ac35dc74be2333ac1bd9115a9180
Schoen, ED (Antwerp and Delft)
Tuesday 12 August 2008, 09:3010:00
Schoen, ED (Antwerp and Delft)
Tuesday 12 August 2008, 09:3010:00
Cambridge University
1859
http://sms.cam.ac.uk/media/1446
A blocking strategy for orthogonal arrays of strength 2
Schoen, ED (Antwerp and Delft)
Tuesday 12 August 2008, 09:3010:00
Orthogonal arrays (OAs) of strength 2 permit independent estimation of main effects. An orthogonally blocked OA could be considered as an OA with one additional factor. Such an OA is in fact a twostratum design. The main effects of the treatment factors are estimated in the bottom stratum. The upper stratum may contain interaction components for these factors. The blocking factor supposedly has no interactions with treatment factors. I propose to search for suitable blocking arrangements by studying projections of arrays into those with one factor less. I illustrate with a complete catalogue of blocked pure or mixed 18run arrays.
http://www.newton.ac.uk/programmes/DOE/seminars/index.html
20140203T11:48:58+00:00
1859
1446
true
4x3
false
no

A Brief History of DCEs and Several Important Challenge
ucs_sms_125_1166369
http://sms.cam.ac.uk/media/1166369
A Brief History of DCEs and Several Important Challenge
Louviere, J (U of Technology, Sydney)
Wednesday 17 August 2011, 09:0009:45
Tue, 23 Aug 2011 08:36:44 +0100
Louviere, J
Isaac Newton Institute
Louviere, J
8a4e28188443d36b2f89d1bc5eecaba3
4df966d3b2d0e596c461a0fde52dcbfd
b1f1dc6eb81d7a56c18d7aeaa9770e55
d243c7ffcb1bafa2083bed936d1efb95
ea96f3ed91bf194df874d679c272850a
Louviere, J (U of Technology, Sydney)
Wednesday 17 August 2011, 09:0009:45
Louviere, J (U of Technology, Sydney)
Wednesday 17 August 2011, 09:0009:45
Cambridge University
2922
http://sms.cam.ac.uk/media/1166369
A Brief History of DCEs and Several Important Challenge
Louviere, J (U of Technology, Sydney)
Wednesday 17 August 2011, 09:0009:45
A confrontation with reality led to integration of conjoint measurement, discrete multivariate analysis of contingency tables, random utility theory and discrete choice models and design of statistical experiments. Few seem to realise that discrete choice experiments (DCEs) are in fact sparse, incomplete contingency tables. Thus, much of that literature informs and assists design and analysis of DCEs, such that often complex statistical models are largely unnecessary. Many lack this perspective, and hence much of the literature is dominated by modeldriven views of the design and analysis of DCEs.
The transition from the first DCEs to the present was very incremental and haphazard, with many advances being driven by market confrontations. For example "availability" designs arose from being asked to solve problems with outofstock conditions, infrastructure interruptions (eg, road or bridge closures), etc. Progress became more rapid and systematic from the late 1990s onwards, particularly with researchers skilled in optimal design theory getting involved in the field. Thus, there have been major strides in the optimal design of DCEs, but there now seems to be growing awareness that experiments on humans pose interesting issues for "optimal" design, particularly designs that seek to optimise statistical efficiency.
Along the way we stumbled onto individuals, error variance differences, cognitive process differences and we're still stumbling.
This talk is about a journey that starts in 1927 with paired comparisons, travels along an ad hoc path until it runs into an airline in 1978, emerges five years later as a systematic way to design and implement multiple comparisons, and slowly wanders back and forth until it begins to pick up speed and follow a "more optimal" path. Where is it going? Well, one researcher's optimum, may well be one human's suboptimum. Where should it be going? The road ahead is littered with overconfidence and assumptions. A better path is to invest in insurance against ignorance and assumptions.
20110823T08:36:54+01:00
2922
1166369
true
16x9
false
no

A Class of ThreeLevel Designs for Definitive Screening in the Presence of SecondOrder Effects
ucs_sms_125_1169909
http://sms.cam.ac.uk/media/1169909
A Class of ThreeLevel Designs for Definitive Screening in the Presence of SecondOrder Effects
Jones, B (JMP)
Friday 02 September 2011, 14:3015:00
Mon, 05 Sep 2011 12:43:07 +0100
Jones, B
Steve Greenham
Isaac Newton Institute
Jones, B
4d137ea8fdbcb39cb3d09666a09eda05
63c0902a3daaf7288f41016355e1aaaa
f5735d46899ce13b53ee8bbafeca8268
662744abc6e07f117ad4b3296a67dca3
27f0b5c2c6c0dc4b753fa0c6c8ef0999
Jones, B (JMP)
Friday 02 September 2011, 14:3015:00
Jones, B (JMP)
Friday 02 September 2011, 14:3015:00
Cambridge University
1613
http://sms.cam.ac.uk/media/1169909
A Class of ThreeLevel Designs for Definitive Screening in the Presence of SecondOrder Effects
Jones, B (JMP)
Friday 02 September 2011, 14:3015:00
Screening designs are attractive for assessing the relative impact of a large number of factors on a response of interest. Experimenters often prefer quantitative factors with three levels over twolevel factors because having three levels allows for some assessment of curvature in the factorresponse relationship. Yet, the most familiar screening designs limit each factor to only two levels. We propose a new class of designs that have three levels, provide estimates of main effects that are unbiased by any secondorder effect, require only one more than twice as many runs as there are factors, and avoid confounding of any pair of secondorder effects. Moreover, for designs having six factors or more, our designs allow for the estimation of the full quadratic model in any three factors. In this respect, our designs may render followup experiments unnecessary in many situations, thereby increasing the efficiency of the entire experimentation process.
20110905T12:43:17+01:00
1613
1169909
true
16x9
false
no

A clusterrandomised crossover trial
ucs_sms_125_1165351
http://sms.cam.ac.uk/media/1165351
A clusterrandomised crossover trial
White, I (MRC)
Monday 15 August 2011, 16:3517:15
Wed, 17 Aug 2011 14:41:21 +0100
White, I
Isaac Newton Institute
White, I
3cc010724279585157fb8e837894625e
85e05d4c31924429aa9bbb2b60ad921a
cc857d6ca5c49da62f76b33915f94903
4c48d5990fcc06d8974054acae9cbcc1
a62c891f2a52d6c994bbaa3924b5bb51
White, I (MRC)
Monday 15 August 2011, 16:3517:15
White, I (MRC)
Monday 15 August 2011, 16:3517:15
Cambridge University
2202
http://sms.cam.ac.uk/media/1165351
A clusterrandomised crossover trial
White, I (MRC)
Monday 15 August 2011, 16:3517:15
I will describe a trial which combined a clusterrandomised design with a crossover design. The Preterm Infant Parenting (PIP) trial evaluated a nurseled training intervention delivered to parents of prematurely born babies to help them meet their babies' needs. An individually randomised trial risked extensive "contamination" of parents in the control arm with knowledge of the intervention, so the investigators instead randomised neonatal units. However, neonatal units differ widely, and only 6 neonatal units were available, so a conventional cluster randomised design would have been underpowered. In the selected design, the six neonatal units were randomly allocated to deliver intervention or control to families recruited during a first 6month period; after a 2month interval, each unit then delivered the opposite condition to families recruited during a second 6month period.
I will present the relative precisions of individually randomised, clusterrandomised and clustercrossover designs, and design issues including the need for a washout period to minimise carryover. The analysis can be conveniently done using clusterlevel summaries. I will end by discussing whether clustercrossover designs should be more widely used.
20110817T14:41:30+01:00
2202
1165351
true
16x9
false
no

A comparison of three Bayesian approaches for constructing model robust designs
ucs_sms_125_1169933
http://sms.cam.ac.uk/media/1169933
A comparison of three Bayesian approaches for constructing model robust designs
Agboto, V (Meharry Medical College)
Friday 02 September 2011, 15:0015:30
Mon, 05 Sep 2011 12:54:04 +0100
Agboto, V
Steve Greenham
Isaac Newton Institute
Agboto, V
b916b5ece5033cad5e6fd84274a825c8
de6632aceb0ba834169a23e1af5280c0
c6ec7f8aa46aedf07a136fb33e7e7dcc
12c132e480826cc7f938e5486b2749d9
8de8c0162b7c338890fdbc84476dba07
Agboto, V (Meharry Medical College)
Friday 02 September 2011, 15:0015:30
Agboto, V (Meharry Medical College)
Friday 02 September 2011, 15:0015:30
Cambridge University
1130
http://sms.cam.ac.uk/media/1169933
A comparison of three Bayesian approaches for constructing model robust designs
Agboto, V (Meharry Medical College)
Friday 02 September 2011, 15:0015:30
While optimal designs are commonly used in the design of experiments, the optimality of those designs frequently depends on the form of an assumed model. Several useful criteria have been proposed to reduce such dependence, and efficient designs have been then constructed based on the criteria, often algorithmically. In the model robust design paradigm, a space of possible models is specified and designs are sought that are efficient for all models in the space. The Bayesian criterion given by DuMouchel and Jones (1994), posits a single model that contains both primary and potential terms. In this article we propose a new Bayesian model robustness criterion that combines aspects of both of these approaches. We then evaluate the efficacy of these three alternatives empirically. We conclude that the model robust criteria generally lead to improved robustness; however, the increased robustness can come at a significant cost in terms of computing requirements.
20110905T12:54:15+01:00
1130
1169933
true
16x9
false
no

A general method to determine sampling windows for nonlinear mixed effects models
ucs_sms_125_1164865
http://sms.cam.ac.uk/media/1164865
A general method to determine sampling windows for nonlinear mixed effects models
Duffull, S (Otago)
Friday 12 August 2011, 14:4515:30
Wed, 17 Aug 2011 08:42:29 +0100
Duffull, S
Steve Greenham
Isaac Newton Institute
Duffull, S
f6a3a0e23f262c0a0095a656cef1431f
0d1f5347036d9ff9c30bd50b54a4fd57
be178b99a7e8a4c857d247bdf7725b48
43eb912c6aef76329c1d40b42e6c2e2b
da3902423307caffdb16c637ec0d1941
Duffull, S (Otago)
Friday 12 August 2011, 14:4515:30
Duffull, S (Otago)
Friday 12 August 2011, 14:4515:30
Cambridge University
2736
http://sms.cam.ac.uk/media/1164865
A general method to determine sampling windows for nonlinear mixed effects models
Duffull, S (Otago)
Friday 12 August 2011, 14:4515:30
Many clinical pharmacology studies require repeated measurements to be taken on each patient and analysis of the data are conducted within the framework of nonlinear mixed effects models. It is increasingly common to design these studies using information theoretic principles due to the need for parsimony because of the presence of many logistical and ethical constraints. Doptimal design methods are often used to identify the best possible study conditions, such as the dose and number and timing of blood sample collection. However, the optimal times for collecting blood samples may not be feasible in clinical practice. Sampling windows, a time interval for blood sample collection, have been proposed to provide flexibility while preserving efficient parameter estimation. Due to the complexity of nonlinear mixed effects models there is generally no analytical solution available to determine sampling windows. We propose a method for determination of sampling windows based on MCMC sampling techniques. The proposed method reaches the stationary distribution rapidly and provides timesensitive windows around the optimal design points. The proposed method is applicable to determine windows around any continuous design variable for which repeated measures per run are required. This has particular importance for clinical pharmacology studies.
20110817T08:42:39+01:00
2736
1164865
true
16x9
false
no

A particle filter for Bayesian sequential design
ucs_sms_125_1168530
http://sms.cam.ac.uk/media/1168530
A particle filter for Bayesian sequential design
McGree, J (Queensland University of Technology)
Tuesday 30 August 2011, 12:0012:30
Thu, 01 Sep 2011 13:33:02 +0100
McGree, J
Steve Greenham
Isaac Newton Institute
McGree, J
579aaa538f8214e3af528f054a485398
57eec891323d57cd946685cb9759e121
8f47bdb08df2dabaccf61672d72d1483
39430efe21a6fc8c048d5644b15c9c99
0dccbc0e9464d3c433795b58df420924
McGree, J (Queensland University of Technology)
Tuesday 30 August 2011,...
McGree, J (Queensland University of Technology)
Tuesday 30 August 2011, 12:0012:30
Cambridge University
1454
http://sms.cam.ac.uk/media/1168530
A particle filter for Bayesian sequential design
McGree, J (Queensland University of Technology)
Tuesday 30 August 2011, 12:0012:30
A particle filter approach is presented for sequential design with a focus on Bayesian adaptive dose finding studies for the estimation of the maximum tolerable dose. The approach is computationally convenient in that the information of newly observed data can be incorporated through a simple reweighting step. Furthermore, the method does not require prior information represented as imagined data as in other dose finding approaches, although such data can be included straightforwardly if available. We also consider a flexible parametric model together with a newly developed hybrid design utility that can produce more robust estimates of the target dose in the presence of substantial model and parameter uncertainty.
20110901T13:33:12+01:00
1454
1168530
true
16x9
false
no

A populationfinding design with nonparametric Bayesian response model
ucs_sms_125_2024238
http://sms.cam.ac.uk/media/2024238
A populationfinding design with nonparametric Bayesian response model
Mueller, P (University of Texas at Austin)
Monday 6th July 2015, 16:15  17:00
Wed, 08 Jul 2015 16:11:44 +0100
Isaac Newton Institute
Mueller, P
3d60398bd2320e6b00a5b762f63c4f00
6d0b541850b211df0c337d5eb2659415
def156b4c3e6b1da2ebe2df1574f55ff
6e210176d61acacb8b4594991faeef94
Mueller, P (University of Texas at Austin)
Monday 6th July 2015, 16:15  17:00...
Mueller, P (University of Texas at Austin)
Monday 6th July 2015, 16:15  17:00
Cambridge University
3029
http://sms.cam.ac.uk/media/2024238
A populationfinding design with nonparametric Bayesian response model
Mueller, P (University of Texas at Austin)
Monday 6th July 2015, 16:15  17:00
Targeted therapies on the basis of genomic aberrations analysis of the tumor have become a mainstream direction of cancer prognosis and treatment. Studies that match patients to targeted therapies for their particular genomic aberrations, across different cancer types, are known as basket trials. For such trials it is important to find and identify the subgroup of patients who can most benefit from an aberrationspecific targeted therapy, possibly across multiple cancer types.
We propose an adaptive Bayesian clinical trial design for such subgroup identification and adaptive patient allocation. We start with a decision theoretic approach, then construct a utility function and a flexible nonparametric Bayesian response model. The main features of the proposed design and population finding methods are that we allow for variable sets of covariates to be recorded by different patients and, at least in principle, high order interactions of covariates. The separation of the decision problem and the probability model allows for the use of highly flexible response models. Another important feature is the adaptive allocation of patients to an optimal treatment arm based on posterior predictive probabilities. The proposed approach is demonstrated via extensive simulation studies.
20150708T16:11:45+01:00
3029
2024238
true
16x9
false
no

A sequential methodology for integrating physical and computer experiments
ucs_sms_125_1518
http://sms.cam.ac.uk/media/1518
A sequential methodology for integrating physical and computer experiments
Romano, D (Cagliari)
Friday 15 August 2008, 15:0015:30
Thu, 18 Sep 2008 08:59:16 +0100
Romano, D
Isaac Newton Institute
Romano, D
91aa543787c1395dd168d2a7fda878b0
fb6ac87bb267814d5c5cbb2cde7aad70
e93718b3989f19fa45b02ce3d2a03b38
8dc214c6323e35f652dd8290ab8aacc5
855da2b42fdf2a8095ec1f0ba833b889
5d56c0507690a1ba3d5461532ca33344
e87cfb8135ee3a1ad6f5dc51482ec275
Romano, D (Cagliari)
Friday 15 August 2008, 15:0015:30
Romano, D (Cagliari)
Friday 15 August 2008, 15:0015:30
Cambridge University
2138
http://sms.cam.ac.uk/media/1518
A sequential methodology for integrating physical and computer experiments
Romano, D (Cagliari)
Friday 15 August 2008, 15:0015:30
In advanced industrial sectors, like aerospace, automotive, microelectronics and telecommunications, intensive use of simulation and lab trials is already a daily practice in R\&D activities. In spite of this, there still is no comprehensive approach for integrating physical and simulation experiments in the applied statistical literature. Computer experiments, an autonomous discipline since the end of the eighties (Sacks et al., 1989, Santner et al., 2003), provides a limited view of what a "computer experiment" can be in an industrial setting (computer program is considered expensive to run and its output strictly deterministic) and has practically ignored the "integration" problem. Existing contributions mainly address the problem of calibrating the computer model basing on field data. Kennedy and O'Hagan (2001) and Bayarri et al.(2007) introduced a fully Bayesian approach for modeling also the bias between the computer model and the physical data, thus addressing also model validation, i.e. assessing how well the model represents reality. Nevertheless, in this body of research the role of physical observations is ancillary: they are generally a few and not subject to design.
In the fifties, Box and Wilson (1951) provided a framework, which they called sequential experimentation, for improving industrial systems by physical experiments. Knowledge on the system is built incrementally by organising the investigation as a sequence of related experiments with varying scope (screening, prediction, and optimisation).
A first attempt to introduce such a systemic view in the context of integrated physical and computer experiments is presented in the paper. We envisage a sequential approach where both physical and computer experiments are used in a synergistic way with the goals of improving a real system of interest and validating/improving the computer model. The whole process and stops when a satisfactory level of improvement is realised.
It is important to point out that the two sources of information have a distinct role as they produce information with different degrees of cost (speed) and reliability. In a typical situation where the simulator is cheaper (faster) and the physical setup is more reliable, it is sensible to use simulation experiments for exploring the space of the design variables in depth in order to get innovative findings, and to use a moderate amount of the costly physical trials for the verification of the findings. If findings obtained by simulation are not confirmed in the field, the computer code should be revised accordingly.
Different decision levels are handled within the framework. High level decisions are whether to stop or continue, whether to conduct the next experiment on the physical system or on its simulator and which is the purpose of the experiment (exploration, improvement, confirmation, model validation). Intermediate level decisions are the location of the experimental region and the run size. L
http://www.newton.ac.uk/programmes/DOE/seminars/index.html
20130327T14:39:39+00:00
2138
1518
true
4x3
false
no

A Short Overview of Orthogonal Arrays
ucs_sms_125_1171657
http://sms.cam.ac.uk/media/1171657
A Short Overview of Orthogonal Arrays
John Stufken (Georgia)
Monday 05 September 2011, 17:1518:15
Tue, 13 Sep 2011 13:26:47 +0100
John Stufken
Steve Greenham
Isaac Newton Institute
John Stufken
dc4a533974d2b6fecdb487e68e8f68ab
7f4a86cb398d4bd65eb6bb2ef432192e
f366442bc0aede70a3b70c3f8b2bf187
669c2818ba62203dffb20fd65bb95f90
f74e5c54ce9b7a2f97597e90d9454a1e
John Stufken (Georgia)
Monday 05 September 2011, 17:1518:15
John Stufken (Georgia)
Monday 05 September 2011, 17:1518:15
Cambridge University
3909
http://sms.cam.ac.uk/media/1171657
A Short Overview of Orthogonal Arrays
John Stufken (Georgia)
Monday 05 September 2011, 17:1518:15
Combinatorial arrangements now known as orthogonal arrays were introduced for use in statistics in the 1940's. The primary purpose for their introduction was to guide the selection of level combinations in a fractional factorial experiment, and this is still an important reason for their interest in statistics. Various criteria based on statistical properties have been introduced over the years to distinguish between different orthogonal arrays of the same size, and some authors have attempted to enumerate all nonisomorphic arrays of small sizes. Orthogonal arrays also possess interesting relationships to several other combinatorial arrangements, including to errorcorrecting codes and Hadamard matrices. In this talk, aimed at a general mathematical audience, we will present a brief and selective overview of orthogonal arrays, including their existence, construction, and relationships to other arrangements.
20110913T13:26:56+01:00
3909
1171657
true
16x9
false
no

A Visitors Guide to Experiment Design for Dynamic DiscreteEvent Stochastic Simulation
ucs_sms_125_1171979
http://sms.cam.ac.uk/media/1171979
A Visitors Guide to Experiment Design for Dynamic DiscreteEvent Stochastic Simulation
Nelson, B
Wednesday 07 September 2011, 09:3009:40
Wed, 14 Sep 2011 13:21:39 +0100
Isaac Newton Institute
Nelson, B
6bbde63bf1496fe7a73684ddbd221c3c
8fe36e109b424aa15c45b7ece13e4172
63e305ec194672c6505143f7c46d1612
02771644e3d1a12391a3c3fb47072202
1461e594b4637c43c94c7449cd5fee9a
Nelson, B
Wednesday 07 September 2011, 09:3009:40
Nelson, B
Wednesday 07 September 2011, 09:3009:40
Cambridge University
437
http://sms.cam.ac.uk/media/1171979
A Visitors Guide to Experiment Design for Dynamic DiscreteEvent Stochastic Simulation
Nelson, B
Wednesday 07 September 2011, 09:3009:40
20110914T13:21:49+01:00
437
1171979
true
16x9
false
no

Aoptimal block designs for the comparison of treatments with a control with autocorrelated errors
ucs_sms_125_1441
http://sms.cam.ac.uk/media/1441
Aoptimal block designs for the comparison of treatments with a control with autocorrelated errors
Kunert, J (Dortmund)
Tuesday 12 August 2008, 10:0010:30
Wed, 10 Sep 2008 14:20:12 +0100
Kunert, J
Isaac Newton Institute
Kunert, J
e1667edae40258d2f8c0512748058d31
fc759684fad1c6791b9a96c7c4e9516c
7bb1a52d8f8f71957651a5991f3b5504
01b756e7bcb12101a21d51c2789888cd
eb38f8a346cfa67ac4151827fb31c3b4
54280bd479e7f97964072a6118c5caf1
4343f5aa6bf49275cac656af8a2d6c90
Kunert, J (Dortmund)
Tuesday 12 August 2008, 10:0010:30
Kunert, J (Dortmund)
Tuesday 12 August 2008, 10:0010:30
Cambridge University
1379
http://sms.cam.ac.uk/media/1441
Aoptimal block designs for the comparison of treatments with a control with autocorrelated errors
Kunert, J (Dortmund)
Tuesday 12 August 2008, 10:0010:30
There is an extensive literature on optimal and efficient designs for comparing /t/ test (or new) treatments with a control (or standard treatment)  see Majumdar (1996). However, almost all results assume the observations are uncorrelated. In many situations, it is more realistic to assume that observations in the same block are positively correlated, and there has been much interest in this case when all contrasts are of equal interest  see, for example, Martin (1996).Assuming that the estimation uses ordinary leastsquares, Bhaumik (1990) found optimal withinblock orderings under a firstorder nearestneighbour model NN(1) among some designs that would have been optimal testcontrol designs under independence. Cutler (1993) obtained some optimality results under a firstorder autoregressive process AR(1) on the circle or the line, assuming generalised leastsquares estimation for a known dependence. There are also some brief examples and discussion of the correlated case in Martin & Eccleston (1993, 2001). Here, we concentrate on generalised leastsquares estimation for a known covariance. Results for independence, and Cutler's (1993) results for the AR(1), are for specific combinations of /t/, /b/, /k/, and use integer minimisation to ensure an optimal design exists. Here, we assume that the number of blocks /b/ is large enough for an optimal design to exist, and consider the form of that optimal design. This method may lead to exact optimal designs for some /b/, /t/, /k/, but usually will only indicate the structure of an efficient design for any particular /b/, /t/, /k/, and yield an efficiency bound, usually unattainable. The bound and the structure can then be used to investigate efficient finite designs.
http://www.newton.ac.uk/programmes/DOE/seminars/index.html
20130327T14:39:20+00:00
1379
1441
true
4x3
false
no

Accelerating Industrial Productivity via Deterministic Computer Experiments and Stochastic Simulation Experiments: Conference Organisation and Goals
ucs_sms_125_1171511
http://sms.cam.ac.uk/media/1171511
Accelerating Industrial Productivity via Deterministic Computer Experiments and Stochastic Simulation Experiments: Conference Organisation and Goals
Santner, T (Ohio State University)
Monday 05 September 2011, 10:0010:20
Tue, 13 Sep 2011 11:25:15 +0100
Isaac Newton Institute
Santner, T
ce1dfdba2340753594f9ab1bc18a467f
2f842c40ee36e94407a088fdbc47dc7a
e219f9c302d463b34504d8b73c166fb9
8194c4060bbe673e0077a5d50d678d44
480e50358cff61a4e450a9319a0580f8
Santner, T (Ohio State University)
Monday 05 September 2011, 10:0010:20
Santner, T (Ohio State University)
Monday 05 September 2011, 10:0010:20
Cambridge University
1003
http://sms.cam.ac.uk/media/1171511
Accelerating Industrial Productivity via Deterministic Computer Experiments and Stochastic Simulation Experiments: Conference Organisation and Goals
Santner, T (Ohio State University)
Monday 05 September 2011, 10:0010:20
Welcome and introduction to the "Accelerating Industrial Productivity via Deterministic Computer Experiments and Stochastic Simulation Experiments" conference.
20110913T11:25:24+01:00
1003
1171511
true
16x9
false
no

Accurate emulators for largescale computer experiments
ucs_sms_125_1172535
http://sms.cam.ac.uk/media/1172535
Accurate emulators for largescale computer experiments
Haaland, B (DukeNUS, NUS)
Friday 09 September 2011, 12:0012:30
Thu, 15 Sep 2011 13:32:29 +0100
Haaland, B
Steve Greenham
Isaac Newton Institute
Haaland, B
0d74b9507019806896fd4c98a7caf2b4
091739b6d56a5a02f30adfe196a161ad
9800500bff352d4fd950d8f3dc0385f1
e29293257aad0652c316e5f9abd10289
f183943cc84a6b2bc40583053b1dc144
Haaland, B (DukeNUS, NUS)
Friday 09 September 2011, 12:0012:30
Haaland, B (DukeNUS, NUS)
Friday 09 September 2011, 12:0012:30
Cambridge University
2094
http://sms.cam.ac.uk/media/1172535
Accurate emulators for largescale computer experiments
Haaland, B (DukeNUS, NUS)
Friday 09 September 2011, 12:0012:30
Largescale computer experiments are becoming increasingly important in science. A multistep procedure is introduced to statisticians for modeling such experiments, which builds an accurate interpolator in multiple steps. In practice, the procedure shows substantial improvements in overall accuracy but its theoretical properties are not well established. We introduce the terms nominal and numeric error and decompose the overall error of an interpolator into nominal and numeric portions. Bounds on the numeric and nominal error are developed to show theoretically that substantial gains in overall accuracy can be attained with the multistep approach.
20110915T13:32:38+01:00
2094
1172535
true
16x9
false
no

Adaptive design and control
ucs_sms_125_1158204
http://sms.cam.ac.uk/media/1158204
Adaptive design and control
Pronzato, L (Université de Nice Sophia Antipolis)
Wednesday 20 July 2011, 09:0010:00
Fri, 22 Jul 2011 14:32:49 +0100
Pronzato, L
Steve Greenham
Isaac Newton Institute
Pronzato, L
c398d3cce8b65b86eda0e8120f617bb3
8a8965ea845aaccf9716f1762dac59d6
9a8410ec3690ed56257262b246c6f103
c99c4f9fd8957fb667e27e2b1722278f
e09a4b90f6a279cab5cb884d962ab707
Pronzato, L (Université de Nice Sophia Antipolis)
Wednesday 20 July 2011,...
Pronzato, L (Université de Nice Sophia Antipolis)
Wednesday 20 July 2011, 09:0010:00
Cambridge University
3682
http://sms.cam.ac.uk/media/1158204
Adaptive design and control
Pronzato, L (Université de Nice Sophia Antipolis)
Wednesday 20 July 2011, 09:0010:00
There exist strong relations between experimental design and control, for instance in situations where optimal inputs are constructed in order to obtain precise parameter estimation in dynamical systems or when suitably designed perturbations are introduced in adaptive control to force enough excitation into the system. The presentation will focus on adaptive design when the construction of an optimal experiment requires the knowledge of the model parameters and current estimated values are substituted for unknown true values. This adaptation to estimated values creates dependency among observations and makes the investigation of the asymptotic behaviors of the design and estimator a much more complicated issue than when the design is specified independently of the observations. Also, even if the system considered is static, this adaptation introduces some feedback and the adaptivedesign mechanism can be considered as a particular adaptivecontrol scheme. The role of experimental design in the asymptotic properties of estimators will be emphasized. The assumption that the set of experimental variables (design points) is finite facilitates the study of the asymptotic properties of estimators (strong consistency and asymptotic normality) in stochastic regression models. Two situations will be considered: adaptive Doptimal design and adaptive design with a cost constraint where the design should make a compromise between maximizing an information criterion (Doptimality) and minimizing a cost (function optimization). The case when the weight given to cost minimization asymptotically dominates will be considered in detail in connection with selftuning regulation and selftuning optimization problems.
20110722T14:32:59+01:00
3682
1158204
true
16x9
false
no

Adaptive designs for clinical trials with prognostic factors that maximize utility
ucs_sms_125_1462
http://sms.cam.ac.uk/media/1462
Adaptive designs for clinical trials with prognostic factors that maximize utility
Atkinson, AC (London School of Economics)
Thursday 14 August 2008, 12:0012:30
Fri, 12 Sep 2008 17:53:36 +0100
Steve Greenham
Atkinson, AC
Isaac Newton Institute
Atkinson, AC
d3c88ed149a76bf90db6f357e2e22b9b
59f2496336d4d86f24cb213ba75cba48
94ebe8aaa4bdd57a2cd051dc3e7be060
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9437970930e81aa6d8dd7b5860ca338f
c23620a1afd98cb9ee5b9af79295eee3
234dd0c600c5e2fd02ab206a626cc163
Atkinson, AC (London School of Economics)
Thursday 14 August 2008, 12:0012:30
Atkinson, AC (London School of Economics)
Thursday 14 August 2008, 12:0012:30
Cambridge University
1750
http://sms.cam.ac.uk/media/1462
Adaptive designs for clinical trials with prognostic factors that maximize utility
Atkinson, AC (London School of Economics)
Thursday 14 August 2008, 12:0012:30
The talk concerns a typical problem in Phase III clinical trials, that is when the number of patients is large. Patients arrive sequentially and are to be allocated to one of $t$ treatments. When the observations all have the same variance an efficient design will be balanced over treatments and over the prognostic factors with which the patients present. However, there should be some randomization in the design, which will lead to slight imbalances. Furthermore, when the responses of earlier patients are already available, there is the ethical concern of allocating more patients to the better treatments, which leads to further imbalance and to some loss of statistical efficiency. The talk will describe the use of the methods of optimum experimental design to combine balance across prognostic factors with a controllable amount of randomization. Use of a utility function provides a specified skewing of the allocation towards better treatments that depends on the ordering of the treatments. The only parameters of the design are the asymptotic proportions of patients to be allocated to the ordered treatments and the extent of randomization. The design is a sophisticated version of those for binary responses that force a prefixed allocation. Comparisons will be made with other rules that employ link functions, where the target proportions depend on the differences between treatments, rather than just on their ranking. If time permits, the extension to binary and survivaltime models will be indicated. Mention will be made of the importance of regularization in avoiding trials giving extreme allocations. A simulation study fails to detect the effect of the adaptive design on inference. (Joint work with Atanu Biswas, Kolkata)
http://www.newton.ac.uk/programmes/DOE/seminars/index.html
20130228T11:59:47+00:00
1750
1462
true
4x3
false
no

Adaptive designs for dose escalation studies  a simulation study
ucs_sms_125_1499
http://sms.cam.ac.uk/media/1499
Adaptive designs for dose escalation studies  a simulation study
Roth, K (Bayer Schering Pharma AG)
Thursday 14 August 2008, 09:3010:00
Wed, 17 Sep 2008 07:23:30 +0100
Steve Greenham
Roth, K
Isaac Newton Institute
Roth, K
a46809b92a17a4fc8c687c57081c441a
54f551e7b583187a2466ab88bc98f5ad
0b65f9b9dce67ecc8409d368bcf28339
7ec1aa17cd8fe0d63d6846877786984c
498518418181d7ee73fdff2c6586fa02
e0e679830f96818d7e1163d5cb5d64b6
2a182da8f2f28f8b2520d4b56498a9f8
Roth, K (Bayer Schering Pharma AG)
Thursday 14 August 2008, 09:3010:00
Roth, K (Bayer Schering Pharma AG)
Thursday 14 August 2008, 09:3010:00
Cambridge University
1826
http://sms.cam.ac.uk/media/1499
Adaptive designs for dose escalation studies  a simulation study
Roth, K (Bayer Schering Pharma AG)
Thursday 14 August 2008, 09:3010:00
Dose escalation studies are used to find the maximum tolerated dose of a new drug. They are among the first studies where the new drug is used in humans, therefore little prior knowledge about the tolerability of the drug is available. Additionally, ethical restrictions have to be considered. To account for this, adaptive approaches are adequate. Most of the current standard methods like the 3+3design are not based on optimal design theory suggesting that there is room for improvement. In a simulation study using four different doseresponsescenarios, three adaptive approaches to find the maximum tolerated dose (MTD) are compared. The traditional 3+3design is compared to a Bayesian approach using the software tool "Bayesian ADEPT". The third approach is a parametric modification of the 3+3design, where the 3+3design is conducted until enough information is gathered to construct locally optimal designs based on a logistic model. It is shown that the Bayesian approach performs best in determining the correct MTD, but at the cost of treating a lot of patients at toxic doses, which makes it less feasible for practical use. The 3+3design is more conservative, tending to underestimate the MTD but treating only few patients at toxic doses. The parametric modification of the 3+3design has higher chances of finding the correct dose while increasing the risk for the treated patients only very slightly, and therefore is a promising alternative to the traditional 3+3design.
http://www.newton.ac.uk/programmes/DOE/seminars/index.html
20130228T11:59:24+00:00
1826
1499
true
4x3
false
no

Adaptive dosefinding with power control
ucs_sms_125_2024203
http://sms.cam.ac.uk/media/2024203
Adaptive dosefinding with power control
Miller, F (Stockholm University)
Monday 6th July 2015, 11:15  12:00
Wed, 08 Jul 2015 15:08:22 +0100
Isaac Newton Institute
Miller, F
349f298363309e8da3b8567d57cc5b29
981530472e8c09b6470dcb5021a2087f
2fb5fa4003e065bbe20a7aa5d7673271
05d2f0b9f1ed2191c2984830d9d80599
Miller, F (Stockholm University)
Monday 6th July 2015, 11:15  12:00
Miller, F (Stockholm University)
Monday 6th July 2015, 11:15  12:00
Cambridge University
2966
http://sms.cam.ac.uk/media/2024203
Adaptive dosefinding with power control
Miller, F (Stockholm University)
Monday 6th July 2015, 11:15  12:00
A main objective in dosefinding trials besides the characterisation of doseresponse relationship is often to prove that the drug has effect. The sample size calculation prior to the trial aims to control the power for this effect proof. In most cases however, there is great uncertainty concerning the anticipated effect of the drug during planning. Sample size reestimation based on an unblinded interim effect estimate has been used in this situation. In practice, this reestimation can have drawbacks as sample size becomes variable which makes planning and funding complicated. Further it introduces the risk that people start to speculate about the effect once the reestimated sample size is communicated. In this talk, we will investigate a design which avoids this problem but controls the power for the effect proof in an adaptive way. We discuss methods for proper statistical inference for the described design.
20150708T15:08:22+01:00
2966
2024203
true
16x9
false
no

Adaptive DoseRanging Designs with Two Efficacy Endpoints
ucs_sms_125_1165402
http://sms.cam.ac.uk/media/1165402
Adaptive DoseRanging Designs with Two Efficacy Endpoints
Dragalin, V (Aptiv Solutions, USA)
Tuesday 16 August 2011, 09:3010:00
Wed, 17 Aug 2011 15:05:27 +0100
Dragalin, V
Isaac Newton Institute
Dragalin, V
eaa3e4b96e35fae799ac1df979f1aa38
bede305f128087a60d0b7a35e0d15762
c7c10d4941f817ebb1f77e0b72f49a57
81c1288744f38068ef974844819d1138
d813496368e8a89719b70062e6e5d224
Dragalin, V (Aptiv Solutions, USA)
Tuesday 16 August 2011, 09:3010:00
Dragalin, V (Aptiv Solutions, USA)
Tuesday 16 August 2011, 09:3010:00
Cambridge University
2195
http://sms.cam.ac.uk/media/1165402
Adaptive DoseRanging Designs with Two Efficacy Endpoints
Dragalin, V (Aptiv Solutions, USA)
Tuesday 16 August 2011, 09:3010:00
Following the introduction of the continual reassessment method by O’Quigley, Pepe and Fisher, there has been considerable interest in formal statistical procedures for phase I dosefinding studies. The great majority of published accounts relate to cancer patients treated once with a single dose of the test drug who return a single binary observation concerning the incidence of toxicity. However, most phase I dosefinding studies are not of such a simple form. Drugs being developed for milder conditions than cancer are usually first tested in healthy volunteers who participate in multiple dosing periods, returning a continuous pharmacokinetic response each time.
This talk will describe Bayesian decision procedures which have been developed for such dosefinding studies in healthy volunteers. The principles behind the approach will be described and an evaluation of its properties presented. An account will be given of an implementation of the approach in a study conducted in Scandinavia. Generalisation to studies in which more than one response is used will also be discussed.
20110831T13:39:55+01:00
2195
1165402
true
16x9
false
no

Adaptive optimum experimental design in Phase I clinical trials
ucs_sms_125_1467
http://sms.cam.ac.uk/media/1467
Adaptive optimum experimental design in Phase I clinical trials
Bogacka, B (QMUL)
Thursday 14 August 2008, 10:0010:30
Mon, 15 Sep 2008 10:08:52 +0100
Bogacka, B
Isaac Newton Institute
Bogacka, B
d0d2d6399e9ca8baaa2d2ff182f0b9af
97b5faa97ce1a6acab81c202fb893aa0
705b1fba649940012766e55853d56b50
19efd5e7a86a6987011e758e692ea631
3d0c1df531a03fadc086d3bc09f19f8f
48d9dff61ca7a2e1ff98fd9d9d042631
1f85cf9855698bcee7fe8270f69417d0
Bogacka, B (QMUL)
Thursday 14 August 2008, 10:0010:30
Bogacka, B (QMUL)
Thursday 14 August 2008, 10:0010:30
Cambridge University
1726
http://sms.cam.ac.uk/media/1467
Adaptive optimum experimental design in Phase I clinical trials
Bogacka, B (QMUL)
Thursday 14 August 2008, 10:0010:30
The maximum tolerable dose in Phase I clinical trials may not only carry too much unnecessary risk for patients but may also not be the most efficacious level. This may occur when the efficacy of the drug is unimodal rather than increasing, while the toxicity will be an increasing function of the dose. It may be more beneficial to design a trial so that doses around the socalled Biologically Optimum Dose (BOD) are used more than other dose levels. Zhang at al (2006) presented simulation results for an adaptive design for a variety of models when the response is trinomial ("no response", "success" and "toxicity"). The choice of dose for the next cohort depends on the information gathered from previous cohorts, which provides an updated estimate of BOD for the next experiment. However, this reasonable approach is confined to a sparse grid of dose levels which may be far from the "true"' BOD. In our work we explore the scenarios used by Zhang but search for the BOD over a continuous dose interval. This increases the percentage of patients treated with a good approximation to the "true" BOD. However, more patients may be treated at a high toxicity probability level and so some further restrictions are introduced to increase the safety of the trial. We give examples of the properties of various design strategies and suggest future developments.
http://www.newton.ac.uk/programmes/DOE/seminars/index.html
20130228T11:56:15+00:00
1726
1467
true
4x3
false
no

Adaptive population enrichment designs
ucs_sms_125_2024231
http://sms.cam.ac.uk/media/2024231
Adaptive population enrichment designs
Dragalin, V (Johnson & Johnson PRD)
Monday 6th July 2015, 15:30  16:15
Wed, 08 Jul 2015 15:55:06 +0100
Isaac Newton Institute
Dragalin, V
4fd906c496ee6207f6073c0e261f219e
4b0d826c85ac1e81ba80b89a3c3827ab
6633fa5f3ae936f72d47632ebada9d4f
3be85525ad6255b2868334d37ad27189
Dragalin, V (Johnson & Johnson PRD)
Monday 6th July 2015, 15:30  16:15
Dragalin, V (Johnson & Johnson PRD)
Monday 6th July 2015, 15:30  16:15
Cambridge University
2456
http://sms.cam.ac.uk/media/2024231
Adaptive population enrichment designs
Dragalin, V (Johnson & Johnson PRD)
Monday 6th July 2015, 15:30  16:15
There is a growing interest among regulators and sponsors in using precision medicine approaches that allow for targeted patients to receive maximum benefit from the correct dose of a specific drug. Population enrichment designs offer a specific adaptive trial methodology to study the effect of experimental treatments in various subpopulations of patients under investigation. Instead of limiting the enrolment only to the enriched population, these designs enable the datadriven selection of one or more prespecified subpopulations at an interim analysis and the confirmatory proof of efficacy in the selected subset at the end of the trial. In this presentation, the general methodology and designing issues when planning such a design will be described and illustrated using two case studies.
20150708T15:55:06+01:00
2456
2024231
true
16x9
false
no

Advances in nonlinear geoscientific experimental and survey design
ucs_sms_125_1158673
http://sms.cam.ac.uk/media/1158673
Advances in nonlinear geoscientific experimental and survey design
Curtis, A (University of Edinburgh)
Friday 22 July 2011, 09:0010:00
Mon, 25 Jul 2011 10:30:46 +0100
Curtis, A
Steve Greenham
Isaac Newton Institute
Curtis, A
12b1350310be7b866beaefcd8c74756e
6975f512e39ad23407d36e3d31fff703
1367a3fb8b8b3a438c2fe261e1bf36ed
9824581d2eb276dc082fa71c6201e25b
2e255f472dfdd46ff06e1d9c9689a6ef
Curtis, A (University of Edinburgh)
Friday 22 July 2011, 09:0010:00
Curtis, A (University of Edinburgh)
Friday 22 July 2011, 09:0010:00
Cambridge University
3741
http://sms.cam.ac.uk/media/1158673
Advances in nonlinear geoscientific experimental and survey design
Curtis, A (University of Edinburgh)
Friday 22 July 2011, 09:0010:00
Geoscience is replete with inverse problems that must be solved routinely. Many such problems such as using satellite remotesensing data to estimate properties of the Earth's surface, or solving Geophysical imaging and monitoring problems for potentially dynamic properties of the Earth's subsurface, involve large datasets that cost millions of dollars to collect. Optimising the information content of such data is therefore crucial. While linearised experimental design methods have been deployed within the Geosciences, most Geophysical problems are significantly nonlinear. This renders linearised design criteria invalid as they can significantly over or underestimate the information content of any dataset. Over the past few years we have therefore focussed on developing new nonlinear design methods that can be applied to practical data types and geometries for surveys of increasing size. We will summarise three advances in practical nonlinear design, one using a new design criterion applied in the data space, one using a new 'bifocal' model space criterion, and one using a fast Monte Carlo refinement procedure that significantly speeds up nonlinear design calculations. Applications of the first two techniques are to design subsurface (micro)seismic energysource location problems, application of the third is to design socalled industrial seismic amplitudeversusoffset data sets to derive (an)elastic properties of subsurface geological strata. Using the first of these we managed to design an industrially practical Geophysical survey design using fully nonlinearised methods.
20110725T10:30:54+01:00
3741
1158673
true
16x9
false
no

An adaptive optimal design for the Emax model and its application in clinical trials
ucs_sms_125_1493
http://sms.cam.ac.uk/media/1493
An adaptive optimal design for the Emax model and its application in clinical trials
Leonov, S (GlaxoSmithKline)
Thursday 14 August 2008, 16:0016:30
Tue, 16 Sep 2008 12:31:36 +0100
Leonov, S
Isaac Newton Institute
Leonov, S
4dd292cc10e0e1a364b102dba4b4a8e1
cbede4f75719e10f84951f0c3a9fcace
7722fc1171e8f16c6887882580d18733
9ac57db7eb3f298f5b663fb437284823
3703ac3fe15b481985df8ba781263204
4db13d777d6dc1db263ecebb0af13271
56a21e3e1bd7e654a859cdafe3ee186f
Leonov, S (GlaxoSmithKline)
Thursday 14 August 2008, 16:0016:30
Leonov, S (GlaxoSmithKline)
Thursday 14 August 2008, 16:0016:30
Cambridge University
1855
http://sms.cam.ac.uk/media/1493
An adaptive optimal design for the Emax model and its application in clinical trials
Leonov, S (GlaxoSmithKline)
Thursday 14 August 2008, 16:0016:30
We discuss an adaptive design for a firsttimeinhuman doseescalation study in patients. A project team working on a compound wished to maximize the efficiency of the study by using doses targeted at maximizing information about the doseresponse relationship within certain safety constraints. We have developed an adaptive optimal design tool to recommend doses when the response follows an Emax model, with functionality for pretrial simulation and instream analysis. We describe the methodology based on modelbased optimal design techniques and present the results of simulations to investigate the operating characteristics of the applied algorithm.
http://www.newton.ac.uk/programmes/DOE/seminars/index.html
20130301T11:15:02+00:00
1855
1493
true
4x3
false
no

An adaptive optimal design with a small fixed stageone sample size
ucs_sms_125_2024217
http://sms.cam.ac.uk/media/2024217
An adaptive optimal design with a small fixed stageone sample size
Flournoy, N (University of MissouriColumbia)
Monday 6th July 2015, 13:30  14:15
Wed, 08 Jul 2015 15:10:41 +0100
Isaac Newton Institute
Flournoy, N
f83a465a8a2e108e2d7d650493606e89
2ebfdc6fca4323d74c3cca7e17caeb33
4f0fe1c22a16043c40fae2fdb9be0518
b9c4ffa76bdec8a78cc69d9e45ffc929
Flournoy, N (University of MissouriColumbia)
Monday 6th July 2015, 13:30 ...
Flournoy, N (University of MissouriColumbia)
Monday 6th July 2015, 13:30  14:15
Cambridge University
2646
http://sms.cam.ac.uk/media/2024217
An adaptive optimal design with a small fixed stageone sample size
Flournoy, N (University of MissouriColumbia)
Monday 6th July 2015, 13:30  14:15
A large number of experiments in clinical trials, biology, biochemistry, etc. are, out of necessity, conducted in two stages. A firststage experiment (a pilot study) is often used to gain information about feasibility of the experiment or to provide preliminary data for grant applications. We study the theoretical statistical implications of using a small sample of data (1) to design the second stage experiment and (2) in combination with the secondstage data for data analysis. To illuminate the issues, we consider an experiment under a nonlinear regression model with normal errors. We show how the dependency between data in the different stages affects the distribution of parameter estimates when the firststage sample size is fixed and finite; letting the second stage sample size go to infinity, maximum likelihood estimates are found to have a mixed normal distribution.
20150708T15:10:41+01:00
2646
2024217
true
16x9
false
no

An approach to the selection of multistratum fractional factorial designs
ucs_sms_125_1429
http://sms.cam.ac.uk/media/1429
An approach to the selection of multistratum fractional factorial designs
Cheng, CS, Tsai, PW (UC Berkeley/National Taiwan Normal Univ)
Monday 11 August 2008, 14:3015:00
Tue, 09 Sep 2008 07:37:59 +0100
Cheng, CS
Tsai, PW
Isaac Newton Institute
Cheng, CS, Tsai, PW
8aa83feefee2a576d7a6dcab617007cd
0dd236c556fb0e39f926035458c23256
6d0b044fc2f1b290591ffd9b74f087ef
74af31862b012e9ff0bfbf84aa2382e9
af65c7ee4930e543bcb04352c76abd82
c9efcd9636a49f54d049439b3e77807a
9eeaa2f97850904deec0779340de76a1
Cheng, CS, Tsai, PW (UC Berkeley/National Taiwan Normal Univ)
Monday 11...
Cheng, CS, Tsai, PW (UC Berkeley/National Taiwan Normal Univ)
Monday 11 August 2008, 14:3015:00
Cambridge University
1775
http://sms.cam.ac.uk/media/1429
An approach to the selection of multistratum fractional factorial designs
Cheng, CS, Tsai, PW (UC Berkeley/National Taiwan Normal Univ)
Monday 11 August 2008, 14:3015:00
We propose an approach to the selection of multistratum fractional factorial designs. Our criterion, derived as a good surrogate for the modelrobustness criterion of information capacity, takes the stratum variances into account. Comparisons with minimumaberration type criteria proposed in some recent works will be presented.
http://www.newton.ac.uk/programmes/DOE/seminars/index.html
20130301T11:15:14+00:00
1775
1429
true
4x3
false
no

An efficient alternative to the complete matchedpairs design for assessing noninferiority of a new diagnostic test
ucs_sms_125_1166495
http://sms.cam.ac.uk/media/1166495
An efficient alternative to the complete matchedpairs design for assessing noninferiority of a new diagnostic test
van de Ven, P (Vrije U, Amsterdam)
Thursday 18 August 2011, 11:4512:30
Tue, 23 Aug 2011 13:37:12 +0100
Isaac Newton Institute
van de Ven, P
ad881a36354d7e5186713956549187e4
491bef7847abacc5aab991809bba2f43
3585201f8f6efe31938ce5d816ce4f98
6ff56f7a008849691fed3b82db719114
1e109c288b7e03be0c5bc6d5c626c8f4
van de Ven, P (Vrije U, Amsterdam)
Thursday 18 August 2011, 11:4512:30
van de Ven, P (Vrije U, Amsterdam)
Thursday 18 August 2011, 11:4512:30
Cambridge University
2365
http://sms.cam.ac.uk/media/1166495
An efficient alternative to the complete matchedpairs design for assessing noninferiority of a new diagnostic test
van de Ven, P (Vrije U, Amsterdam)
Thursday 18 August 2011, 11:4512:30
Studies for assessing noninferiority of a new diagnostic test relative to a standard test typically use a complete matchedpairs design in which results for both tests are obtained for all subjects. We present alternative noninferiority tests for the situation where results for the standard test are obtained for all subjects but results for the new test are obtained for a subset of those subjects only. This situation is common when results for the standard test are available from a monitoring or screening programme or from a large biobank. A stratified sampling procedure is presented for drawing the subsample of subjects that receive the new diagnostic test with strata defined by the two outcome categories of the standard test. Appropriate statistical tests for noninferiority of the new diagnostic test are derived. We show that if diagnostic test positivity is low, the number of subjects to be tested with the new test is minimized when stratification is nonproportional.
20110823T13:37:20+01:00
2365
1166495
true
16x9
false
no

An Evolutionary Approach to Experimental Design for Combinatorial Optimization
ucs_sms_125_1169837
http://sms.cam.ac.uk/media/1169837
An Evolutionary Approach to Experimental Design for Combinatorial Optimization
Borrotti, M (Bologna)
Friday 02 September 2011, 11:3012:00
Mon, 05 Sep 2011 12:21:21 +0100
Borrotti, M
Steve Greenham
Isaac Newton Institute
Borrotti, M
652cd67952d5c9a42708cd21477eac05
44a0ea24c253fb0ff1601e98147b85b9
8ea7c2074325cc4bb0f433e37a696d75
f6b0b797d399af27e7996330849edfdf
82264682fbc52c3786dc5bcfa616f400
Borrotti, M (Bologna)
Friday 02 September 2011, 11:3012:00
Borrotti, M (Bologna)
Friday 02 September 2011, 11:3012:00
Cambridge University
1732
http://sms.cam.ac.uk/media/1169837
An Evolutionary Approach to Experimental Design for Combinatorial Optimization
Borrotti, M (Bologna)
Friday 02 September 2011, 11:3012:00
In this presentation we investigate an approach which combines statistical methods and optimization algorithms in order to explore a large search space when the great number of variables and the economical constraints limit the ability of classical techniques to reach the optimum of a function. The method we propose  the Model Based Ant Colony Design (MACD)  couples real experimentation with simulated experiments and boosts an “Ant Colony” algorithm (Dorigo et al., 2004) by means of a simulator (strictly speaking an emulator), i.e. a predictive statistical model. Candidate solutions are generated by computer simulation using Ant Colony Optimization, a probabilistic technique for solving computational problem which consists in finding good paths through graphs and is based on the foraging behaviour of real ants. The evaluation of the candidate solutions is achieved by physical experiments and is fed back into the simulative phase in a recursive way.
The properties of the proposed approach are studied by means of numerical simulations, testing the algorithm on some mathematical benchmark functions. Generation after generation, the evolving design requires a small number of experimental points to test, and consequently a small investment in terms of resources. Furthermore, since the research was inspired by a real problem in Enzyme Engineering and Design, namely finding a new enzyme with a specific biological function, we have tested MACD on the real application. The results shows that the algorithm has explored a region of the sequence space not sampled by natural evolution, identifying artificial sequences that fold into a tertiary structure closely related to the target one.
20110905T12:21:30+01:00
1732
1169837
true
16x9
false
no

An introduction to polynomial chaos and its applications
ucs_sms_125_1199096
http://sms.cam.ac.uk/media/1199096
An introduction to polynomial chaos and its applications
Ko, J
Tuesday 20 December 2011, 10:0011:00
Wed, 21 Dec 2011 08:54:10 +0000
Isaac Newton Institute
Ko, J
b86cf7d3895e02dfda40ebfe04b6ff44
ddb121b26b81d323e02be1bc49b0c21d
4c64a254a6b25bf08bab9673ab9d0f8e
25af1ac690ff68eba51c2dd86074ba49
b5bb3707ce123cd85267ccc12cfad75f
Ko, J
Tuesday 20 December 2011, 10:0011:00
Ko, J
Tuesday 20 December 2011, 10:0011:00
Cambridge University
4557
http://sms.cam.ac.uk/media/1199096
An introduction to polynomial chaos and its applications
Ko, J
Tuesday 20 December 2011, 10:0011:00
20111221T08:54:22+00:00
4557
1199096
true
16x9
false
no

An overview of functional data analysis with an application facial motion modelling
ucs_sms_125_1168595
http://sms.cam.ac.uk/media/1168595
An overview of functional data analysis with an application facial motion modelling
Faraway, J (Bath)
Tuesday 30 August 2011, 16:0016:30
Thu, 01 Sep 2011 14:03:29 +0100
Faraway, J
Steve Greenham
Isaac Newton Institute
Faraway, J
53c2c2d30a879cf622c0fc08bc8ffefa
a2e1983cbbba5bc9104c57366031a2fd
7eab6d16f8aad4c91340bd9714d28932
71d56500c769256bddce4fe3d47fed83
a624ac2dc4a5f54b15bee9ca9a120f06
Faraway, J (Bath)
Tuesday 30 August 2011, 16:0016:30
Faraway, J (Bath)
Tuesday 30 August 2011, 16:0016:30
Cambridge University
1733
http://sms.cam.ac.uk/media/1168595
An overview of functional data analysis with an application facial motion modelling
Faraway, J (Bath)
Tuesday 30 August 2011, 16:0016:30
Data in the form of curves, trajectories and shape changes present unique challenges. We present an overview of functional data analysis. We show how these methods can be used to model facial motion with application to cleft lip surgery.
20110901T14:03:39+01:00
1733
1168595
true
16x9
false
no

Application of modelbased designs in drug development
ucs_sms_125_1166255
http://sms.cam.ac.uk/media/1166255
Application of modelbased designs in drug development
Leonov, S (GlaxoSmithKline)
Tuesday 16 August 2011, 15:3016:00
Mon, 22 Aug 2011 15:58:33 +0100
Leonov, S
Steve Greenham
Isaac Newton Institute
Leonov, S
1c81cec305195c7ffc073914d0b90e20
60916b53165750da8e5426a84b0d06e7
0c3ee5f341f1bc2ecc5049b0f7c437d2
9ac38d19e653258c9a5a47b46db9b8d0
0f67d9a39ee32880d69465ea6cd2abed
Leonov, S (GlaxoSmithKline)
Tuesday 16 August 2011, 15:3016:00
Leonov, S (GlaxoSmithKline)
Tuesday 16 August 2011, 15:3016:00
Cambridge University
1826
http://sms.cam.ac.uk/media/1166255
Application of modelbased designs in drug development
Leonov, S (GlaxoSmithKline)
Tuesday 16 August 2011, 15:3016:00
We discuss the use of optimal modelbased designs at different stages of drug development. Special attention is given to adaptive modelbased designs in dose finding studies and designs for nonlinear mixed models which arise in population pharmacokinetic/pharmacodynamic studies. Examples of software tools and their application are provided.
20110831T13:40:55+01:00
1826
1166255
true
16x9
false
no

Applicationsoriented experiment design for dynamical systems
ucs_sms_125_1158258
http://sms.cam.ac.uk/media/1158258
Applicationsoriented experiment design for dynamical systems
Hjalmarsson, H (KTH  Royal Institute of Technology)
Wednesday 20 July 2011, 11:3012:30
Fri, 22 Jul 2011 14:55:04 +0100
Hjalmarsson, H
Steve Greenham
Isaac Newton Institute
Hjalmarsson, H
07c9cd2bb0b9e750101c12c30e9f8643
6deb90c8314db91ecb53587fc5bd2b00
1bd3881a13728d8c5d48be6da7513be6
40adef9d961a83a37a4934da3a62918e
f8b7f7b5b812fa5c69796b497da32f71
Hjalmarsson, H (KTH  Royal Institute of Technology)
Wednesday 20 July 2011,...
Hjalmarsson, H (KTH  Royal Institute of Technology)
Wednesday 20 July 2011, 11:3012:30
Cambridge University
3546
http://sms.cam.ac.uk/media/1158258
Applicationsoriented experiment design for dynamical systems
Hjalmarsson, H (KTH  Royal Institute of Technology)
Wednesday 20 July 2011, 11:3012:30
In this talk we present a framework for applicationsoriented experiment design for dynamic systems. The idea is to generate a design such that certain performance criteria of the application are satisfied with high probability. We discuss how to approximate this problem by a convex optimization problem and how to address Achilles' heel of optimal experiment design, i.e., that the optimal design depends on the true system. We also elaborate on how the cost of an identification experiment is related to the performance requirements of the application and the importance of experiment design in reduced order modeling. We illustrate the methods on some problems from control and systems theories.
20110722T14:55:12+01:00
3546
1158258
true
16x9
false
no

Approximation of the Fisher information and design in nonlinear mixed effects models
ucs_sms_125_1164653
http://sms.cam.ac.uk/media/1164653
Approximation of the Fisher information and design in nonlinear mixed effects models
Mielke, T (OttovonGuericke)
Friday 12 August 2011, 11:0011:45
Tue, 16 Aug 2011 18:28:30 +0100
Mielke, T
Steve Greenham
Isaac Newton Institute
Mielke, T
e37dd9023f94082a4cdc49394bc6fe8e
590ba43b4a27fe810d090588bcf7f761
8644e6e19d4facb6acb570d37c743b45
6e57336c179dd197a5b399404c434483
48ce3aa3f2b29bb8d090ec43674e325a
Mielke, T (OttovonGuericke)
Friday 12 August 2011, 11:0011:45
Mielke, T (OttovonGuericke)
Friday 12 August 2011, 11:0011:45
Cambridge University
2972
http://sms.cam.ac.uk/media/1164653
Approximation of the Fisher information and design in nonlinear mixed effects models
Mielke, T (OttovonGuericke)
Friday 12 August 2011, 11:0011:45
The missing closed form representation of the probability density of the observations is one main problem in the analysis of Nonlinear Mixed Effects Models. Often local approximations based on linearizations of the model are used to approximately describe the properties of estimators. The Fisher Information is of special interest for designing experiments, as its inverse yields a lower bound of the variance of any unbiased estimator. Different linearization approaches for the model yield different approximations of the true underlying stochastical model and the Fisher Information (Mielke and Schwabe (2010)). Target of the presentation are alternative motivations of FisherInformation approximations, based on conditional moments. For an individual design, known interindividual variance and intraindividual variance, the Fisher Information for estimating the population location parameter vector results in an expression depending on conditional moments, such that approximations of the expectation of the conditional variance and the variance of the conditional expectation yield approximations of the Fisher Information, which are less based on distribution assumptions. Tierney et. al. (1986) described fully exponential Laplace approximations as an accurate method for approximating posterior moments and densities in Bayesian models. We present approximations of the Fisher Information, obtained by approximations of conditional moments with a similar heuristic and compare the impact of different Fisher Information approximations on the optimal design for estimating the population location parameters in pharmacokinetic studies.
20110816T18:28:40+01:00
2972
1164653
true
16x9
false
no

Approximation of the individual Fisher information matrix and its use in design of population PK/PD studies
ucs_sms_125_1164607
http://sms.cam.ac.uk/media/1164607
Approximation of the individual Fisher information matrix and its use in design of population PK/PD studies
Leonov, S (GlaxoSmithKline)
Friday 12 August 2011, 09:4510:30
Tue, 16 Aug 2011 16:32:22 +0100
Leonov, S
Steve Greenham
Isaac Newton Institute
Leonov, S
b7f91ee5039df393e553fe9d48b89732
51706507c770044b9a1f6989061a8cc6
bed0b13bd328c601b1ca7e0f636a946f
17daf3b4bb4e234404c330f1f37189a0
3ae2d1f00b7567d9d25b52e6732e0e54
Leonov, S (GlaxoSmithKline)
Friday 12 August 2011, 09:4510:30
Leonov, S (GlaxoSmithKline)
Friday 12 August 2011, 09:4510:30
Cambridge University
3306
http://sms.cam.ac.uk/media/1164607
Approximation of the individual Fisher information matrix and its use in design of population PK/PD studies
Leonov, S (GlaxoSmithKline)
Friday 12 August 2011, 09:4510:30
We continue a discussion started at PODE 2010 meeting about different types of approximation of the individual Fisher information matrix and their use in design of population PK/PD studies which are described by nonlinear mixed effects models. We focus on several Monte Carlobased options and provide examples of their performance.
20110816T16:32:31+01:00
3306
1164607
true
16x9
false
no

Assessing simulator uncertainty using evaluations from several different simulators
ucs_sms_125_1172175
http://sms.cam.ac.uk/media/1172175
Assessing simulator uncertainty using evaluations from several different simulators
House, L (Virginia Tech)
Thursday 08 September 2011, 14:0014:30
Wed, 14 Sep 2011 18:26:06 +0100
House, L
Steve Greenham
Isaac Newton Institute
House, L
8c7116b792705a13c0beb66acc654bc7
a7408d2ef435c8c47b338f197e0422a3
d0339b5e6cece8e17286f3ada9a1a123
6fac05aad627e03800ce5ca0d068fd9a
00aa4ec5dac0dd5212a67113e8b0ba65
House, L (Virginia Tech)
Thursday 08 September 2011, 14:0014:30
House, L (Virginia Tech)
Thursday 08 September 2011, 14:0014:30
Cambridge University
2031
http://sms.cam.ac.uk/media/1172175
Assessing simulator uncertainty using evaluations from several different simulators
House, L (Virginia Tech)
Thursday 08 September 2011, 14:0014:30
Any simulatorbased prediction must take account of the discrepancy between the simulator and the underlying system. In physical systems, such as climate, this discrepancy has a complex, unknown structure that makes direct elicitation very demanding. Here, we propose a fundamentally different framework to that currently in use and consider information in a collection of simulatorevaluations, known as a MultiModel Ensemble (MME). We justify our approach both in terms of its transparency, tractability, and consistency with standard practice in, say, Climate Science. The statistical modelling framework is that of secondorder exchangeability, within a Bayes linear treatment. We apply our methods based on a reconstruction of boreal winter surface temperature.
20110914T18:26:16+01:00
2031
1172175
true
16x9
false
no

Assessing the Efficiencies of Optimal Discrete Choice Experiments in the Presence of Respondent Fatigue
ucs_sms_125_1168750
http://sms.cam.ac.uk/media/1168750
Assessing the Efficiencies of Optimal Discrete Choice Experiments in the Presence of Respondent Fatigue
Li, W (Minnesota)
Wednesday 31 August 2011, 16:3017:00
Thu, 01 Sep 2011 15:18:30 +0100
Li, W
Steve Greenham
Isaac Newton Institute
Li, W
a5d8c4bcf838319dbc06c566889463dc
76f09b2871a63a86fb88009e5e5bd477
1cebb79febe37f2f22509b2f6baac7ec
64d2d73f1ce7895701eb9f597190ece0
74ab64250f546baa1a7846dc8455494a
Li, W (Minnesota)
Wednesday 31 August 2011, 16:3017:00
Li, W (Minnesota)
Wednesday 31 August 2011, 16:3017:00
Cambridge University
1645
http://sms.cam.ac.uk/media/1168750
Assessing the Efficiencies of Optimal Discrete Choice Experiments in the Presence of Respondent Fatigue
Li, W (Minnesota)
Wednesday 31 August 2011, 16:3017:00
Discrete choice experiments are an increasingly popular form of marketing research due to the accessibility of online respondents. While statistically optimal experimental designs have been developed for use in discrete choice experiments, recent research has suggested that efficient designs often fatigue or burden the respondent to the point that decreased response rates and/or decreased response precision are observed. Our study was motivated by high earlytermination rates for one such optimallydesigned study.
In this talk, we examine the design of discrete choice experiments in the presence of respondent fatigue and/or burden. To do so, we propose a model that links the respondent's utility error variance to a function that accommodates respondent fatigue and burden. Based on estimates of fatigue and burden effects from our own work and published studies, we study the impact of these factors on the realized efficiencies of commonlyused Doptimal choice designs. The tradeoffs between the number of surveys, the number of choice sets per survey, and the number of profiles per choice set are delineated.
20110901T15:18:39+01:00
1645
1168750
true
16x9
false
no

Assessment of randomization procedures based on single sequences under selection bias
ucs_sms_125_2028360
http://sms.cam.ac.uk/media/2028360
Assessment of randomization procedures based on single sequences under selection bias
Hilgers, RD (RWTH Aachen University)
Friday 10th July 2015, 11:45  12:30
Tue, 14 Jul 2015 17:18:13 +0100
Isaac Newton Institute
Hilgers, RD
2762a8b42f7b11330ffeb91456eec27b
3a9bd35707d6062bea0e1f1570f6f28d
50c6abe07ba150ad9aebfa924fd0cba7
b3cd4e319bba8cae8366d21f7363e7fc
Hilgers, RD (RWTH Aachen University)
Friday 10th July 2015, 11:45  12:30
Hilgers, RD (RWTH Aachen University)
Friday 10th July 2015, 11:45  12:30
Cambridge University
2334
http://sms.cam.ac.uk/media/2028360
Assessment of randomization procedures based on single sequences under selection bias
Hilgers, RD (RWTH Aachen University)
Friday 10th July 2015, 11:45  12:30
Randomization is a key feature of randomized clinical trials aiming to protect against various types of bias. Different randomization procedures were introduced in the past decades and their analytical properties have been studied by various authors. Among others, balancing behaviour, protection against selection and chronological bias etc have been investigated. However, in summary no procedure performs best on all criteria. On the other hand, in the design phase of a clinical trial the scientist has to select a particular randomization procedure to be used in the allocation process which takes into account the research conditions of the trial. Up to now, less support is available to guide the scientist hereby, e.g. to weigh up the properties with respect to practical needs of the research question to be answered by the clinical trial. We propose a method to assess the impact of chronological and selection bias in a parallel group randomized clinical trial with continuous normal outcome on the probability of type one error to derive scientific arguments for selection of an appropriate randomization procedure.
This is joint work with Simon Langer.
20150714T17:18:13+01:00
2334
2028360
true
16x9
false
no

Automatic analysis of variance for orthogonal plot structures
ucs_sms_125_1181614
http://sms.cam.ac.uk/media/1181614
Automatic analysis of variance for orthogonal plot structures
Grossmann, H (Queen Mary, University of London)
Thursday 13 October 2011, 14:0015:00
Fri, 14 Oct 2011 14:53:56 +0100
Grossmann, H
Steve Greenham
Isaac Newton Institute
Grossmann, H
5a834414bdd2e699a88562b4d61b2b05
71705cb49d5253d316810031be5419a4
0ea257e49a4bba43f2571a555e1dd164
4ab93a8185d4f10c2ac41288e5e9259c
30f62944a5d1aa621cdd4d6196c9cd50
Grossmann, H (Queen Mary, University of London)
Thursday 13 October 2011,...
Grossmann, H (Queen Mary, University of London)
Thursday 13 October 2011, 14:0015:00
Cambridge University
4943
http://sms.cam.ac.uk/media/1181614
Automatic analysis of variance for orthogonal plot structures
Grossmann, H (Queen Mary, University of London)
Thursday 13 October 2011, 14:0015:00
20111014T14:54:07+01:00
4943
1181614
true
16x9
false
no

Batch sequential experimental designs for computer experiments
ucs_sms_125_1171908
http://sms.cam.ac.uk/media/1171908
Batch sequential experimental designs for computer experiments
Notz, W (Ohio State University)
Tuesday 06 September 2011, 15:3016:00
Wed, 14 Sep 2011 10:51:58 +0100
Notz, W
Steve Greenham
Isaac Newton Institute
Notz, W
17d2fcc624bf81d8a99879e30fd7c1a8
994bf5ce2ec82519eb35fc7b9de00415
b218dafa677b5d9e4ca9a27ea0dbebb1
e2353a69154d3bffa99739af0c4854c5
73b7a8bed81af2b6c2b694f2ff4ad3a1
Notz, W (Ohio State University)
Tuesday 06 September 2011, 15:3016:00
Notz, W (Ohio State University)
Tuesday 06 September 2011, 15:3016:00
Cambridge University
1727
http://sms.cam.ac.uk/media/1171908
Batch sequential experimental designs for computer experiments
Notz, W (Ohio State University)
Tuesday 06 September 2011, 15:3016:00
Finding optimal designs for computer experiments that are modeled using a stationary Gaussian Stochastic Process (GaSP) model is challenging because optimality criteria are usually functions of the unknown model parameters. One popular approach is to adopt sequential strategies. These have been shown to be very effective when the optimality criterion is formulated as an expected improvement function. Most of these sequential strategies assume observations are taken sequentially one at a time. However, when observations can be taken k at a time, it is not obvious how to implement sequential designs. We discuss the problems that can arise when implementing batch sequential designs and present several strategies for sequential designs taking observations in katatime batches. We illustrate these strategies with examples.
20110914T10:52:08+01:00
1727
1171908
true
16x9
false
no

Bayesian Adaptive Design for Statespace Models with Covariates
ucs_sms_125_1169601
http://sms.cam.ac.uk/media/1169601
Bayesian Adaptive Design for Statespace Models with Covariates
Sahu, S (Southampton)
Thursday 01 September 2011, 11:0011:30
Mon, 05 Sep 2011 10:49:15 +0100
Sahu, S
Steve Greenham
Isaac Newton Institute
Sahu, S
6185ce9b231ff333be64b5b0deda61e1
f71e91cd960470b39a33bf689da7cf30
5d3a8ef4dd6e09d49165e1e506eeaddf
594ebb51d7ebfcc68a6bd7d72a2f3340
38508d5a53bb7ba76457abc6009ecdff
Sahu, S (Southampton)
Thursday 01 September 2011, 11:0011:30
Sahu, S (Southampton)
Thursday 01 September 2011, 11:0011:30
Cambridge University
1851
http://sms.cam.ac.uk/media/1169601
Bayesian Adaptive Design for Statespace Models with Covariates
Sahu, S (Southampton)
Thursday 01 September 2011, 11:0011:30
Modelling data that change over space and time is important in many areas, such as environmental monitoring of air and noise pollution using a sensor network over a long period of time. Often such data are collected dynamically together with the values of a variety of related variables. Due to resource limitations, an optimal choice (or design) for the locations of the sensors is important for achieving accurate predictions. This choice depends on the adopted model, that is, the spatial and temporal processes, and the dependence of the responses on relevant covariates. We investigate adaptive designs for statespace models where the selection of locations at time point tn+1 draws on information gained from observations made at the locations sampled at preceding time points t1,…,tn. A Bayesian design selection criterion is developed and its performance is evaluated using several examples.
20110905T10:49:24+01:00
1851
1169601
true
16x9
false
no

Bayesian Adaptive Designs for Identifying Maximum Tolerated Combinations of Two Agents
ucs_sms_125_1165739
http://sms.cam.ac.uk/media/1165739
Bayesian Adaptive Designs for Identifying Maximum Tolerated Combinations of Two Agents
Braun, T (Michigan)
Monday 15 August 2011, 15:2516:05
Thu, 18 Aug 2011 08:34:57 +0100
Braun, T
Steve Greenham
Isaac Newton Institute
Braun, T
719b9fe1d4a74fd358c2ae20b98fad55
f6f88352b3eb3856b4f3343850031750
3e5a09a372cb2c84f0bd192e493aac30
43def9d7f945215131ba4294e9ec3b94
d569e6d2eb31ddba3cf014a1e35e53ef
18e32988798b6f26ebc65982f5876482
d758d5f31ab5df854b5cede1bca399c1
Braun, T (Michigan)
Monday 15 August 2011, 15:2516:05
Braun, T (Michigan)
Monday 15 August 2011, 15:2516:05
Cambridge University
2087
http://sms.cam.ac.uk/media/1165739
Bayesian Adaptive Designs for Identifying Maximum Tolerated Combinations of Two Agents
Braun, T (Michigan)
Monday 15 August 2011, 15:2516:05
Phase I trials of combination cancer therapies have been published for a variety of cancer types. Unfortunately, a majority of these trials suffer from poor study designs that either escalate doses of only one of the agents and/or use an algorithmic approach to determine which combinations of the two agents maintain a desired rate of doselimiting toxicities (DLTs), which we refer to as maximum tolerated combinations (MTCs). We present a survey of recent approaches we have developed for the design of Phase I trials seeking to determine the MTC. For each approach, we present a model for the probability of DLT as a function of the doses of both agents. We use Bayesian methods to adaptively estimate the parameters of the model as each patient completes their followup in the trial, from which we determine the doses to assign to the next patient enrolled in the trial. We describe methods for generating prior distributions for the parameters in our model from a basic set of i nformation elicited from clinical investigators. We compare and contrast the performance of each approach in a series of simulations of a hypothetical trial that examines combinations of four doses of two agents and compare the results to those of an algorithmic design known as an A+B+C design.
20110818T08:35:07+01:00
2087
1165739
true
4x3
false
no

Bayesian approaches to Phase I clinical trials: methodological and practical aspects
ucs_sms_125_1166237
http://sms.cam.ac.uk/media/1166237
Bayesian approaches to Phase I clinical trials: methodological and practical aspects
Neuenschwander, B (Novartis Pharma AG)
Tuesday 16 August 2011, 15:0015:30
Mon, 22 Aug 2011 15:53:35 +0100
Neuenschwander, B
Steve Greenham
Isaac Newton Institute
Neuenschwander, B
a396ff6cf738bffcb1a919e9104978e2
33b6aa188bd60006474e3fafecf349f3
1574adc44b1f23702f98784e906e9ab9
952641861d9197f44b84be483d2033d3
0dd91b4ee160c4abcdf045fd86b7a29d
Neuenschwander, B (Novartis Pharma AG)
Tuesday 16 August 2011, 15:0015:30
Neuenschwander, B (Novartis Pharma AG)
Tuesday 16 August 2011, 15:0015:30
Cambridge University
2239
http://sms.cam.ac.uk/media/1166237
Bayesian approaches to Phase I clinical trials: methodological and practical aspects
Neuenschwander, B (Novartis Pharma AG)
Tuesday 16 August 2011, 15:0015:30
Statistics plays an important role in drug development, in particular in confirmatory (phase III) clinical trials, where statistically convincing evidence is a requirement for the registration of a drug. However, statistical contributions to phase I clinical trials are typically sparse. A notable exception is oncology, where statistical methods abound. After a short review of the main approaches to phase I cancer trials, we discuss a fully adaptive modelbased Bayesian approach which strikes a reasonable balance with regard to various objectives. First, proper quantification of the risk of doselimiting toxicities (DLT) is the key to acceptable dosing recommendations during the trial, and the declaration of the maximum tolerable dose (MTD), a dose with an acceptable risk of DLT, at the end of the trial. In other words, statistically driven dosingrecommendations should be clinically meaningful. Second, the operating characteristics of the design should be acceptable. That is, the probability to find the correct MTD should be reasonably high. Third, not too many patients should be exposed to overly toxic doses. And fourth, the approach should allow for the inclusion of relevant studyexternal information, such as preclinical data or data from other human studies. The methodological and practical aspects of the Bayesian approach to dose finding trials in Oncology phase I will be discussed, and examples from actual trials will be used to illustrate and highlight important issues. The presentation concludes with a discussion of the main challenges for a largescale implementation of innovative clinical trial designs in the pharmaceutical industry.
20110831T13:41:25+01:00
2239
1166237
true
16x9
false
no

Bayesian Calibration of Computer Model Ensembles
ucs_sms_125_1172513
http://sms.cam.ac.uk/media/1172513
Bayesian Calibration of Computer Model Ensembles
Pratola, M (Los Alamos National Laboratory)
Friday 09 September 2011, 11:3012:00
Thu, 15 Sep 2011 13:21:38 +0100
Pratola, M
Steve Greenham
Isaac Newton Institute
Pratola, M
762536e8709c415a4293ca80571512ad
b9da3980c0f4dbbf072148d9e5eb2730
c9da042e88eb725e0b5ea12188649901
37ce555d288de8daa6ff7b7ceb2ad37b
61f81b2859722e468055fdfcb1d7b70a
Pratola, M (Los Alamos National Laboratory)
Friday 09 September 2011,...
Pratola, M (Los Alamos National Laboratory)
Friday 09 September 2011, 11:3012:00
Cambridge University
1588
http://sms.cam.ac.uk/media/1172513
Bayesian Calibration of Computer Model Ensembles
Pratola, M (Los Alamos National Laboratory)
Friday 09 September 2011, 11:3012:00
Using field observations to calibrate complex mathematical models of a physical process allows one to obtain statistical estimates of model parameters and construct predictions of the observed process that ideally incorporate all sources of uncertainty. Many of the methods in the literature use response surface approaches, and have demonstrated success in many applications. However there are notable limitations, such as when one has a small ensemble of model runs where the model outputs are high dimensional. In such instances, arriving at a response surface model that reasonably describes the process can be dicult, and computational issues may also render the approach impractical.
In this talk we present an approach that has numerous beneifts compared to some popular methods. First, we avoid the problems associated with defining a particular regression basis or covariance model by making a Gaussian assumption on the ensemble. By applying Bayes theorem, the posterior distribution of unknown calibration parameters and predictions of the field process can be constructed. Second, as the approach relies on the empirical moments of the distribution, computational and stationarity issues are much reduced compared to some popular alternatives. Finally, in the situation that additional observations are arriving over time, our method can be seen as a fully Bayesian generalization of the popular Ensemble Kalman Filter.
20110915T13:21:47+01:00
1588
1172513
true
16x9
false
no

Bayesian enrichment strategies for randomized discontinuation trials
ucs_sms_125_1162745
http://sms.cam.ac.uk/media/1162745
Bayesian enrichment strategies for randomized discontinuation trials
Rosner, G (Johns Hopkins)
Wednesday 10 August 2011, 14:0014:45
Thu, 11 Aug 2011 15:34:34 +0100
Rosner, G
Steve Greenham
Isaac Newton Institute
Rosner, G
f89b880e816722918fb6765cfe0c4222
2aeea544651895e7a397dd69f95463c3
51cb13173d48a94011a268642e8c7d25
5b18d7a80f1525d86eedd0bb490a9156
357b613b05a0b579fc65a38ca9856cb2
Rosner, G (Johns Hopkins)
Wednesday 10 August 2011, 14:0014:45
Rosner, G (Johns Hopkins)
Wednesday 10 August 2011, 14:0014:45
Cambridge University
2915
http://sms.cam.ac.uk/media/1162745
Bayesian enrichment strategies for randomized discontinuation trials
Rosner, G (Johns Hopkins)
Wednesday 10 August 2011, 14:0014:45
We propose optimal choice of the design parameters for random discontinuation designs (RDD) using a Bayesian decisiontheoretic approach. We consider applications of RDDs to oncology phase II studies evaluating activity of cytostatic agents. The design consists of two stages. The preliminary openlabel stage treats all patients with the new agent and identi?es a possibly sensitive subpopulation. The subsequent second stage randomizes, treats, follows, and compares outcomes among patients in the identi?ed subgroup, with randomization to either the new or a control treatment. Several tuning parameters characterize the design: the number of patients in the trial, the duration of the preliminary stage, and the duration of followup after randomization. We de?ne a probability model for tumor growth, specify a suitable utility function, and develop a computational procedure for selecting the optimal tuning parameters.
20110811T15:34:43+01:00
2915
1162745
true
16x9
false
no

Bayesian evidence synthesis to estimate progression of human papillomavirus
ucs_sms_125_1177572
http://sms.cam.ac.uk/media/1177572
Bayesian evidence synthesis to estimate progression of human papillomavirus
Jackson, C (MRC Biostats)
Monday 26 September 2011, 10:4010:50
Mon, 03 Oct 2011 15:32:45 +0100
Isaac Newton Institute
Jackson, C
855c18c342cdd7553e0a01cb3975b354
66dc2fc1b34f4631dc6b643a6e4fbea3
bcaaab6685bc6a7aa03672b19245d84d
a7aa89fc8d321afacde973da1c36939e
506b67e1bb3244cd2d10bed6badb0a00
Jackson, C (MRC Biostats)
Monday 26 September 2011, 10:4010:50
Jackson, C (MRC Biostats)
Monday 26 September 2011, 10:4010:50
Cambridge University
748
http://sms.cam.ac.uk/media/1177572
Bayesian evidence synthesis to estimate progression of human papillomavirus
Jackson, C (MRC Biostats)
Monday 26 September 2011, 10:4010:50
20111003T15:32:54+01:00
748
1177572
true
16x9
false
no

Bayesian experimental design for percolation and other random graph models
ucs_sms_125_1158348
http://sms.cam.ac.uk/media/1158348
Bayesian experimental design for percolation and other random graph models
Bejan, A (University of Cambridge)
Wednesday 20 July 2011, 17:0017:30
Fri, 22 Jul 2011 15:28:00 +0100
Bejan, A
Steve Greenham
Isaac Newton Institute
Bejan, A
24c1d9e242a62a482cdf890f6a5b762a
1d3ec7eb737d6a69976180cf2fd04296
cc53174dca22fc1f081c44124ba459a9
8099d69e3655496c672069de4630764c
d2691230f6bc13a7a2df448043a8fa76
Bejan, A (University of Cambridge)
Wednesday 20 July 2011, 17:0017:30
Bejan, A (University of Cambridge)
Wednesday 20 July 2011, 17:0017:30
Cambridge University
2199
http://sms.cam.ac.uk/media/1158348
Bayesian experimental design for percolation and other random graph models
Bejan, A (University of Cambridge)
Wednesday 20 July 2011, 17:0017:30
The problem of optimal arrangement of nodes of a random graph will be discussed in this workshop. The nodes of graphs under study are fixed, but their edges are random and established according to the so called edgeprobability function. This function may depend on the weights attributed to the pairs of graph nodes (or distances between them) and a statistical parameter. It is the purpose of experimentation to make inference on the statistical parameter and, thus, to learn about it as much as possible. We also distinguish between two different experimentation scenarios: progressive and instructive designs. We adopt a utilitybased Bayesian framework to tackle this problem. We prove that the infinitely growing or diminishing node configurations asymptotically represent the worst node arrangements. We also obtain the exact solution to the optimal design problem for proximity (geometric) graphs and numerical solution for graphs with threshold edgeprobability functions. We use simulation based optimisation methods, mainly Monte Carlo and Markov Chain Monte Carlo, in order to obtain solution in the general case. We study the optimal design problem for inference based on partial observations of random graphs by employing data augmentation technique. In particular, we consider inference and optimal design problems for finite open clusters from bond percolation on the integer lattices and derive a range of both numerical and analytical results for these graphs. (Our motivation here is that open clusters in bond percolation may be seen as final outbreaks of an SIR epidemic with constant infectious times.) We introduce innerouter design plots by considering a bounded region of the lattice and deleting some of the lattice nodes within this region and show that the 'mostly populated' designs are not necessarily optimal in the case of incomplete observations under both progressive and instructive design scenarios. Some of the obtained results may generalise to other lattices.
20110722T15:28:09+01:00
2199
1158348
true
16x9
false
no

Bayesian experimental design for stochastic dynamical models
ucs_sms_125_1158425
http://sms.cam.ac.uk/media/1158425
Bayesian experimental design for stochastic dynamical models
Gibson, GJ (HeriotWatt University)
Thursday 21 July 2011, 11:3012:30
Fri, 22 Jul 2011 16:05:10 +0100
Gibson, GJ
Steve Greenham
Isaac Newton Institute
Gibson, GJ
8c32ffc47a02e466e39fe21f4e9beeda
ce610ce6ac8df1ffab0aacc07fa1efe2
c2e3fd0c1dc5210315749de2ffb8e073
e4405acdae6013d7db56c1d1fce6f707
405986c9cb16a55fa68b93af09bb277d
Gibson, GJ (HeriotWatt University)
Thursday 21 July 2011, 11:3012:30
Gibson, GJ (HeriotWatt University)
Thursday 21 July 2011, 11:3012:30
Cambridge University
3483
http://sms.cam.ac.uk/media/1158425
Bayesian experimental design for stochastic dynamical models
Gibson, GJ (HeriotWatt University)
Thursday 21 July 2011, 11:3012:30
Advances in Bayesian computational methods have meant that it is now possible to fit a broad range of stochastic, nonlinear dynamical models (including spatiotemporal formulations) within a rigorous statistical framework. In epidemiology these methods have proved particularly valuable for producing insights into transmission dynamics on historical epidemics and for assessing potential control strategies. On the other hand, there has been less attention paid to the question how future data should be collected most efficiently for the purpose of analysis with these models. This talk will describe how the Bayesian approach to experimental design can be applied with standard epidemic models in order to identify the most efficient manner for collecting data to provide information on key rate parameters. Central to the approach is the representation of the design as a 'parameter' in an extended parameter space with the optimal design appearing as the marginal mode for an appropriately specified joint distribution. We will also describe how approximations, derived using momentclosure techniques, can be applied in order to make tractable the computational of likelihood functions which, given the partial nature of the data, would be prohibitively complex using methods such as data augmentation. The talk will illustrate the ideas in the context of designing microcosm experiments to study the spread of fungal pathogens in agricultural crops, where the design problem relates to the particular choice of sampling times used. We will examine the use of utility functions based entirely on information measures that quantify the difference between prior and posterior parameter distributions, and also discuss how economic factors can be incorporated in the construction of utilities for this class of problems. The talk will demonstrate how, if sampling times are appropriately selected, it may be possible to reduce drastically the amount of sampling required in comparison to designs currently used, without compromising the information gained on key parameters. Some challenges and opportunities for future research on design with stochastic epidemic models will also be discussed.
20110722T16:05:20+01:00
3483
1158425
true
16x9
false
no

Bayesian optimization: A framework for optimal computational effort for experimental design
ucs_sms_125_1157769
http://sms.cam.ac.uk/media/1157769
Bayesian optimization: A framework for optimal computational effort for experimental design
Winterfors, E
Tuesday 19 July 2011, 17:0017:30
Thu, 21 Jul 2011 13:29:22 +0100
Winterfors, E
Steve Greenham
Isaac Newton Institute
Winterfors, E
84cce61bc2738277cdf39802a2bd4f00
dc3cbaaabb748d71497300b53a515021
a19e2a394353c5a802dbed07aa09c402
0d1fadae9231892567d5e103a3da5039
2c5f168ea0b1f5c4eecf9a930669d4db
Winterfors, E
Tuesday 19 July 2011, 17:0017:30
Winterfors, E
Tuesday 19 July 2011, 17:0017:30
Cambridge University
1851
http://sms.cam.ac.uk/media/1157769
Bayesian optimization: A framework for optimal computational effort for experimental design
Winterfors, E
Tuesday 19 July 2011, 17:0017:30
DOE on models involving time or space dynamics is often very computationally demanding. Predicting a single experimental outcome may require significant computation, let alone evaluating a design criterion and optimizing it with respect to design parameters. To find the exact optimum of the design criterion would typically take infinite computation, and any finite computation will yield a result possessing some uncertainty (due to approximation of the design criterion as well as stopping the optimization procedure). Ideally, one would like to optimize not only the design criterion, but also the way it is approximated and optimized in order to get the largest likely improvement in the design criterion relative to the computational effort spent. Using a Bayesian method for the optimization of the design criterion (not only for calculating the design criterion) can accomplish such an optimal tradeoff between (computational) resources spent planning the experiment and expected gain from carrying it out. This talk will lay out the concepts and theory necessary to perform a fully Bayesian optimization that maximizes the expected improvement of the design criterion in relation the computational effort spent.
20110721T13:29:30+01:00
1851
1157769
true
16x9
false
no

Bayesian sequential experiment design for quantum tomography
ucs_sms_125_1177953
http://sms.cam.ac.uk/media/1177953
Bayesian sequential experiment design for quantum tomography
Huszar, F; Houlsby, NMT (Engineering)
Monday 26 September 2011, 14:0014:10
Tue, 04 Oct 2011 09:12:06 +0100
Isaac Newton Institute
Huszar, F; Houlsby, NMT
3035ca6036f12ed8881f7db525a968c1
6d7f7bb42e4223b0ddf0805e760fbf29
b5d6047e8d79eef9e1137158366cfe8f
3d754c233e9973cc14c1313d98ac34c0
129e92ba7a546f5f9cabdbf8913e9cc0
Huszar, F; Houlsby, NMT (Engineering)
Monday 26 September 2011, 14:0014:10
Huszar, F; Houlsby, NMT (Engineering)
Monday 26 September 2011, 14:0014:10
Cambridge University
736
http://sms.cam.ac.uk/media/1177953
Bayesian sequential experiment design for quantum tomography
Huszar, F; Houlsby, NMT (Engineering)
Monday 26 September 2011, 14:0014:10
Quantum tomography is a valuable tool in quantum information processing and ex perimental quantum physics, being essential for characterisation of quantum states, processes, and measurement equipment. Quantum state tomography (QST) aims to determine the unobservable quantum state of a system from outcomes of measurements performed on an ensemble of identically prepared systems. Measurements in quantum systems are nondeterministic, hence QST is a classical statistical estimation problem.
Full tomography of quantum states is inherently resourceintensive: even in moder ately sized systems these experiments often take weeks. Sequential optimal experiment design aims at making these experiments shorter by adaptively reconfiguring the mea surement in the light of partial data. In this talk, I am going to introduce the problem of quantum state tomography from a statistical estimation perspective, and describe a sequential Bayesian Experiment Design framework that we developed. I will report simulated experiments in which our framework achieves a tenfold reduction in required experimentation time.
20111004T09:12:16+01:00
736
1177953
true
16x9
false
no

Biomarkerbased Bayesian Adaptive Designs for Targeted Agent Development  Implementation and Lessons Learned from the BATTLE Trial
ucs_sms_125_1165796
http://sms.cam.ac.uk/media/1165796
Biomarkerbased Bayesian Adaptive Designs for Targeted Agent Development  Implementation and Lessons Learned from the BATTLE Trial
Lee, J (MD Anderson Cancer Center)
Monday 15 August 2011, 14:0014:45
Thu, 18 Aug 2011 10:58:50 +0100
Lee, J
Steve Greenham
Isaac Newton Institute
Lee, J
406ee67df4009d6ba2f6f3ea30da62ae
a2f77a3354f1756c00a6c3577eb2a3a7
8f32a67e9c8702ba1746c96f28951d51
52995b2a38cf7b92e700edc342e86e35
6c0b8c38fe244ee88dd9934e677f70fd
5e9dcc0bbfb18c1b4630ecb803d0c2ab
2299cbaa3afece9ca02345691afb3f8f
Lee, J (MD Anderson Cancer Center)
Monday 15 August 2011, 14:0014:45
Lee, J (MD Anderson Cancer Center)
Monday 15 August 2011, 14:0014:45
Cambridge University
2679
http://sms.cam.ac.uk/media/1165796
Biomarkerbased Bayesian Adaptive Designs for Targeted Agent Development  Implementation and Lessons Learned from the BATTLE Trial
Lee, J (MD Anderson Cancer Center)
Monday 15 August 2011, 14:0014:45
Advances in biomedicine have fueled the development of targeted agents in cancer therapy. Targeted therapies have shown to be more efficacious and less toxic than the conventional chemotherapies. Targeted therapies, however, do not work for all patients. One major challenge is to identify markers for predicting treatment efficacy. We have developed biomarkerbased Bayesian adaptive designs to (1) identify prognostic and predictive markers for targeted agents, (2) test treatment efficacy, and (3) provide better treatments for patients enrolled in the trial. In contrast to the frequentist equal randomization designs, Bayesian adaptive randomization designs allow treating more patients with effective treatments, monitoring the trial more frequently to stop ineffective treatments early, and increasing efficiency while controlling type I and type II errors. Bayesian adaptive design can be more efficient, more ethical, and more flexible in the study conduct than standard design s. We have recently completed a biopsyrequired, biomarkerdriven lung cancer trial, BATTLE, for evaluating four targeted treatments. Lessons learned from the design, conduct, and analysis of this Bayesian adaptive design will be given.
20110818T10:58:59+01:00
2679
1165796
true
4x3
false
no

Block designs for nonnormal data via conditional and marginal models
ucs_sms_125_1162659
http://sms.cam.ac.uk/media/1162659
Block designs for nonnormal data via conditional and marginal models
Woods, D (Southampton)
Tuesday 09 August 2011, 14:4515:30
Thu, 11 Aug 2011 14:44:47 +0100
Isaac Newton Institute
Woods, D
0ae1d73ad509ba382ab8b494115e20e9
aa30dd9879973615a580f96a57e61696
66fe0cb853e51702259dc4394832adde
ed4b7ffe38cc9b04bf39a90ead955e07
954176cb0717ae5eac74e910ddd06201
Woods, D (Southampton)
Tuesday 09 August 2011, 14:4515:30
Woods, D (Southampton)
Tuesday 09 August 2011, 14:4515:30
Cambridge University
2449
http://sms.cam.ac.uk/media/1162659
Block designs for nonnormal data via conditional and marginal models
Woods, D (Southampton)
Tuesday 09 August 2011, 14:4515:30
Many experiments in all areas of science, technology and industry measure a response that cannot be adequately described by a linear model with normally distributed errors. In addition, the further complication often arises of needing to arrange the experiment into blocks of homogeneous units. Examples include industrial manufacturing experiments with binary responses, clinical trials where subjects receive multiple treatments and crystallography experiments in earlystage drug discovery. This talk will present some new approaches to the design of such experiments, assuming both conditional (subjectspecific) and marginal (populationaveraged) models. The different methods will be compared, and some advantages and disadvantages highlighted. Common issues, including the impact of correlations and the dependence of the design on the values of model parameters, will also be discussed.
20110811T14:44:55+01:00
2449
1162659
true
16x9
false
no

Bridge Designs for Modeling Systems with Small Error Variance
ucs_sms_125_1171933
http://sms.cam.ac.uk/media/1171933
Bridge Designs for Modeling Systems with Small Error Variance
Jones, B (SAS Institute, Inc.)
Tuesday 06 September 2011, 16:0016:30
Wed, 14 Sep 2011 11:24:55 +0100
Jones, B
Steve Greenham
Isaac Newton Institute
Jones, B
76125aa881e4353fbeebdd6bb780d44b
ab1bd67b64c535966ecb36dccaef29a9
5a6af38d9844440d40e20562c97f3b28
ceeb0d98ec10f50c6bc085486fd78bbc
203f44bda5463279b9f76520217da665
Jones, B (SAS Institute, Inc.)
Tuesday 06 September 2011, 16:0016:30
Jones, B (SAS Institute, Inc.)
Tuesday 06 September 2011, 16:0016:30
Cambridge University
1712
http://sms.cam.ac.uk/media/1171933
Bridge Designs for Modeling Systems with Small Error Variance
Jones, B (SAS Institute, Inc.)
Tuesday 06 September 2011, 16:0016:30
A necessary characteristic of designs for deterministic computer simulations is that they avoid replication. This characteristic is also necessary for onedimensional projections of the design, since it may turn out that only one of the design factors has any nonnegligible effect on the response. Latin Hypercube designs have uniform onedimensional projections are not efficient for fitting low order polynomials when there is a small error variance. Doptimal designs are very efficient for polynomial fitting but have substantial replication in projections. We propose a new class of designs that bridge the gap between Latin Hypercube designs and Doptimal designs. These designs guarantee a minimum distance between points in any onedimensional projection. Subject to this constraint they are Doptimal for any prespecified model.
20110914T11:25:05+01:00
1712
1171933
true
16x9
false
no

Calibration of multifidelity models for radiative shock
ucs_sms_125_1172423
http://sms.cam.ac.uk/media/1172423
Calibration of multifidelity models for radiative shock
Bingham, D (Simon Fraser University)
Friday 09 September 2011, 09:3010:00
Thu, 15 Sep 2011 12:58:02 +0100
Bingham, D
Steve Greenham
Isaac Newton Institute
Bingham, D
d4d1a31c2e0e5784e8aa588bb2bb29f2
43b9e27d26674d11371c3532c17405dc
68f660eb31c00940c124d7de7594c40f
fbfe5887a239e84d1235dc8a85db574a
86c909abda965c8e9d7fb36c2076e97e
Bingham, D (Simon Fraser University)
Friday 09 September 2011, 09:3010:00
Bingham, D (Simon Fraser University)
Friday 09 September 2011, 09:3010:00
Cambridge University
1873
http://sms.cam.ac.uk/media/1172423
Calibration of multifidelity models for radiative shock
Bingham, D (Simon Fraser University)
Friday 09 September 2011, 09:3010:00
Environmental and economic industry relies on highperformance materials such as lightweight alloys, recyclable motor vehicle and building components, and high efficiency lighting. Material properties as expressed through crystal structure is crucial to this understanding. Based on firstprinciples calculations, it is still impossible in most materials to infer groundstate properties purely from a knowledge of their atomic components. Many methods attempt to predict crystal structures and compound stability, we explore models which infer the existence of structures on the basis of combinatorics and geometric simplicity. Computational models based on these first physics principles are called VASP codes. We illustrate the use of a statistical surrogate model to produce predictions of VASP codes as a function of a moderate number of VASP inputs.
20110915T12:58:12+01:00
1873
1172423
true
16x9
false
no

Cambridge Statistics Initiative (CSI) Special OneDay meeting: Discussion & Conclusions
ucs_sms_125_1178494
http://sms.cam.ac.uk/media/1178494
Cambridge Statistics Initiative (CSI) Special OneDay meeting: Discussion & Conclusions
Dawid, P
Monday 26 September 2011, 17:1017:30
Tue, 04 Oct 2011 12:11:42 +0100
Isaac Newton Institute
Dawid, P
ef8bbca2f58c90b2d4f9d7dd1e972edc
3f6718101c2ce75adcd0846f62011433
0982af4e89af2eebde589cb64877deee
57aa0b84b21fa3a6016fdb139d8f406f
b1314b040c8928f3878bc08b56c953bf
Dawid, P
Monday 26 September 2011, 17:1017:30
Dawid, P
Monday 26 September 2011, 17:1017:30
Cambridge University
241
http://sms.cam.ac.uk/media/1178494
Cambridge Statistics Initiative (CSI) Special OneDay meeting: Discussion & Conclusions
Dawid, P
Monday 26 September 2011, 17:1017:30
20111004T12:11:52+01:00
241
1178494
true
16x9
false
no

Cambridge Statistics Initiative (CSI) Special OneDay meeting: Introduction
ucs_sms_125_1176216
http://sms.cam.ac.uk/media/1176216
Cambridge Statistics Initiative (CSI) Special OneDay meeting: Introduction
Samworth, R
Monday 26 September 2011, 09:3009:40
Fri, 30 Sep 2011 15:07:19 +0100
Isaac Newton Institute
Samworth, R
6b24cca397fa644649a390ff047c6bd5
2b6bdcda1f3f05c945959603bbc07552
d439e4ee8f2e269b8a66e3004cc16c3a
c2d0c0e52e94b4deb59aa42ab482a567
7fd867482b8127813015372ed3e38c79
Samworth, R
Monday 26 September 2011, 09:3009:40
Samworth, R
Monday 26 September 2011, 09:3009:40
Cambridge University
308
http://sms.cam.ac.uk/media/1176216
Cambridge Statistics Initiative (CSI) Special OneDay meeting: Introduction
Samworth, R
Monday 26 September 2011, 09:3009:40
The Cambridge Statistics Initiative (CSI) will host a third Special OneDay meeting, jointly organised with the Design and Analysis of Experiments programme at the Isaac Newton Institute for Mathematical Sciences. This is part of continuing efforts to bring together statisticians from various fields within the University of Cambridge and outside, to meet as well as encourage new research collaborations within academia and industry.
20110930T15:07:29+01:00
308
1176216
true
16x9
false
no

Can we design for smoothing parameters?
ucs_sms_125_1506
http://sms.cam.ac.uk/media/1506
Can we design for smoothing parameters?
Challenor, P (National Oceanography Centre)
Friday 15 August 2008, 12:0012:30
Wed, 17 Sep 2008 10:22:41 +0100
Challenor, P
Isaac Newton Institute
Challenor, P
6863273c67c855b02dbfaf7b225b4cc1
390a42ead6676227af6148dba357fb4a
86a69aef149f7a5acda5d08e4f58b707
29fe65f6e8c84d91fb4ce646ead87826
3baab678b537d6bcabc1b9c98405557a
1a50a46611db4ff84dc32779cf34c8b4
2cc458548526660044e50ecd1050a105
Challenor, P (National Oceanography Centre)
Friday 15 August 2008, 12:0012:30
Challenor, P (National Oceanography Centre)
Friday 15 August 2008, 12:0012:30
Cambridge University
1800
http://sms.cam.ac.uk/media/1506
Can we design for smoothing parameters?
Challenor, P (National Oceanography Centre)
Friday 15 August 2008, 12:0012:30
When we analyse computer experiments we usually use an emulator (or surrogate). Emulators are based on Gaussian processes with parameters estimated from a designed experiments. Most effort in the design of computer experiments has concentrated on the idea of 'space filling' design, such as the Latin hypercube. However an important parameter in the emulator is its smoothness. Intuition suggests that adding some points closer together should improve our estimates of smoothness over the standard space filling designs. Using some ideas from geostatistics we investigate whether we can improve our designs in this way.
http://www.newton.ac.uk/programmes/DOE/seminars/index.html
20130301T11:14:17+00:00
1800
1506
true
4x3
false
no

Casual inference in observational epidemiology: looking into mechanism
ucs_sms_125_1178149
http://sms.cam.ac.uk/media/1178149
Casual inference in observational epidemiology: looking into mechanism
Berzuini, C (Stats Lab)
Monday 26 September 2011, 14:4014:50
Tue, 04 Oct 2011 10:36:28 +0100
Isaac Newton Institute
Berzuini, C
9c7146b678cf154eac4ba51848cfd389
ba23337e7508b424059d6b524d513d00
ce06cf303303830ffa8b9340e8fc641f
6d2784d3e0967494447f4e98b5450973
66e38ccd50713ec4956a8cd9e6ac57e6
Berzuini, C (Stats Lab)
Monday 26 September 2011, 14:4014:50
Berzuini, C (Stats Lab)
Monday 26 September 2011, 14:4014:50
Cambridge University
736
http://sms.cam.ac.uk/media/1178149
Casual inference in observational epidemiology: looking into mechanism
Berzuini, C (Stats Lab)
Monday 26 September 2011, 14:4014:50
We propose a method for the study of genegene, geneenvironment and genetreatment interactions which are interpretable in terms of mechanism. Tests for detecting mechanistic  as opposed to 'statistical'  interactions have been previously proposed, but they are meaningful only if a number of assumptions and conditions are verified. Con sequently, they are not always applicable and, in those situations where they are, their validity depends on an appropriate choice of stratifying variables. This paper proposes a novel formulation of the problem. We illustrate the method with the aid of studies where evidence from casecontrol studies of genetic association is combined with information from biological experiments, to elucidate the role of specific molecular mechanism (au tophagy, ion channels) in susceptibility to specific diseases (Crohn's Disease, Multiple Sclerosis).
20111004T10:36:37+01:00
736
1178149
true
16x9
false
no

Causal Inference from 2level factorial designs
ucs_sms_125_1168563
http://sms.cam.ac.uk/media/1168563
Causal Inference from 2level factorial designs
Dasgupta, T (Harvard)
Tuesday 30 August 2011, 14:3015:00
Thu, 01 Sep 2011 13:47:15 +0100
Dasgupta, T
Steve Greenham
Isaac Newton Institute
Dasgupta, T
59a64851b6cd22b193491e209707d283
65c44bd98a4eff5cdbaf3a38694d8985
deda4597c89cead276a73decf08df26e
d71321d178df1982da7e196a842ed006
3de1b77ad0f3a87f37feb9ede3c806bc
Dasgupta, T (Harvard)
Tuesday 30 August 2011, 14:3015:00
Dasgupta, T (Harvard)
Tuesday 30 August 2011, 14:3015:00
Cambridge University
1658
http://sms.cam.ac.uk/media/1168563
Causal Inference from 2level factorial designs
Dasgupta, T (Harvard)
Tuesday 30 August 2011, 14:3015:00
A framework for causal inference from twolevel factorial and fractional factorial designs with particular sensitivity to applications to social, behavioral and biomedical sciences is proposed. The framework utilizes the concept of potential outcomes that lies at the center stage of causal inference and extends Neyman's repeated sampling approach for estimation of causal effects and randomization tests based on Fisher's sharp null hypothesis to the case of 2level factorial experiments. The framework allows for statistical inference from a finite population, permits definition and estimation of parameters other than "average factorial effects" and leads to more flexible inference procedures than those based on ordinary least squares estimation from a linear model. It also ensures validity of statistical inference when the investigation becomes an observational study in lieu of a randomized factorial experiment due to randomization restrictions.
20110901T13:47:25+01:00
1658
1168563
true
16x9
false
no

Causal Inference from Experimental Data
ucs_sms_125_1187347
http://sms.cam.ac.uk/media/1187347
Causal Inference from Experimental Data
Dawid, P (University of Cambridge)
Thursday 10 November 2011, 17:0018:00
30th R A Fisher Memorial Lecture
Fri, 11 Nov 2011 12:25:30 +0000
Dawid, P
Steve Greenham
Isaac Newton Institute
Dawid, P
8434fe5e4647da310231a6ff152d97e0
ea86ae8d338e606a7d829a508c967df4
2d81dedc040be40005c729624172a5aa
95ea46ac590e9ff00c466352d35c6f20
a037868f1ce48099b2cf56f1b5635767
Dawid, P (University of Cambridge)
Thursday 10 November 2011,...
Dawid, P (University of Cambridge)
Thursday 10 November 2011, 17:0018:00
30th R A Fisher Memorial Lecture
Cambridge University
3673
http://sms.cam.ac.uk/media/1187347
Causal Inference from Experimental Data
Dawid, P (University of Cambridge)
Thursday 10 November 2011, 17:0018:00
30th R A Fisher Memorial Lecture
One of the greatest scientific advances of the 20th Century was not substantive, but methodological: the laying out by Fisher of the principles of sound experimentation, so allowing valid conclusions to be drawn about the effects of interventions  what we must surely regard as "causal inference". More recently "causal inference" has developed as a major enterprise in its own right, with its own specialist formulations and methods; however, these owe more to Neyman than to Fisher. In this lecture I shall explore the connexions and contrasts between older and newer ideas in causal inference, revisit an old argument between Neyman and Fisher, and argue for the restructuring of modern theories of causal inference along more Fisherian lines.
20111111T12:25:41+00:00
3673
1187347
true
16x9
false
no

Challenges in the Design and Analysis of a Randomized, Phased Implementation (SteppedWedge) Study in Brazil
ucs_sms_125_1165839
http://sms.cam.ac.uk/media/1165839
Challenges in the Design and Analysis of a Randomized, Phased Implementation (SteppedWedge) Study in Brazil
Moulton, L (Johns Hopkins)
Wednesday 17 August 2011, 11:4512:30
Thu, 18 Aug 2011 11:26:41 +0100
Moulton, L
Steve Greenham
Isaac Newton Institute
Moulton, L
7129d522333780c6442e23b753002f9b
e994dea372593caa286f16cf641e3eb7
a752a17da21cbdaf30769fcb0ffab318
cf1f2e324a629d2740a8a9d8a781e5ad
52e2eed97fed577f760297388d0405e1
b6998f27c8c27515da836192221a522c
6f6aeb7dc5d4570c9521f41a2ef3698c
Moulton, L (Johns Hopkins)
Wednesday 17 August 2011, 11:4512:30
Moulton, L (Johns Hopkins)
Wednesday 17 August 2011, 11:4512:30
Cambridge University
3346
http://sms.cam.ac.uk/media/1165839
Challenges in the Design and Analysis of a Randomized, Phased Implementation (SteppedWedge) Study in Brazil
Moulton, L (Johns Hopkins)
Wednesday 17 August 2011, 11:4512:30
The cluster randomized oneway crossover design, known as a steppedwedge design, is becoming increasingly popular, especially for health studies in less industrialized countries. This design, however, presents numerous challenges, both for design and analysis.
Two issues regarding the design of a steppedwedge study will be highlighted: randomization and power. Specifically, first, there is the question of how best to constrain the randomization so that it is balanced over time with respect to covariatesa highly constrained but ad hoc procedure will be presented. Second, the various pieces of information necessary for a full power calculation will be delineated.
As with clusterrandomized designs in general, close attention must be given to study hypotheses of interest, and the relation of these to the two levels of interventioncluster and individual. A study of isoniazid prophylaxis implementation in 29 clinics in Rio de Janeiro is used to exemplify the range of questions that can arise. A few analyses of the data are also presented, so as to illustrate the degree to which data analytic choices to address these questions can vary the results, and to show the longitudinal complexities that need be considered.
20110818T11:26:50+01:00
3346
1165839
true
4x3
false
no

Challenges When Interfacing Physical Experiments and Computer Models
ucs_sms_125_1171890
http://sms.cam.ac.uk/media/1171890
Challenges When Interfacing Physical Experiments and Computer Models
Reinman, G; O'Hagan, A; Bingham, D
Tuesday 06 September 2011, 14:0015:00
Wed, 14 Sep 2011 10:39:06 +0100
Steve Greenham
Isaac Newton Institute
Reinman, G; O'Hagan, A; Bingham, D
3ab53075e8bb8a1a8b23cbb12f959431
9c231736d9276c0f8f9a996b277c26a9
1b77a513e3093c2a9baefbb21feff385
095e309a23f8bc71649fb7a4e032f501
293b47768d0d00ce3a6427530656b99d
Reinman, G; O'Hagan, A; Bingham, D
Tuesday 06 September 2011, 14:0015:00
Reinman, G; O'Hagan, A; Bingham, D
Tuesday 06 September 2011, 14:0015:00
Cambridge University
3653
http://sms.cam.ac.uk/media/1171890
Challenges When Interfacing Physical Experiments and Computer Models
Reinman, G; O'Hagan, A; Bingham, D
Tuesday 06 September 2011, 14:0015:00
Panel discussion from the Design and Modelling session, during the Accelerating Industrial Productivity via Deterministic Computer Experiments & Stochastic Simulation conference.
20110914T10:39:16+01:00
3653
1171890
true
16x9
false
no

Cluster Randomised Trials: coping with selective recruitment, baseline covariates and anticipated dropouts?
ucs_sms_125_1165251
http://sms.cam.ac.uk/media/1165251
Cluster Randomised Trials: coping with selective recruitment, baseline covariates and anticipated dropouts?
Campbell, M (Sheffield)
Monday 15 August 2011, 14:4515:25
Wed, 17 Aug 2011 14:27:19 +0100
Campbell, M
Isaac Newton Institute
Campbell, M
f57814de4ee88e97fce32c6134aeb289
9e16ddf09a7b512e9d73adde77c637d9
567e6064f073399fb32d403ab79456b6
68f13318b1490346e801cae718392632
d9764d78003bc9bfe2a1c507bd5e254c
Campbell, M (Sheffield)
Monday 15 August 2011, 14:4515:25
Campbell, M (Sheffield)
Monday 15 August 2011, 14:4515:25
Cambridge University
2208
http://sms.cam.ac.uk/media/1165251
Cluster Randomised Trials: coping with selective recruitment, baseline covariates and anticipated dropouts?
Campbell, M (Sheffield)
Monday 15 August 2011, 14:4515:25
20110817T14:27:28+01:00
2208
1165251
true
16x9
false
no

Communicating and evaluating probabilities
ucs_sms_125_1178181
http://sms.cam.ac.uk/media/1178181
Communicating and evaluating probabilities
Spiegelhalter, D (MRC Biostats & Stats Lab)
Monday 26 September 2011, 16:1516:25
Tue, 04 Oct 2011 10:45:38 +0100
Isaac Newton Institute
Spiegelhalter, D
70321ab1a4a7dfd69670a81b9fa01ad4
2e91067427ed67960fa8fc40f63373c9
23276168255106f30031cb8eee2da02f
1086cccc0271ee3135a93d9dd775fee2
39bf465352641de6419861a77cf85ea9
Spiegelhalter, D (MRC Biostats & Stats Lab)
Monday 26 September 2011,...
Spiegelhalter, D (MRC Biostats & Stats Lab)
Monday 26 September 2011, 16:1516:25
Cambridge University
913
http://sms.cam.ac.uk/media/1178181
Communicating and evaluating probabilities
Spiegelhalter, D (MRC Biostats & Stats Lab)
Monday 26 September 2011, 16:1516:25
I will talk about three related projects: (a) Getting children to express numerical confidence in their knowledge (b) Collaboration with the Met Office in an online weather game incorporating probabilistic forecasts. (c) Development of an online quiz.
20111004T10:45:48+01:00
913
1178181
true
16x9
false
no

Computation of the Fisher information matrix for discrete nonlinear mixed effects models
ucs_sms_125_2025480
http://sms.cam.ac.uk/media/2025480
Computation of the Fisher information matrix for discrete nonlinear mixed effects models
Ueckert, S (INSERM, Paris)
Tuesday 7th July 2015, 12:00  12:30
Fri, 10 Jul 2015 13:06:45 +0100
Isaac Newton Institute
Ueckert, S
87dd70caa24589294d02646ba6597b97
3d1750771df76d2b51d4559806501acf
2e021ff235f0fd3d3c109c547d2db568
f69eefbe8c5a3d88bff60480d911d145
Ueckert, S (INSERM, Paris)
Tuesday 7th July 2015, 12:00  12:30
Ueckert, S (INSERM, Paris)
Tuesday 7th July 2015, 12:00  12:30
Cambridge University
2597
http://sms.cam.ac.uk/media/2025480
Computation of the Fisher information matrix for discrete nonlinear mixed effects models
Ueckert, S (INSERM, Paris)
Tuesday 7th July 2015, 12:00  12:30
Despite an increasing use of optimal design methodology for nonlinear mixed effect models (NLMEMs) during the clinical drug development process (Mentr´e et al., 2013), examples involving discrete data NLMEMs remain scarce (Ernest et al., 2014). One reason are the limitations of existing approaches to calculate the Fisher information matrix (FIM) which are either model dependent and based on linearization (Ogungbenro and Aarons, 2011) or computationally very expensive (Nyberg et al., 2009). The main computational challenges in the computation of the FIM for discrete NLMEMs evolve around the calculation of two integrals. First the integral required to calculate the expectation over the data and second the integral of the likelihood over the distribution of the random effects. In this presentation MonteCarlo (MC), LatinHypercube (LH) and QuasiRandom (QR) sampling for the calculation of the first as well as adaptive Gaussian quadrature (AGQ) and QR sampling for the calculation of the second integral are proposed. The resulting methods are compared for a number of discrete data models and evaluated in the context of model based adaptive optimal design.
20150710T13:06:45+01:00
2597
2025480
true
16x9
false
no

Conjoint choice experiments for estimating efficiently willingnesstopay
ucs_sms_125_1461
http://sms.cam.ac.uk/media/1461
Conjoint choice experiments for estimating efficiently willingnesstopay
Vandebroek, M (Leuven)
Wednesday 13 August 2008, 09:3010:00
Fri, 12 Sep 2008 14:07:10 +0100
Vandebroek, M
Isaac Newton Institute
Vandebroek, M
becdcc3bc695d90f238c5b8890605e97
e396d045d71091fc4344aa70165c9d0f
b83b12215471c566bfa6af512ec5049a
3e3592bd25b1f52d665f926ebe5fc0dd
f45042b1f3a13c57ff91573bf56de626
277a3ae5820bda3325c8ff37cd0035ae
e81f8f3b90198a2f407adc1a39d2c506
Vandebroek, M (Leuven)
Wednesday 13 August 2008, 09:3010:00
Vandebroek, M (Leuven)
Wednesday 13 August 2008, 09:3010:00
Cambridge University
1898
http://sms.cam.ac.uk/media/1461
Conjoint choice experiments for estimating efficiently willingnesstopay
Vandebroek, M (Leuven)
Wednesday 13 August 2008, 09:3010:00
In a stated preference or conjoint experiment respondents evaluate a number of products that are defined by their underlying characteristics. The resulting data yield information on the importance that respondents attach to the different characteristics, also called the partworths. In a conjoint choice experiment, respondents indicate which alternative they prefer from each choice set presented to them. The design of a conjoint choice experiment consists of choosing the appropriate alternatives and of grouping the alternatives in choice sets such that the information gathered about the partworths is maximized. In this talk special attention will be given to the problem of assessing accurately the marginal rate of substitution by a conjoint choice experiment. The marginal rate of substitution measures the consumer's willingness to give up an attribute of a good in exchange for another attribute. As this rate of substitution is computed by taking the ratio of two partworths, specific design problems are involved.
http://www.newton.ac.uk/programmes/DOE/seminars/index.html
20130228T12:15:07+00:00
1898
1461
true
4x3
false
no

Constrained Optimization and Calibration for Deterministic and Stochastic Simulation Experiments
ucs_sms_125_1172191
http://sms.cam.ac.uk/media/1172191
Constrained Optimization and Calibration for Deterministic and Stochastic Simulation Experiments
Lee, H (University of California, Santa Cruz)
Thursday 08 September 2011, 14:3015:00
Wed, 14 Sep 2011 18:32:28 +0100
Lee, H
Steve Greenham
Isaac Newton Institute
Lee, H
ba6799656e790975e946842bf66c4a9f
45b2cb20c204270bcf1e6b488d047d53
73d06fbe328606ff01555fd7f674c7ae
98ec00c944aa012b43d6505cd4e5dbf8
79df57489d5e3b45ead22f52adbb4cbe
Lee, H (University of California, Santa Cruz)
Thursday 08 September 2011,...
Lee, H (University of California, Santa Cruz)
Thursday 08 September 2011, 14:3015:00
Cambridge University
1807
http://sms.cam.ac.uk/media/1172191
Constrained Optimization and Calibration for Deterministic and Stochastic Simulation Experiments
Lee, H (University of California, Santa Cruz)
Thursday 08 September 2011, 14:3015:00
Optimization of the output of computer simulators, whether deterministic or stochastic, is a challenging problem because of the typical severe multimodality. The problem is further complicated when the optimization is subject to unknown constraints, those that depend on the value of the output, so the function must be evaluated in order to determine if the constraint has been violated. Yet, even an invalid response may still be informative about the function, and thus could potentially be useful in the optimization. We develop a statistical approach based on Gaussian processes and Bayesian learning to approximate the unknown function and to estimate the probability of meeting the constraints, leading to a sequential design for optimization and calibration.
20110914T18:32:37+01:00
1807
1172191
true
16x9
false
no

Constructing and Assessing Exact GOptimal Designs
ucs_sms_125_1168546
http://sms.cam.ac.uk/media/1168546
Constructing and Assessing Exact GOptimal Designs
Montgomery, D (Arizona State)
Tuesday 30 August 2011, 14:0014:30
Thu, 01 Sep 2011 13:40:52 +0100
Montgomery, D
Steve Greenham
Isaac Newton Institute
Montgomery, D
89a31190de676528ca25a620cb084897
6008d9abb728cdf511ac8cf8d1b49b8b
56a659df32404d4cbd3377749bcbe324
7170868e1875a7bd563fa55ddf467517
d9f0fadd3e4af8c8a911d5c21fc1c278
Montgomery, D (Arizona State)
Tuesday 30 August 2011, 14:0014:30
Montgomery, D (Arizona State)
Tuesday 30 August 2011, 14:0014:30
Cambridge University
1839
http://sms.cam.ac.uk/media/1168546
Constructing and Assessing Exact GOptimal Designs
Montgomery, D (Arizona State)
Tuesday 30 August 2011, 14:0014:30
Methods for constructing Goptimal designs are reviewed. A new and very efficient algorithm for generating near Goptimal designs is introduced, and employed to construct designs for secondorder models over cuboidal regions. The algorithm involves the use of Brent’s minimization algorithm with coordinate exchange to create designs for 2 to 5 factors. Designs created using this new method either match or exceed the Gefficiency of previously reported designs. A new graphical tool, the variance ratio fraction of design space (VRFDS) plot, is used for comparison of the prediction variance for competing designs over a given region of interest. Using the VRFDS plot to compare Goptimal designs to Ioptimal designs shows that the Goptimal designs have higher prediction variance over the vast majority of the design region. This suggests that, for many response surface studies, Ioptimal designs may be superior to Goptimal designs.
20110901T13:41:02+01:00
1839
1168546
true
16x9
false
no

Construction of efficient experimental designs under resource constraints
ucs_sms_125_2027310
http://sms.cam.ac.uk/media/2027310
Construction of efficient experimental designs under resource constraints
Harman, R (Comenius University)
Wednesday 8th July 2015, 11:30  12:00
Mon, 13 Jul 2015 12:31:33 +0100
Isaac Newton Institute
Harman, R
27aae08f2448db2dd5088ead366d5d5a
56ee2377a05bdda3e561cca3f66d6a42
1ecc26104ee02a389916fdbfd8685868
5d16c569c48c22d9d08d17f6f11fb325
Harman, R (Comenius University)
Wednesday 8th July 2015, 11:30  12:00
Harman, R (Comenius University)
Wednesday 8th July 2015, 11:30  12:00
Cambridge University
1631
http://sms.cam.ac.uk/media/2027310
Construction of efficient experimental designs under resource constraints
Harman, R (Comenius University)
Wednesday 8th July 2015, 11:30  12:00
We will introduce "resource constraints" as a general concept that covers many practical restrictions on experimental design, such as limits on budget, time, or available material. To compute optimal or nearoptimal exact designs of experiments under multiple resource constraints, we will propose a tabu search heuristic related to the Detmax procedure. To illustrate the scope and performance of the proposed algorithm, we chose the problem of construction of optimal experimental designs for dose escalation studies.
20150713T12:31:33+01:00
1631
2027310
true
16x9
false
no

Construction of orthogonal and nearly orthogonal Latin hypercube designs for computer experiments
ucs_sms_125_1171675
http://sms.cam.ac.uk/media/1171675
Construction of orthogonal and nearly orthogonal Latin hypercube designs for computer experiments
Tang, B (Simon Fraser University)
Tuesday 06 September 2011, 09:0009:30
Tue, 13 Sep 2011 13:45:54 +0100
Tang, B
Steve Greenham
Isaac Newton Institute
Tang, B
fa13fff2079621cba2b626812029758f
c3d4d61e16d15e0143c02089e7a12b16
e56c397c6775858cff5ac5dc707c7ab0
295928ecd44e8e5f0470dce73989d8c1
474f5825db5c7d13fd13e268933c5304
Tang, B (Simon Fraser University)
Tuesday 06 September 2011, 09:0009:30
Tang, B (Simon Fraser University)
Tuesday 06 September 2011, 09:0009:30
Cambridge University
1800
http://sms.cam.ac.uk/media/1171675
Construction of orthogonal and nearly orthogonal Latin hypercube designs for computer experiments
Tang, B (Simon Fraser University)
Tuesday 06 September 2011, 09:0009:30
We present a method for constructing good designs for computer experiments. The method derives its power from its basic structure that builds large designs using small designs. We specialize the method for the construction of orthogonal Latin hypercubes and obtain many results along the way. In terms of run sizes, the existence problem of orthogonal Latin hypercubes is completely solved. We also present an explicit result showing that how large orthogonal Latin hypercubes can be constructed using small orthogonal Latin hypercubes. Another appealing feature of our method is that it can easily be adapted to construct other designs. We examine how to make use of the method to construct nearly orthogonal Latin hypercubes.
20110913T13:46:03+01:00
1800
1171675
true
16x9
false
no

Cost constrained optimal designs for regression models with random parameters
ucs_sms_125_2025541
http://sms.cam.ac.uk/media/2025541
Cost constrained optimal designs for regression models with random parameters
Fedorov, V (Innovation Center, ICON plc.)
Tuesday 7th July 2015, 16:30 to 17:00
Fri, 10 Jul 2015 13:38:30 +0100
Isaac Newton Institute
Fedorov, V
cf1a6ddf93986bb61f311d409d38c74e
5f8794a62784159350aa1678368dcdf3
9703c1630057a5f4360e499f4936879a
fe9ed930f7ff2b4fe25447700c652a21
Fedorov, V (Innovation Center, ICON plc.)
Tuesday 7th July 2015, 16:30 to 17:00
Fedorov, V (Innovation Center, ICON plc.)
Tuesday 7th July 2015, 16:30 to 17:00
Cambridge University
2695
http://sms.cam.ac.uk/media/2025541
Cost constrained optimal designs for regression models with random parameters
Fedorov, V (Innovation Center, ICON plc.)
Tuesday 7th July 2015, 16:30 to 17:00
I describe various optimization problems related to the design of experiments for regression models with random parameters, aka mixed effect models and population models. In the terms of the latter two different goals can be pursuit: estimation of population parameters and individual parameters. Respectively we have to face two types of optimality criteria and cost constraints. Additional strata appear if one would observe that the following two experimental situations occur in practice: either repeated observations are admissible for a given experimental unit (object or subject), or not. Clinical studies with multiple sites with slightly different treatment outcomes (treatmentbycite interaction) is an example when repeated and independent observation are possible  a few subjects can on each treatment arm. PK studies may serve as an example when repeated observations cannot be performed  only one observation at the given moment can be performed on a subject. All these caveats lead to the different design problems that I try to link together.
20150710T13:38:30+01:00
2695
2025541
true
16x9
false
no

Coupled Gaussian Process Models
ucs_sms_125_1172141
http://sms.cam.ac.uk/media/1172141
Coupled Gaussian Process Models
Joseph, R V (Georgia Institute of Technology)
Thursday 08 September 2011, 12:0012:30
Wed, 14 Sep 2011 16:16:24 +0100
Joseph, R V
Steve Greenham
Isaac Newton Institute
Joseph, R V
0156e41f53c69631dd63654047e8195d
793f8a44be568654cbd269ce6d94cefc
8aa8f3e0dd96a841fec1720d34af1157
6f8ba3f99f51a78e43f82f4f16c7c01c
1bb409fdabd3c38c56523ed9ba92f258
Joseph, R V (Georgia Institute of Technology)
Thursday 08 September 2011,...
Joseph, R V (Georgia Institute of Technology)
Thursday 08 September 2011, 12:0012:30
Cambridge University
1697
http://sms.cam.ac.uk/media/1172141
Coupled Gaussian Process Models
Joseph, R V (Georgia Institute of Technology)
Thursday 08 September 2011, 12:0012:30
Gaussian Process (GP) models are commonly employed in computer experiments for modeling deterministic functions. The model assumes secondorder stationarity and therefore, the predictions can become poor when such assumptions are violated. In this work, we propose a more accurate approach by coupling two GP models together that incorporates both the nonstationarity in mean and variance. It gives better predictions when the experimental design is sparse and can also improve the prediction intervals by quantifying the change of local variability associated with the response. Advantages of the new predictor are demonstrated using several examples from the literature.
20110914T16:16:33+01:00
1697
1172141
true
16x9
false
no

Crosssectional versus longitudinal design: does it matter?
ucs_sms_125_2028346
http://sms.cam.ac.uk/media/2028346
Crosssectional versus longitudinal design: does it matter?
Schwabe, R (OttovonGuerickeUniversität Magdeburg)
Friday 10th July 2015, 09:45  10:30
Tue, 14 Jul 2015 17:02:02 +0100
Isaac Newton Institute
Schwabe, R
6095c622584f275bbb2301ab2476445c
c07e82106340c64a4ee133291b82a3eb
6895bdaa0b99e5036680ba03709c2dec
16a8e586da966f3140894498dc212f37
Schwabe, R (OttovonGuerickeUniversität Magdeburg)
Friday 10th July 2015,...
Schwabe, R (OttovonGuerickeUniversität Magdeburg)
Friday 10th July 2015, 09:45  10:30
Cambridge University
2136
http://sms.cam.ac.uk/media/2028346
Crosssectional versus longitudinal design: does it matter?
Schwabe, R (OttovonGuerickeUniversität Magdeburg)
Friday 10th July 2015, 09:45  10:30
Mixed models play an important role in describing observations in healthcare. To keep things simple we only consider linear mixed models, and we are only interested in the typical response, i.e. in the population location parameters. When discussing the corresponding design issues one is often faced with the widespread belief that standard designs which are optimal, when the random effects are neglected, retain their optimality in the mixed model. This seems to be obviously true, if there are only random block effects related to a unit specific level (e.g. random intercept). However, if there are also random effect sizes which vary substantially between units, then these standard designs may lose their optimality property. This phenomenon occurs in the situation of a crosssectional design, where the experimental setting is fixed for each unit and varies between units, as well as in the situation of a longitudinal design, where the experimental setting varies within units and coincides across units. We will compare the resulting optimal solutions and check their relative efficiencies.
20150714T17:02:02+01:00
2136
2028346
true
16x9
false
no

Doptimal designs for multinomial experiments
ucs_sms_125_1169548
http://sms.cam.ac.uk/media/1169548
Doptimal designs for multinomial experiments
Russell, K (Charles Sturt University)
Thursday 01 September 2011, 09:3010:00
Mon, 05 Sep 2011 10:39:05 +0100
Russell, K
Steve Greenham
Isaac Newton Institute
Russell, K
e7434338f978966dfb8083fafb5487b1
102fc92e50bed5718d94aae5c4f28318
34dfde4ad059c63e186089552ada9f71
98d86fc172f09280b7a3fa72a6744f68
e622a745d47992e48814905cb50d4fd2
Russell, K (Charles Sturt University)
Thursday 01 September 2011, 09:3010:00
Russell, K (Charles Sturt University)
Thursday 01 September 2011, 09:3010:00
Cambridge University
1562
http://sms.cam.ac.uk/media/1169548
Doptimal designs for multinomial experiments
Russell, K (Charles Sturt University)
Thursday 01 September 2011, 09:3010:00
Consider a multinomial experiment where the value of a response variable falls in one of k classes. The k classes are not assumed to have a hierarchical structure. Let ij represent the probability that the ith experimental unit gives a response that falls in the jth class. By modelling ln(ij=i1) as a linear function of the values of m predictor variables, we may analyse the results of the experiment using a Generalized Linear Model.
We describe the construction of Doptimal experimental designs for use in such an experiment. Difficulties in obtaining these designs will be described, together with attempts to overcome these obstacles.
20110905T10:39:14+01:00
1562
1169548
true
16x9
false
no

Doptimal designs for TwoVariable Binary Logistic Models with Interaction
ucs_sms_125_1168717
http://sms.cam.ac.uk/media/1168717
Doptimal designs for TwoVariable Binary Logistic Models with Interaction
Haines, L (Cape Town)
Wednesday 31 August 2011, 15:0015:30
Thu, 01 Sep 2011 14:58:36 +0100
Haines, L
Steve Greenham
Isaac Newton Institute
Haines, L
98d02bf42025c58a933ba006114d6fca
50574e1c435b0af467f38164104d8cd1
10d128565a0fe5356ce31ff19dbf0c47
ae9b66b9a63c56f31f6ee1f60f7535d0
0fc30bca3bd1700b5c0b19dad6bca626
Haines, L (Cape Town)
Wednesday 31 August 2011, 15:0015:30
Haines, L (Cape Town)
Wednesday 31 August 2011, 15:0015:30
Cambridge University
1563
http://sms.cam.ac.uk/media/1168717
Doptimal designs for TwoVariable Binary Logistic Models with Interaction
Haines, L (Cape Town)
Wednesday 31 August 2011, 15:0015:30
It is not uncommon for medical researchers to administer two drugs simultaneously to a patient and to monitor the response as binary, that is either positive or negative. Interest lies in the interaction of the drugs and specifically in whether that interaction is synergistic, antagonistic or simply additive. A number of statistical models for this setting have been proposed in the literature, some complex, but arguably the most widely used is the twovariable binary logistic model which can be formulated succinctly as ln (p/(1p)) = beta0 + beta1 x1 + beta2 x2 + + beta12 x1 x2 (*) where p is the probability of a positive response, x1 and x2 are the doses or logdoses of the drugs and beta0, beta1, beta2 and beta12 are unknown parameters. There is a broad base of research on the fitting, analysis and interpretation of this model but, somewhat surprisingly, few studies on the construction of the attendant optimal designs. In fact there are two substantive reports on this design problem, both unpublished, namely the Ph.D. thesis of Kupchak (2000) and the technical report of Jia and Myers (2001). In this talk the problem of constructing Doptimal designs for the model (*) is addressed. The approach builds on that of Jia and Myers (2001) with design points represented in logit space and lying on hyperbolae in that space. Algebraic results proved somewhat elusive and just two tentative propositions are given. To counter this, a taxonomy of designs, obtained numerically and dictated by the values of the unknown parameters, is also reported. This work forms part of the Ph.D. thesis of Kabera (2009) and is joint with Gaetan Kabera of the Medical Research Council of South Africa and Prince Ndlovu of the University of South Africa. References Jia Y. and Myers R.H. (2001). “Optimal Experimental Designs for Twovariable Logistic Regression Models.” Technical Report, Department of Statistics, VPI & SU, Backsberg, Virginia. Kabera M.G. (2009). “Doptimal Designs for Drug Synergy.” Ph.D. thesis, University of KwaZuluNatal. Kupchak P.I. (2000). “Optimal Designs for the Detection of Drug Interaction.” Ph.D. thesis, University of Toronto.
20110901T14:58:45+01:00
1563
1168717
true
16x9
false
no

Design and analysis of biodiversity experiments
ucs_sms_125_1177970
http://sms.cam.ac.uk/media/1177970
Design and analysis of biodiversity experiments
Bailey, R (University of London)
Monday 26 September 2011, 14:1014:20
Tue, 04 Oct 2011 09:15:28 +0100
Isaac Newton Institute
Bailey, R
4f6f45877358bc803182e70ec9bfb2b5
6004dba0c95e71159e21c9ab8d46932a
764ae7f5869450f89cea93e962ae02b0
ea63ae87d8f7164a6a0b1eff5a0f4784
6067f6b6adac869b733dcf6db5be89ce
Bailey, R (University of London)
Monday 26 September 2011, 14:1014:20
Bailey, R (University of London)
Monday 26 September 2011, 14:1014:20
Cambridge University
659
http://sms.cam.ac.uk/media/1177970
Design and analysis of biodiversity experiments
Bailey, R (University of London)
Monday 26 September 2011, 14:1014:20
Colleagues in ecology designed an experiment to see whether various favourable responses were affected by the number of different species present in the ecosystem, keeping the total number of organisms constant.
I thought that their data were better explained by a model that was more obvious to me. I will describe the experiment, the family of models we discussed, the conclusion from the data analysis, and the design of subsequent studies.
20111004T09:15:38+01:00
659
1177970
true
16x9
false
no

Design and analysis of experiments applied to critical infrastructure simulation
ucs_sms_125_1497
http://sms.cam.ac.uk/media/1497
Design and analysis of experiments applied to critical infrastructure simulation
Moore, LM (Los Alamos National Laboratory)
Thursday 14 August 2008, 17:0017:30
Tue, 16 Sep 2008 14:20:42 +0100
Moore, LM
Isaac Newton Institute
Moore, LM
c63a24943a62c9b8cc3113f4486662ab
58e60dae8a1db9fbc4bfa6ac34fe6d44
254d8d0fa042c5504e415b3a6bec2b98
93e776fd896ddb11e67504864b044920
3b937f3395d4739f50819210e8ebf626
59f17f8e6c654444bb0e8b8fe54d7e1d
1392bb443677a5c33213c2f47a0dfa14
Moore, LM (Los Alamos National Laboratory)
Thursday 14 August 2008, 17:0017:30
Moore, LM (Los Alamos National Laboratory)
Thursday 14 August 2008, 17:0017:30
Cambridge University
1889
http://sms.cam.ac.uk/media/1497
Design and analysis of experiments applied to critical infrastructure simulation
Moore, LM (Los Alamos National Laboratory)
Thursday 14 August 2008, 17:0017:30
Critical infrastructures are a complex "system of systems" and interdependent infrastructure simulation models are useful to assess consequences of disruptions initiated in any infrastructure. A riskinformed decision support tool using systems dynamics methods has been developed at Los Alamos National Laboratory to provide an efficient running simulation tool to gain insight for making critical infrastructure protection related decisions in the presence of uncertainty. Modeling of consequences of an infectious disease outbreak provides a case study and opportunity to demonstrate exploratory statistical experiment planning and analysis capability. In addition to modeling consequences of an incident, alternative mitigation strategies can be implemented and consequences under these alternatives compared. Statistical analyses include screening, sensitivity and uncertainty analysis in addition to designing experiments which are sets of simulation runs for comparing relative consequences from implementation of different mitigation strategies.
http://www.newton.ac.uk/programmes/DOE/seminars/index.html
20130228T12:21:36+00:00
1889
1497
true
4x3
false
no

Design and analysis of variable fidelity multiobjective experiments
ucs_sms_125_1171566
http://sms.cam.ac.uk/media/1171566
Design and analysis of variable fidelity multiobjective experiments
Forrester, A (University of Southampton)
Monday 05 September 2011, 14:3015:00
Tue, 13 Sep 2011 11:50:48 +0100
Forrester, A
Steve Greenham
Isaac Newton Institute
Forrester, A
c3364274e1ad15e8b6c9c0f0a391d0c8
118076a8f27ae9c3fe6dcd0b1c54e731
74d973779e98e451e0d38f4496ed9170
ee4fa4ce9b5490447efb0aaaac9b3511
d0510781bc1c009dfeb821a0f972aba5
Forrester, A (University of Southampton)
Monday 05 September 2011, 14:3015:00
Forrester, A (University of Southampton)
Monday 05 September 2011, 14:3015:00
Cambridge University
1750
http://sms.cam.ac.uk/media/1171566
Design and analysis of variable fidelity multiobjective experiments
Forrester, A (University of Southampton)
Monday 05 September 2011, 14:3015:00
At the core of any design process is the need to predict performance and vary designs accordingly. Performance prediction may come in many forms, from backofenvelope through high fidelity simulations to physical testing. Such experiments may be one or twodimensional simplifications and may include all or some environmental factors. Traditional practice is to increase the fidelity and expense of the experiments as the design progresses, superseding previous lowfidelity results. However, by employing a surrogate modelling approach, all results can contribute to the design process. This talk presents the use of nested space filling experimental designs and a coKriging based multiobjective expected improvement criterion to select pareto optimal solutions. The method is applied to the design of an unmanned air vehicle wing and the rear wing of a racecar.
20110913T11:50:58+01:00
1750
1171566
true
16x9
false
no

Design for Variation
ucs_sms_125_1171548
http://sms.cam.ac.uk/media/1171548
Design for Variation
Reinman, G
Monday 05 September 2011, 14:0014:30
Tue, 13 Sep 2011 11:45:03 +0100
Reinman, G
Steve Greenham
Isaac Newton Institute
Reinman, G
0351e060a4566bbc783233710ac1bb3b
6b4e233a810e62a25b8e6ebf2d48957f
50c61a7965f2c51e26fd6914b225f951
98e7eabbbfb3c00b18a04d2b0d709385
acbd5d3f20c0f2a49015ce8d42ed7ab3
Reinman, G
Monday 05 September 2011, 14:0014:30
Reinman, G
Monday 05 September 2011, 14:0014:30
Cambridge University
1729
http://sms.cam.ac.uk/media/1171548
Design for Variation
Reinman, G
Monday 05 September 2011, 14:0014:30
20110913T11:45:13+01:00
1729
1171548
true
16x9
false
no

Design of clinical trials with multiple end points of different types
ucs_sms_125_1164972
http://sms.cam.ac.uk/media/1164972
Design of clinical trials with multiple end points of different types
Fedorov, V (GSK)
Monday 15 August 2011, 09:0009:45
Wed, 17 Aug 2011 11:23:25 +0100
Fedorov, V
Steve Greenham
Isaac Newton Institute
Fedorov, V
463114581e3430f79ae21dabbea18259
a437a208cd4463fa91924da516fbb518
c016749a5a7a9f77608a264f6fcda27d
cea93b85c798de6174f415a9a17d43b1
21e8aa3817dbc727b49c0e4eb9ff893e
Fedorov, V (GSK)
Monday 15 August 2011, 09:0009:45
Fedorov, V (GSK)
Monday 15 August 2011, 09:0009:45
Cambridge University
3306
http://sms.cam.ac.uk/media/1164972
Design of clinical trials with multiple end points of different types
Fedorov, V (GSK)
Monday 15 August 2011, 09:0009:45
Several correlated endpoints are observed in almost any clinical trial. Typically one of them is claimed as a primary end point and the design (dose allocation and sample size) is driven by a single response model. I discuss the design problem with multiple end points which potentially may be of different nature. For instance, the efficacy end point may be continuous while the toxicity end point may be discrete. I emphasize the necessity to differentiate between the responses and utility functions. The response (end point) functions are what we observe while the utility functions are what should be reported or used in the decision making process. The discussed criteria of optimality are related to the latter and usually describe the precision of their estimators.
20110817T13:24:20+01:00
3306
1164972
true
16x9
false
no

Design of Experiments in Healthcare, doseranging studies, astrophysics and other dangerous things
ucs_sms_125_1166286
http://sms.cam.ac.uk/media/1166286
Design of Experiments in Healthcare, doseranging studies, astrophysics and other dangerous things
Krams, M (Johnson & Johnson)
Tuesday 16 August 2011, 16:4518:00
Mon, 22 Aug 2011 17:57:48 +0100
Krams, M
Steve Greenham
Isaac Newton Institute
Krams, M
554599134606dcc3d6e79da02a3f2040
4f85fc26022f6c15d7a2c648c4a18643
06011a46866af3334cf72da1c488e24d
96a7796d651a0f306fd63ce7e19cc578
1372b1b17859e8c8bf26d79e45f6b169
Krams, M (Johnson & Johnson)
Tuesday 16 August 2011, 16:4518:00
Krams, M (Johnson & Johnson)
Tuesday 16 August 2011, 16:4518:00
Cambridge University
4273
http://sms.cam.ac.uk/media/1166286
Design of Experiments in Healthcare, doseranging studies, astrophysics and other dangerous things
Krams, M (Johnson & Johnson)
Tuesday 16 August 2011, 16:4518:00
Panel discussion of the day's topics including:
 Clinical objectives at different stages of drug development
 Their formulation in terms of design of experiment objectives
 Optimal design for each objective
 Challenges in their implementation
20110831T13:41:48+01:00
4273
1166286
true
16x9
false
no

Design of Networked Experiments
ucs_sms_125_1169642
http://sms.cam.ac.uk/media/1169642
Design of Networked Experiments
Parker, B (QMUL)
Thursday 01 September 2011, 11:3012:00
Mon, 05 Sep 2011 11:10:12 +0100
Parker, B
Steve Greenham
Isaac Newton Institute
Parker, B
5c8d8fee2756dbbee9a032979dead0a0
a1a7aa4f4f5f4cd967dab3b35c9523c7
2297bce7078250da11aaac9c4f5425f0
b4abce7126315fd09449014eb0b177fa
50b25c93dab733fdb4790a6ee85f9654
Parker, B (QMUL)
Thursday 01 September 2011, 11:3012:00
Parker, B (QMUL)
Thursday 01 September 2011, 11:3012:00
Cambridge University
1831
http://sms.cam.ac.uk/media/1169642
Design of Networked Experiments
Parker, B (QMUL)
Thursday 01 September 2011, 11:3012:00
We consider experiments on a number of subjects, and examine how the links between subjects in an experiment, affect the optimal design. For example, in a marketing experiment, it is reasonable to believe that a product may be preferred more by a subject whose 'friend' also prefers that product, and we may wish to use this 'friendship' information to improve our design.
We present optimal designs to measure both the direct effect and the network effect. We discuss how the structure of the network has a large influence on the optimal design, but show that even if we know many properties of the network, as represented by the eigenvalues of a graph, we cannot determine an absolute design.
We present examples based on marketing experiments, and show how the results can be applied to experiments in social sciences and elsewhere.
20110905T11:10:23+01:00
1831
1169642
true
16x9
false
no

Design of twophase experiments
ucs_sms_125_1453
http://sms.cam.ac.uk/media/1453
Design of twophase experiments
Bailey, RA (QMUL)
Wednesday 13 August 2008, 11:3012:30
Fri, 12 Sep 2008 06:59:06 +0100
Bailey, RA
Steve Greenham
Isaac Newton Institute
Bailey, RA
2eba9a53cf6919c259423a3e62b80bc4
b8efc9e1c7abd5e82e43990e5e33ec57
6e8222e5a4514850d502ceb3e81e7513
b14c74a3dba6feda31025b8050ef16c0
91485865424c2875b1e7f50990060087
8f32424c4586328ec8e9352203f028da
8fe650c606a233898b967aae69562ee7
Bailey, RA (QMUL)
Wednesday 13 August 2008, 11:3012:30
Bailey, RA (QMUL)
Wednesday 13 August 2008, 11:3012:30
Cambridge University
3867
http://sms.cam.ac.uk/media/1453
Design of twophase experiments
Bailey, RA (QMUL)
Wednesday 13 August 2008, 11:3012:30
In a twophase experiment, treatments are allocated to experimental units in the first phase, and the products from those experimental units are allocated to a second sort of experimental unit in the second phase. The appropriate data analysis (and therefore the quality of the overall design) depends on the designs used for the two phases and on how they fit together. Usually we want to estimate the most important contrasts with low variance and with a large number of degrees of freedom for the appropriate residual. In a twophase experiment, these criteria may conflict. I will discuss some of the issues to think about when designing such experiments, and show how sometimes Patterson's design key can help.
http://www.newton.ac.uk/programmes/DOE/seminars/index.html
20130228T12:21:20+00:00
3867
1453
true
4x3
false
no

Designed Biofluid Mixtures Allow FeatureWise Evaluation Of Metabolic Profiling Analytical Platforms
ucs_sms_125_1168628
http://sms.cam.ac.uk/media/1168628
Designed Biofluid Mixtures Allow FeatureWise Evaluation Of Metabolic Profiling Analytical Platforms
Athersuch, T (Imperial College London)
Tuesday 30 August 2011, 17:0017:30
Thu, 01 Sep 2011 14:16:31 +0100
Athersuch, T
Steve Greenham
Isaac Newton Institute
Athersuch, T
7ece914bc3e0d7aa81d46b42b7757a24
138ad4b7561b87d0cd77a16c33d70f44
872021a840f61ce2b038e97bb5577df6
e63592d3fb8975ad2aff983e8606e0af
6d0b81fd7c9fc52d30b6b8b0760d065d
Athersuch, T (Imperial College London)
Tuesday 30 August 2011, 17:0017:30
Athersuch, T (Imperial College London)
Tuesday 30 August 2011, 17:0017:30
Cambridge University
2040
http://sms.cam.ac.uk/media/1168628
Designed Biofluid Mixtures Allow FeatureWise Evaluation Of Metabolic Profiling Analytical Platforms
Athersuch, T (Imperial College London)
Tuesday 30 August 2011, 17:0017:30
The development of spectral analysis platforms for targeted metabolic profiling may help streamline quantification and will undoubtedly facilitate biological interpretation of metabolomics/metabonomics datasets. A general method for evaluating the performance, coverage and applicability of analytical methods in metabolic profiling is much needed to aid biomarker assessment.
The substantial variation in spectral and compositional background that exist in samples generated by real biofluid studies are often not capture by traditional evaluations of analytical performance that use compounds addition (spikes). Such approaches may therefore underestimate the contribution of matrix effects to the measurement of major metabolites and confound analysis.
We illustrate how a strategy of mixing intact biofluids in a predetermined experimental design can be used to evaluate, compare and optimise the performance of quantitative spectral analysis tools in conditions that better approximate a real metabolic profiling experiment.
Results of preliminary experiments on two commonlyused profiling platforms (highresolution 1D 1H nuclear magnetic resonance (NMR) spectroscopy and ultra high performance liquid chromatographymass spectrometry (UPLCMS)) are discussed. Use of multivariate regression allowed featurewise statistics to be generated as a summary of the overall performance of each platform.
The use of designed biofluid mixtures as a basis of evaluating the featurewise variation in instrument response provides a rational basis for exploiting information from several samples simultaneously, in contrast to spectral deconvolution, which is typically applied to one spectrum at a time.
20110901T14:16:41+01:00
2040
1168628
true
16x9
false
no

Designing an adaptive trial with treatment selection and a survival endpoint
ucs_sms_125_2024196
http://sms.cam.ac.uk/media/2024196
Designing an adaptive trial with treatment selection and a survival endpoint
Jennison, C (University of Bath)
Monday 6th July 2015, 10:00  10:45
Wed, 08 Jul 2015 15:12:57 +0100
Isaac Newton Institute
Jennison, C
9a2806ab1270983086a38bf2d1df1fa0
869b2e447b58de3c789920c37a1d7f8e
a277cccf8bf1417446f015bf33b3c169
Jennison, C (University of Bath)
Monday 6th July 2015, 10:00  10:45
Jennison, C (University of Bath)
Monday 6th July 2015, 10:00  10:45
Cambridge University
3403
http://sms.cam.ac.uk/media/2024196
Designing an adaptive trial with treatment selection and a survival endpoint
Jennison, C (University of Bath)
Monday 6th July 2015, 10:00  10:45
We consider a clinical trial in which two versions of a new treatment are compared against control with the primary endpoint of overall survival. At an interim analysis, midway through the trial, one of the two treatments is selected, based on the short term response of progressionfree survival. For such an adaptive design the familywise type I error rate can be protected by use of a closed testing procedure to deal with the two null hypotheses and combination tests to combine data from before and after the interim analysis. However, with the primary endpoint of overall survival, there is still a danger of inflating the type I error rate: we present a way of applying the combination test that solves this problem simply and effectively. With the methodology in place, we then assess the potential benefits of treatment selection in this adaptive trial design.
20150708T15:12:57+01:00
3403
2024196
true
16x9
false
no

Designing experiments for an application in laser and surface chemistry
ucs_sms_125_1466
http://sms.cam.ac.uk/media/1466
Designing experiments for an application in laser and surface chemistry
Biedermann, SGM (Southampton)
Thursday 14 August 2008, 10:3011:00
Mon, 15 Sep 2008 08:14:21 +0100
Biedermann, SGM
Isaac Newton Institute
Biedermann, SGM
671c6e55782d8e9ded7ec35278efa7f2
6e14bcecdb0466d0fef75c93907b5f44
a105ef2fc015b5d02921ad47f7e6e3e2
abbc95f51a539da6012a7b41570f636d
27c19c96c219b1c28b16470f37f4c614
5745f7cfc3329128db80029b57614346
4b4983d608bac8d87723095f1178d6e4
Biedermann, SGM (Southampton)
Thursday 14 August 2008, 10:3011:00
Biedermann, SGM (Southampton)
Thursday 14 August 2008, 10:3011:00
Cambridge University
1735
http://sms.cam.ac.uk/media/1466
Designing experiments for an application in laser and surface chemistry
Biedermann, SGM (Southampton)
Thursday 14 August 2008, 10:3011:00
Second harmonic generation (SHG) experiments are widely used in Chemistry to investigate the behaviour of interfaces between two phases. We discuss issues arising in planning SHG experiments at the air/liquid interface in order to obtain maximal precision in the subsequent data analysis. An interesting feature of such models is that the unknown model parameters are complex. We provide designs that are optimal for estimating these parameters and discuss robustness issues arising from the nonlinearity of the model.
http://www.newton.ac.uk/programmes/DOE/seminars/index.html
20130228T12:21:08+00:00
1735
1466
true
4x3
false
no

Designs and models for Phase I oncology trials with intrapatient dose escalation
ucs_sms_125_1166777
http://sms.cam.ac.uk/media/1166777
Designs and models for Phase I oncology trials with intrapatient dose escalation
Taylor, J (Michigan)
Friday 19 August 2011, 11:4512:30
Wed, 24 Aug 2011 13:00:35 +0100
Isaac Newton Institute
Taylor, J
5e2477fcd6199b8cfd6bae47035e6eac
043b2ff774327ae240f3510e994ecb18
5374b9107f0380f017edeb4e1745fff8
d0a2847689ee82b00e1b04acb5780428
8b78050a4e05bfdf0875b120f7271a3e
Taylor, J (Michigan)
Friday 19 August 2011, 11:4512:30
Taylor, J (Michigan)
Friday 19 August 2011, 11:4512:30
Cambridge University
3101
http://sms.cam.ac.uk/media/1166777
Designs and models for Phase I oncology trials with intrapatient dose escalation
Taylor, J (Michigan)
Friday 19 August 2011, 11:4512:30
20110824T13:00:45+01:00
3101
1166777
true
16x9
false
no

Designs for dependent observations
ucs_sms_125_2028374
http://sms.cam.ac.uk/media/2028374
Designs for dependent observations
Zhigljavsky, A (Cardiff University)
Friday 10th July 2015, 14:15  15:00
Tue, 14 Jul 2015 17:38:13 +0100
Isaac Newton Institute
Zhigljavsky, A
4a958a40a6713a23edb651b23618caf1
45a875430f8d14e19d02ac32549e7eb0
379c0832cf9792d21d74c13ba35e57a4
73900ee014f1a3e3eca612e61136f168
Zhigljavsky, A (Cardiff University)
Friday 10th July 2015, 14:15  15:00
Zhigljavsky, A (Cardiff University)
Friday 10th July 2015, 14:15  15:00
Cambridge University
2500
http://sms.cam.ac.uk/media/2028374
Designs for dependent observations
Zhigljavsky, A (Cardiff University)
Friday 10th July 2015, 14:15  15:00
We consider the problem of optimal experimental design for the regression models on an interval, where the observations are correlated and the errors come from either Markov or conditionally Markov process. We study transformations of these regression models and corresponding designs. We show, in particular, that in many cases we can assume that the underlying model of errors is the Brownian motion.
This is a joint work with H. Dette and A. Pepelyshev.
20150714T17:38:13+01:00
2500
2028374
true
16x9
false
no

Designs for doseescalation trials
ucs_sms_125_2027317
http://sms.cam.ac.uk/media/2027317
Designs for doseescalation trials
Bailey, R (University of St Andrews)
Wednesday 8th July 2015, 12:00  12:30
Mon, 13 Jul 2015 12:42:09 +0100
Isaac Newton Institute
Bailey, R
fe3ec9eed859fa4b7f079040581b0e94
cee933ce4c24e181617c9a697a1fb957
38e18b46bfe0ad6bdb29c128e2c362d5
a0c166bd50743c56c7151c56725791d3
Bailey, R (University of St Andrews)
Wednesday 8th July 2015, 12:00  12:30
Bailey, R (University of St Andrews)
Wednesday 8th July 2015, 12:00  12:30
Cambridge University
1951
http://sms.cam.ac.uk/media/2027317
Designs for doseescalation trials
Bailey, R (University of St Andrews)
Wednesday 8th July 2015, 12:00  12:30
For FirstinHuman trials of a new drug, healthy volunteers are recruited in cohorts. For safety reasons, only the lowest dose and placebo may be used in the first cohort, and no new planned dose may be used until the one immediately below has been used in a previous cohort. How should doses be allocated to cohorts?
20150713T12:42:09+01:00
1951
2027317
true
16x9
false
no

Designs for generalized linear models with random block effects
ucs_sms_125_2025487
http://sms.cam.ac.uk/media/2025487
Designs for generalized linear models with random block effects
Waite, T (University of Southampton)
Tuesday 7th July 2015, 14:00  14:30
Fri, 10 Jul 2015 13:05:06 +0100
Isaac Newton Institute
Waite, T
cd938c28e994f9a0deca95e124472158
ecc3fa06086741fa649d02f730abef0e
e00d32181fd9eb44e2972e2b24a798f1
24d4eb00858345608f829fd60a33e508
Waite, T (University of Southampton)
Tuesday 7th July 2015, 14:00  14:30
Waite, T (University of Southampton)
Tuesday 7th July 2015, 14:00  14:30
Cambridge University
2141
http://sms.cam.ac.uk/media/2025487
Designs for generalized linear models with random block effects
Waite, T (University of Southampton)
Tuesday 7th July 2015, 14:00  14:30
For an experiment measuring independent discrete responses, a generalized linear model, such as the logistic or loglinear, is typically used to analyse the data. In blocked experiments, where observations from the same block are potentially correlated, it may be appropriate to include random effects in the predictor, thus producing a generalized linear mixed model. Selecting optimal designs for such models is complicated by the fact that the Fisher information matrix, on which most optimality criteria are based, is computationally expensive to evaluate. In addition, the dependence of the information matrix on the unknown values of the parameters must be overcome by, for example, use of a pseudoBayesian approach. For the first time, we evaluate the efficiency, for estimating conditional models, of optimal designs from closedform approximations to the information matrix, derived from marginal quasilikelihood and generalized estimating equations. It is found that, for binaryresponse models, naive application of these approximations may result in inefficient designs. However, a simple correction for the marginal attenuation of parameters yields much improved designs when the intrablock dependence is moderate. For stronger intrablock dependence, our adjusted marginal modelling approximations are sometimes less effective. Here, more efficient designs may be obtained from a novel asymptotic approximation. The use of approximations from this suite reduces the computational burden of design search substantially, enabling straightforward selection of multifactor designs.
20150710T13:05:06+01:00
2141
2025487
true
16x9
false
no

Designs for mixed models with binary response
ucs_sms_125_1162680
http://sms.cam.ac.uk/media/1162680
Designs for mixed models with binary response
Waite, T (Southampton)
Tuesday 09 August 2011, 16:0016:45
Thu, 11 Aug 2011 14:54:03 +0100
Waite, T
Steve Greenham
Isaac Newton Institute
Waite, T
ace7c82a3dd286514d29acb7fd1cab06
da896006304042674436ae4edb8dcb36
777299ed09d646073241e59984e5778a
2a91db520f405f3f5db351036734a524
96bd48df7874b71818635d5ef9ac0d45
Waite, T (Southampton)
Tuesday 09 August 2011, 16:0016:45
Waite, T (Southampton)
Tuesday 09 August 2011, 16:0016:45
Cambridge University
2557
http://sms.cam.ac.uk/media/1162680
Designs for mixed models with binary response
Waite, T (Southampton)
Tuesday 09 August 2011, 16:0016:45
For an experiment measuring a binary response, a generalized linear model such as the logistic or probit is typically used to model the data. However these models assume that the responses are independent. In blocked experiments, where responses in the same block are potentially correlated, it may be appropriate to include random effects in the predictor, thus producing a generalized linear mixed model (GLMM). Obtaining designs for such models is complicated by the fact that the information matrix, on which most optimality criteria are based, is computationally expensive to evaluate (indeed if one computes naively, the search for an optimal design is likely to take several months). When analyzing GLMMs, it is common to use analytical approximations such as marginal quasilikelihood (MQL) and penalized quasilikelihood (PQL) in place of full maximum likelihood. In this talk, we consider the use of such computationally cheap approximations as surrogates for the true information matrix when producing designs. This reduces the computational burden substantially, and enables us to obtain designs within a much shorter time frame. However, other issues also need to be considered such as the accuracy of the approximations and the dependence of the optimal design on the unknown values of the parameters. In particular, we evaluate the effectiveness of designs found using these analytic approximations through comparison to designs that are found using a more computationally expensive numerical approximation to the likelihood.
20110811T14:54:13+01:00
2557
1162680
true
16x9
false
no

Dimensional Analysis in Experimental Design
ucs_sms_125_1191742
http://sms.cam.ac.uk/media/1191742
Dimensional Analysis in Experimental Design
Davis, T
Wednesday 30 November 2011, 11:0012:00
Thu, 01 Dec 2011 11:48:07 +0000
Davis, T
Steve Greenham
Isaac Newton Institute
Davis, T
4cf78fd73928068ada176ea108b2c785
48d301649e1679cfcf779738830b73ba
55a9e295ec0cd61486399c9e628f2955
f68cb5dc9abad30d0064900f3808d45c
84f197cde992ae358639271290524b71
Davis, T
Wednesday 30 November 2011, 11:0012:00
Davis, T
Wednesday 30 November 2011, 11:0012:00
Cambridge University
3367
http://sms.cam.ac.uk/media/1191742
Dimensional Analysis in Experimental Design
Davis, T
Wednesday 30 November 2011, 11:0012:00
In this talk, and since we are in the Isaac Newton Institute, I will focus on using the physics of the problem being tackled to determine a strategy to design an experiment to fit a model for prediction. At the heart of the approach is an application of Edgar Buckingham’s 1914 “Pi” theorem. Buckingham’s result, which is based on dimensional analysis, has been seemingly neglected by statisticians, but it provides a “bridge” between a purely theoretical approach to model building, and an empirical one based on e.g. polynomial approximations such as 2nd order response surfaces. I will illustrate the ideas with a few examples, in the hope that I can show that dimensional analysis should take its place at the heart of experimental design in engineering applications.
20111202T09:40:21+00:00
3367
1191742
true
16x9
false
no

Discrete choice experimental design for alternative specific choice models: an application exploring preferences for drinking water
ucs_sms_125_1166466
http://sms.cam.ac.uk/media/1166466
Discrete choice experimental design for alternative specific choice models: an application exploring preferences for drinking water
Lancsar, E (Newcastle)
Thursday 18 August 2011, 09:0009:45
Tue, 23 Aug 2011 13:06:55 +0100
Isaac Newton Institute
Lancsar, E
7795b55570dc7908b309d2eb73f15094
6782d971076b7e67204ee580113372db
86965ac99ca4858e5e54bce254d80f10
fd482f244c15c7d6a6da9d6a1adad4b9
eae9c223ee117c4e749b5e19046a18e3
Lancsar, E (Newcastle)
Thursday 18 August 2011, 09:0009:45
Lancsar, E (Newcastle)
Thursday 18 August 2011, 09:0009:45
Cambridge University
2569
http://sms.cam.ac.uk/media/1166466
Discrete choice experimental design for alternative specific choice models: an application exploring preferences for drinking water
Lancsar, E (Newcastle)
Thursday 18 August 2011, 09:0009:45
Health economic applications of discrete choice experiments have generally used generic forced choice experimental designs, or to a lesser extent generic designs with an appended status quo or opt out option. Each has implications for the types of indirect utility functions that can be estimated from such designs. Less attention has been paid to allowing for alternative specific choice experiments. This paper focuses on the development and use of an experimental design that allows for both labelled alternatives and alternative specific attribute effects in the context of a best worst choice study designed to investigate preferences for different types of drinking water. Results including testing for alternative specific effects and preferences for different types of drinking water options are presented, with implications explored.
20110823T13:07:05+01:00
2569
1166466
true
16x9
false
no

Discrete Choice Experiments in Health Economics
ucs_sms_125_1166386
http://sms.cam.ac.uk/media/1166386
Discrete Choice Experiments in Health Economics
Ryan, M (Aberdeen)
Wednesday 17 August 2011, 09:4510:30
Tue, 23 Aug 2011 08:45:15 +0100
Isaac Newton Institute
Ryan, M
5c84352313b7448cff69fa25f7c96767
14d6b62b1bf66d825f4929184126f8a1
cd2be5ddf91dc133fd880b7bc8fc5ff1
8ed9028ab4a19b8205a4cd057b54ffe9
916ce966d6eb1c8a8e693481896c70f8
Ryan, M (Aberdeen)
Wednesday 17 August 2011, 09:4510:30
Ryan, M (Aberdeen)
Wednesday 17 August 2011, 09:4510:30
Cambridge University
2700
http://sms.cam.ac.uk/media/1166386
Discrete Choice Experiments in Health Economics
Ryan, M (Aberdeen)
Wednesday 17 August 2011, 09:4510:30
Since their introduction in health economics in the early 1990s, there has been an increased interest in the use of discrete choice experiments (DCEs), both at the applied and methodological level. At the applied level, whilst the technique was introduced into health economics to go beyond narrow definitions of health benefits (Quality Adjusted Life Years, QALYs), and value broader measures of utility (patient experiences/well being), the technique is being applied to address an ever increasing range of policy questions. Methodologically developments have also been made with respect to methods for developing attributes and levels, techniques for defining choice sets to present to individuals (experimental design) and methods for analysis of response data. This talk considers the journey of DCEs in health economics, discussing both where we are, and where we should go.
20110823T08:45:24+01:00
2700
1166386
true
16x9
false
no

Discussion of three talks on (covariate) adaptive designs
ucs_sms_125_1166718
http://sms.cam.ac.uk/media/1166718
Discussion of three talks on (covariate) adaptive designs
Mukherjee, B (Michigan)
Thursday 18 August 2011, 16:4517:30
Wed, 24 Aug 2011 10:53:41 +0100
Isaac Newton Institute
Mukherjee, B
b406f561896e0fb63c722d7099c395e8
a76032b38227359520dd654a1481a671
6d8f2702c994c4a6a6882298ae350104
bba63226ecc8f340e4a3a61adb9033cf
4079f62163295f6319de8ab2cb6735e9
Mukherjee, B (Michigan)
Thursday 18 August 2011, 16:4517:30
Mukherjee, B (Michigan)
Thursday 18 August 2011, 16:4517:30
Cambridge University
2602
http://sms.cam.ac.uk/media/1166718
Discussion of three talks on (covariate) adaptive designs
Mukherjee, B (Michigan)
Thursday 18 August 2011, 16:4517:30
20110824T10:53:50+01:00
2602
1166718
true
16x9
false
no

Distributed Federation of Multiparadigm Simulations and Decision Models for Planning and Control
ucs_sms_125_1171993
http://sms.cam.ac.uk/media/1171993
Distributed Federation of Multiparadigm Simulations and Decision Models for Planning and Control
Son, YJ (University of Arizona)
Wednesday 07 September 2011, 09:4010:00
Wed, 14 Sep 2011 13:38:23 +0100
Son, YJ
Steve Greenham
Isaac Newton Institute
Son, YJ
9f77cd5c8afa64381f7a060cbbcee45b
4159b85a8ec7c40e850bd20a7ecbe991
681b0441c007bba24567cb08bf6fa0d9
3c34002b39c86f3909a0d09ea8b7ffd4
7d502194256311720dcc068b4a9dbb6a
Son, YJ (University of Arizona)
Wednesday 07 September 2011, 09:4010:00
Son, YJ (University of Arizona)
Wednesday 07 September 2011, 09:4010:00
Cambridge University
2322
http://sms.cam.ac.uk/media/1171993
Distributed Federation of Multiparadigm Simulations and Decision Models for Planning and Control
Son, YJ (University of Arizona)
Wednesday 07 September 2011, 09:4010:00
In this talk, we first discuss simulationbased shop floor planning and control, where 1) online simulation is used to evaluate decision alternatives at the planning stage, 2) the same simulation model (executing in the fast mode) used at the planning stage is used as a realtime task generator (realtime simulation) during the control stage, and 3) the realtime simulation drives the manufacturing system by sending and receiving messages to an executor. We then discuss how simulationbased shop floor planning and control can be extended to enterprise level activities (top floor). To this end, we discuss the analogies between the shop floor and top floor in terms of the components required to construct simulationbased planning and control systems such as resource models, coordination models, physical entities, and simulation models. Differences between them are also discussed in order to identify new challenges that we face for top floor planning and control. A major difference is the way a simulation model is constructed so that it can be used for planning, depending on whether time synchronization among member simulations becomes an issue or not. We also discuss the distributed computing platform using web services and grid computing technologies, which allow us to integrate simulation and decision models, and software and hardware components. Finally, we discuss DDDAMS (Dynamic DataDriven Adaptive MultiScale Simulation) framework, where the aim is to augment the validity of simulation models in the most economic way via incorporating dynamic data into the executing model and the executing model's steering the measurement process for selective data update.
20110914T13:38:33+01:00
2322
1171993
true
16x9
false
no

DoE in the Automotive Industry  Approaching the Limits of Current Methods?
ucs_sms_125_1191759
http://sms.cam.ac.uk/media/1191759
DoE in the Automotive Industry  Approaching the Limits of Current Methods?
Seabrook, J
Wednesday 30 November 2011, 12:0012:30
Thu, 01 Dec 2011 11:54:03 +0000
Seabrook, J
Steve Greenham
Isaac Newton Institute
Seabrook, J
4f3dbf12bd55a9ac28de6077dc32196d
2eae627deece3724e33cd3ddecc74ca3
ca1c48271c9763c226256d509ec6c0f0
85b0889ac4570c9a26fe0c10d4a0ecce
2a0ad4017e0fe7ea3e8ee9507e1ba9fb
Seabrook, J
Wednesday 30 November 2011, 12:0012:30
Seabrook, J
Wednesday 30 November 2011, 12:0012:30
Cambridge University
1873
http://sms.cam.ac.uk/media/1191759
DoE in the Automotive Industry  Approaching the Limits of Current Methods?
Seabrook, J
Wednesday 30 November 2011, 12:0012:30
The presentation outlines the main applications of DoE in the field of automotive engine development and calibration. DoE has been applied to engine calibration (optimising the settings of electronicallycontrolled engine parameters for low emissions and fuel consumption) for many years. The task has become significantly more complex in recent years due to the various new fuel injection technologies, and up to ten variables must be calibrated at each and every engine speed and load. Many engine responses are nonlinear and there are considerable interactions between control variables so the conservative approach of separate DoEs at multiple speedload conditions still predominates. Polynomials are adequate for such "local" models with narrow variable ranges and six or fewer variables. But over wider ranges or when speed and load are included (socalled "global" models) responses are highly nonlinear and polynomials are unsuitable. Some practitioners use radial basis functions, neural networks or stochastic process models but such methods do not always yield the requisite accuracy for "global" models. Furthermore, the most reliable of these techniques, stochastic process models, are limited by computational considerations when datasets are large. The overview of the current "state of the art" methods is presented with the aim of stimulating discussion on what mathematical methods could form the basis of future DoE tools for the automotive industry.
20111202T09:44:13+00:00
1873
1191759
true
16x9
false
no

Dose Escalation using a Bayesian Model: rational decision rules
ucs_sms_125_1166218
http://sms.cam.ac.uk/media/1166218
Dose Escalation using a Bayesian Model: rational decision rules
Thygesen, H (Lancaster)
Tuesday 16 August 2011, 14:3015:00
Mon, 22 Aug 2011 15:41:03 +0100
Thygesen, H
Steve Greenham
Isaac Newton Institute
Thygesen, H
04c6883e64fe015e200bef9c4f725ee6
fd3db2e25682ed5bae17e59b4259a7ee
da6e3b0232e97a04029083a0fdb59f03
73c2a9c55ae00f0beeb529f405e42191
a2178c3269d31f4bdc3a961dd7ea957e
Thygesen, H (Lancaster)
Tuesday 16 August 2011, 14:3015:00
Thygesen, H (Lancaster)
Tuesday 16 August 2011, 14:3015:00
Cambridge University
1475
http://sms.cam.ac.uk/media/1166218
Dose Escalation using a Bayesian Model: rational decision rules
Thygesen, H (Lancaster)
Tuesday 16 August 2011, 14:3015:00
In dose escalation studies, the potential ethical costs of administering high doses must be weighted against the added utility from gaining safety information about high (and potentially effective doses). This is the rationale for starting with low doses while the confidence in the safety of the drug is low, and escalating to higher doses as confidence grow. A decision theoretical framework is proposed.
20110831T13:42:16+01:00
1475
1166218
true
16x9
false
no

Dose Selection Incorporating PK/PD Information in Early Phase Clinical Trials
ucs_sms_125_1165716
http://sms.cam.ac.uk/media/1165716
Dose Selection Incorporating PK/PD Information in Early Phase Clinical Trials
Bogachka, B (QMUL)
Monday 15 August 2011, 16:3517:15
Thu, 18 Aug 2011 08:23:25 +0100
Bogachka, B
Steve Greenham
Isaac Newton Institute
Bogachka, B
6deca8a228332262f8db670d1a9a6846
1150a986519306e2bb82447e8f62f88a
1ac54546031c65e402989aecd8f6f418
657abfc785366de628e6320aa242227a
4a5f832cfdb55ff23912f0a42e7e3869
69c957db69b916bcbe28bd8a192f4d70
03056d9f9068e4e929eef88ffb1d09f7
Bogachka, B (QMUL)
Monday 15 August 2011, 16:3517:15
Bogachka, B (QMUL)
Monday 15 August 2011, 16:3517:15
Cambridge University
2461
http://sms.cam.ac.uk/media/1165716
Dose Selection Incorporating PK/PD Information in Early Phase Clinical Trials
Bogachka, B (QMUL)
Monday 15 August 2011, 16:3517:15
Early phase clinical trials generate information on pharmacokinetic parameters and on safety issues. In addition, a dose level, or a set of dose levels, needs to be selected for further examination in later phases. If patients, rather than healthy volunteers, take part in the early phase, it may be possible to observe the effects of the drug on the disease. In the presentation we will discuss some statistical, ethical and economic aspects of designing optimum adaptive clinical trials for dose selection incorporating both pharmacokinetic and pharmacodynamic endpoints.
20110818T08:23:34+01:00
2461
1165716
true
4x3
false
no

Efficiency, optimality, and differential treatment interest
ucs_sms_125_1514
http://sms.cam.ac.uk/media/1514
Efficiency, optimality, and differential treatment interest
Morgan, JP (Virginia Tech)
Friday 15 August 2008, 17:0017:30
Wed, 17 Sep 2008 17:19:21 +0100
Morgan, JP
Isaac Newton Institute
Morgan, JP
0880c2f0a199f78033ce41d0621b2516
8ddf5c82454be974eeaf01013dcd96fc
01266f05295605f8359d6b8a0fcc3277
e57fd12ac4d1cbe04633b7732810ac40
83c600ad6dbe71d15e72cece4fd5c21f
4721bfc84f6e57cacaabe3aa37e5d3ea
7c8710a71ce737ef3afaef5b7e20bec0
Morgan, JP (Virginia Tech)
Friday 15 August 2008, 17:0017:30
Morgan, JP (Virginia Tech)
Friday 15 August 2008, 17:0017:30
Cambridge University
2289
http://sms.cam.ac.uk/media/1514
Efficiency, optimality, and differential treatment interest
Morgan, JP (Virginia Tech)
Friday 15 August 2008, 17:0017:30
Standard optimality arguments for designed experiments rest on the assumption that all treatments are of equal interest. One exception is found in the "test treatment versus control" literature, where the control is allocated special status. Optimality work there has focused on all pairwise comparisons with the control, making no explicit account of how well test treatments are compared to one another. In many applications it would be preferable to choose a design depending on the relative importance placed on contrasts involving the control to those of test treatments only. This is an example of where a weighted optimality approach can better reflect experimenter goals. When evaluating designs for comparing $v$ treatments, weights $w_1,\ldots,w_v$ ($\sum_iw_i=1$) can be assigned to account for differential treatment interest. These weights enter the evaluation through optimality measures, leading to, for example, weighted versions of the popular A, E, and MV measures of design efficacy. Families of weightedoptimal designs have been identified for both blocked and unblocked experiments. The theory for weighted optimality leads quite naturally to the notion of weightbalanced designs. Weighted balance and partial balance incorporate the concepts of efficiency balance and its generalizations that have been built on the foundation laid by Jones (1959, JRSSB 21, 172179). These balance ideas are closely tied to the weighted E criterion.
http://www.newton.ac.uk/programmes/DOE/seminars/index.html
20130228T12:33:01+00:00
2289
1514
true
4x3
false
no

Efficient blocked designs allowing for pure error estimation
ucs_sms_125_1452
http://sms.cam.ac.uk/media/1452
Efficient blocked designs allowing for pure error estimation
Trinca, L (UNESP)
Tuesday 12 August 2008, 10:3011:00
Thu, 11 Sep 2008 14:16:13 +0100
Trinca, L
Isaac Newton Institute
Trinca, L
4609c39b2ad264fccb342cc6076ed581
7d5cd3ccb9d1194c7a2a9494d0b25838
f2879d2bdf1951e2843e62dd97d417cd
7c0051e9204950acc1951074d9d8b66f
eb62b1581419c1bc4dfb627d2bf611cb
4c07d9572b38388b6655f429863d088f
a294bb262a40208be05c1b8751d08da1
Trinca, L (UNESP)
Tuesday 12 August 2008, 10:3011:00
Trinca, L (UNESP)
Tuesday 12 August 2008, 10:3011:00
Cambridge University
2089
http://sms.cam.ac.uk/media/1452
Efficient blocked designs allowing for pure error estimation
Trinca, L (UNESP)
Tuesday 12 August 2008, 10:3011:00
In this talk we reconsider the problem of designing response surface experiments on process with high variation. We propose some alternative criteria for efficiently blocking a fixed treatment set that have the flexibility of allowing for pure error estimation. Such criteria are compound design criteria that focus on jointly parameter estimation through confidence region or individual parameter estimation through confidence interval. Several examples will illustrate the idea.
http://www.newton.ac.uk/programmes/DOE/seminars/index.html
20130228T12:32:50+00:00
2089
1452
true
4x3
false
no

Emulating complex codes: The implications of using separable covariance functions
ucs_sms_125_1171639
http://sms.cam.ac.uk/media/1171639
Emulating complex codes: The implications of using separable covariance functions
Rougier, J (University of Bristol)
Monday 05 September 2011, 16:3017:00
Tue, 13 Sep 2011 13:07:29 +0100
Rougier, J
Steve Greenham
Isaac Newton Institute
Rougier, J
613f6990c4165220cfafdeb54604290a
9a2b4b5d0f2971b77cc1851c880bf98a
b2d89aac247ec55f247e3d386f2fc341
fdebc307aead98902564f09450b3a8a6
24473885457537fcd7a9a758c04d51aa
Rougier, J (University of Bristol)
Monday 05 September 2011, 16:3017:00
Rougier, J (University of Bristol)
Monday 05 September 2011, 16:3017:00
Cambridge University
1946
http://sms.cam.ac.uk/media/1171639
Emulating complex codes: The implications of using separable covariance functions
Rougier, J (University of Bristol)
Monday 05 September 2011, 16:3017:00
Emulators are crucial in experiments where the computer code is sufficiently expensive that the ensemble of runs cannot span the parameter space. In this case they allow the ensemble to be augmented with additional judgements concerning smoothness and monotonicity. The emulator can then replace the code in inferential calculations, but in my experience a more important role for emulators is in trapping code errors.
The theory of emulation is based around the construction of a stochastic processes prior, which is then updated by conditioning on the runs in the ensemble. Almost invariably, this prior contains a component with a separable covariance function. This talk considers exactly what this separability implies for the nature of the underlying function. The strong conclusion is that processes with separable covariance functions are secondorder equivalent to the product of secondorder uncorrelated processes.
This is an alarmingly strong prior judgement about the computer code, ruling out interactions. But, like the property of stationarity, it does not survive the conditioning process. The cautionary response is to include several regression terms in the emulator prior.
20110913T13:07:39+01:00
1946
1171639
true
16x9
false
no

Emulating the Nonlinear Matter Power Spectrum for the Universe
ucs_sms_125_1171528
http://sms.cam.ac.uk/media/1171528
Emulating the Nonlinear Matter Power Spectrum for the Universe
Higdon, D
Monday 05 September 2011, 11:3011:55
Tue, 13 Sep 2011 11:32:02 +0100
Higdon, D
Steve Greenham
Isaac Newton Institute
Higdon, D
348f3c516d20b56c0019ed6039acf424
9afa144a60e5d5d7f9f18519f848ff7a
b059f1aa380d66b35dbfb5eb38d10da4
e03bffbb7e4abebaf623ae269b99c4ce
42f1a98803c9e6684456558565a67aa9
Higdon, D
Monday 05 September 2011, 11:3011:55
Higdon, D
Monday 05 September 2011, 11:3011:55
Cambridge University
2318
http://sms.cam.ac.uk/media/1171528
Emulating the Nonlinear Matter Power Spectrum for the Universe
Higdon, D
Monday 05 September 2011, 11:3011:55
20110913T11:32:12+01:00
2318
1171528
true
16x9
false
no

EngineeringDriven Statistical Adjustment and Calibration
ucs_sms_125_1168580
http://sms.cam.ac.uk/media/1168580
EngineeringDriven Statistical Adjustment and Calibration
Vengazhiyil, RJ (Georgia Tech)
Tuesday 30 August 2011, 15:0015:30
Thu, 01 Sep 2011 13:57:29 +0100
Isaac Newton Institute
Vengazhiyil, RJ
c00aad0d8db25a135ee953a8252dca31
fcfd9cd6e1a327b8f30a4ae90743c7a0
13f2a44718cb3d968c3e89575b079613
e11c7a88e5a1d12152bee090c44a4b8a
4081ef6c7f94cacd4396fd3c57ab7ba6
Vengazhiyil, RJ (Georgia Tech)
Tuesday 30 August 2011, 15:0015:30
Vengazhiyil, RJ (Georgia Tech)
Tuesday 30 August 2011, 15:0015:30
Cambridge University
1680
http://sms.cam.ac.uk/media/1168580
EngineeringDriven Statistical Adjustment and Calibration
Vengazhiyil, RJ (Georgia Tech)
Tuesday 30 August 2011, 15:0015:30
There can be discrepancy between physicsbased models and reality, which can be reduced by statistically adjusting and calibrating the models using real data. Gaussian process models are commonly used for capturing the bias between the physicsbased model and the truth. Although this is a powerful approach, the resulting adjustment can be quite complex and physically noninterpretable. A different approach is proposed here which is to postulate adjustment models based on the engineering judgment of the observed discrepancy. This often leads to models that are very simple and easy to interpret. The approach will be illustrated using many real case studies.
20110901T13:57:38+01:00
1680
1168580
true
16x9
false
no

Enhanced modelbased experiment design techniques for parameter identification in complex dynamic systems under uncertainty
ucs_sms_125_1157587
http://sms.cam.ac.uk/media/1157587
Enhanced modelbased experiment design techniques for parameter identification in complex dynamic systems under uncertainty
Bezzo, F (Università degli Studi di Padova )
Tuesday 19 July 2011, 10:0011:00
Wed, 20 Jul 2011 18:29:49 +0100
Bezzo, F
Steve Greenham
Isaac Newton Institute
Bezzo, F
5d06c4bec4937d6066d0d7bc845ef3dc
1e166a8e1d239eac550f65baf3e9072d
8bfec8f068f2947232ddcdc26aa4f156
b75bff77e51ce4f2b327f0cd8b159d2a
7b9570b604236e3cff7b7ce54a92a642
Bezzo, F (Università degli Studi di Padova )
Tuesday 19 July 2011, 10:0011:00
Bezzo, F (Università degli Studi di Padova )
Tuesday 19 July 2011, 10:0011:00
Cambridge University
3537
http://sms.cam.ac.uk/media/1157587
Enhanced modelbased experiment design techniques for parameter identification in complex dynamic systems under uncertainty
Bezzo, F (Università degli Studi di Padova )
Tuesday 19 July 2011, 10:0011:00
A wide class of physical systems can be described by dynamic deterministic models expressed in the form of systems of differential and algebraic equations. Once a dynamic model structure is found adequate to represent a physical system, a set of identification experiments needs to be carried out to estimate the set of parameters of the model in the most precise and accurate way. Modelbased design of experiments (MBDoE) techniques represent a valuable tool for the rapid assessment and development of dynamic deterministic models, allowing for the maximisation of the information content of the experiments in order to support and improve the parameter identification task. However, uncertainty in the model parameters or in the model structure itself or in the representation of the experimental facility may lead to design procedures that turn out to be scarcely informative. Additionally, constraints may occur to be violated, thus making the experiment unfeasible or even unsafe. Handling uncertainty is a complex and still open problem, although over the last years significant research effort has been devoted to tackle some issues in this area. Here, some approaches developed at CAPELab at University of Padova will be critically discussed. First Online ModelBased Redesign of Experiment (OMBRE) strategies will be taken into account. In OMBRE the objective is to exploit the information as soon as soon as it is generated by the running experiment. The manipulated input profiles of the running experiment are updated by performing one or more intermediate experiment designs (i.e., redesigns), and each redesign is performed adopting the current value of the parameter set. In addition, a model updating policy including disturbance estimation embedded within an OMBRE strategy (DEOMBRE) can be considered. In the DEOMBRE approach, an augmented model lumping the effect of systematic errors is considered to estimate both the states and the system outputs in a given time frame, updating the constraint conditions in a consistent way as soon as the effect of unknown disturbances propagates in the system. Backoffbased MBDoE, where uncertainty is explicitly accounted for so as to plan a test that is both optimally informative and safe by design, is eventually discussed.
20110720T18:29:58+01:00
3537
1157587
true
16x9
false
no

Enhancing Stochastic Kriging Metamodels with Stochastic Gradient Estimators
ucs_sms_125_1172030
http://sms.cam.ac.uk/media/1172030
Enhancing Stochastic Kriging Metamodels with Stochastic Gradient Estimators
Nelson, B; Ankenman, B (Northwestern University)
Wednesday 07 September 2011, 11:3012:00
Wed, 14 Sep 2011 14:39:38 +0100
Steve Greenham
Isaac Newton Institute
Nelson, B; Ankenman, B
bb7a6110c5c14bc039b570399c179fde
02553366a9b7722f71ab2d442f8d7ed8
88199ccc5060ad5ab098207c2cfd74a8
0e50e1be14be0acaa76ec626b0d310dc
54d84ae28337bba44b7dceafd3a90a0a
Nelson, B; Ankenman, B (Northwestern University)
Wednesday 07 September 2011,...
Nelson, B; Ankenman, B (Northwestern University)
Wednesday 07 September 2011, 11:3012:00
Cambridge University
1827
http://sms.cam.ac.uk/media/1172030
Enhancing Stochastic Kriging Metamodels with Stochastic Gradient Estimators
Nelson, B; Ankenman, B (Northwestern University)
Wednesday 07 September 2011, 11:3012:00
Stochastic kriging is the natural extension of kriging metamodels for the design and analysis of computer experiments to the design and analysis of stochastic simulation experiments where response variance may differ substantially across the design space. In addition to estimating the mean response, it is sometimes possible to obtain an unbiased or consistent estimator of the responsesurface gradient from the same simulation runs. However, like the response itself, the gradient estimator is noisy. In this talk we present methodology for incorporating gradient estimators into response surface prediction via stochastic kriging, evaluate its effectiveness in improving prediction, and specifically consider two gradient estimators: the score function/likelihood ratio method and infinitesimal perturbation analysis.
20110914T14:39:48+01:00
1827
1172030
true
16x9
false
no

Estimating statistical significance of exome sequencing data for rare mendelian disorders using populationwide linkage analysis
ucs_sms_125_1176270
http://sms.cam.ac.uk/media/1176270
Estimating statistical significance of exome sequencing data for rare mendelian disorders using populationwide linkage analysis
Albers, K (Haematology)
Monday 26 September 2011, 10:1010:20
Fri, 30 Sep 2011 15:29:18 +0100
Isaac Newton Institute
Albers, K
96ac70e603f510a8d24a7053b8d4448f
814ce20705fc88bb8d64f9b0762f58c8
7c5f2df4f94786aef4b2c291fed8a85c
32cad53ed1f2d295cf2ecb8d72621709
34e2a989a8c96618f916accdf7bde1dc
Albers, K (Haematology)
Monday 26 September 2011, 10:1010:20
Albers, K (Haematology)
Monday 26 September 2011, 10:1010:20
Cambridge University
698
http://sms.cam.ac.uk/media/1176270
Estimating statistical significance of exome sequencing data for rare mendelian disorders using populationwide linkage analysis
Albers, K (Haematology)
Monday 26 September 2011, 10:1010:20
Exome sequencing of a small number of unrelated affected individuals has proved to be a highly effective approach for identifying causative genes of rare mendelian diseases. A widely used strategy is to consider as candidate causative mutations only those variants that have not been seen previously in other individuals, and those variants predicted to aect protein sequence, e.g. nonsynonymous variants or stopcodons.
For the recessive disorder Gray Platelet Syndrome we identified 7 novel coding mutations in 4 affected individuals, all in different locations in one gene and absent from 994 individuals from the 1000 Genomes project; intuitively a highly significant result (Albers et al. Nat Genet 2011). However, in the case where the candidate causative mutations segregate at low frequency in the general population the significance may be less obvious. This raises a number of questions: what is the statistical significance of such findings in small numbers of affected individuals? If we would assume that the causative mutations are not necessarily in coding sequence, would these results be genomewide significant? Motivated by these issues, we are developing a statistical model based on the idea that
filtering out previously seen variants can be thought of as performing a wholepopulation parametric linkage analysis, whereby the individuals carrying previously seen variants represent the unaffected individuals.
We use the coalescent, a mathematical description of the notion that ultimately all individuals in a population are descendants of a single common ancestor, to model the unknown pedigree shared by the aected individuals and the unaffected individuals. I will discuss implications of population stratification, false positive variant calls and variation in coverage for singleton rates and significance estimates.
20110930T15:29:27+01:00
698
1176270
true
16x9
false
no

Estimating the heterogeneity distribution of willingnesstopay using individualized choice sets
ucs_sms_125_1168734
http://sms.cam.ac.uk/media/1168734
Estimating the heterogeneity distribution of willingnesstopay using individualized choice sets
Vandebroek, M (KU Leuven)
Wednesday 31 August 2011, 16:0016:30
Thu, 01 Sep 2011 15:10:34 +0100
Vandebroek, M
Steve Greenham
Isaac Newton Institute
Vandebroek, M
fe8932551e0a8b13a17ee7a26504743e
d4e19a153437c0c20f58b5119d20b763
98adabd3990c7e4ddefb3c49427ec5db
735ed290626494fbf44c0e0e2b71999d
729cd757eb04fe27baf8411a67ef419e
Vandebroek, M (KU Leuven)
Wednesday 31 August 2011, 16:0016:30
Vandebroek, M (KU Leuven)
Wednesday 31 August 2011, 16:0016:30
Cambridge University
1897
http://sms.cam.ac.uk/media/1168734
Estimating the heterogeneity distribution of willingnesstopay using individualized choice sets
Vandebroek, M (KU Leuven)
Wednesday 31 August 2011, 16:0016:30
Two prominent approaches exist nowadays for estimating the distribution of willingnesstopay (WTP) based on choice experiments. One is to work in the usual preference space in which the random utility model is expressed in terms of partworths. These partworths or utility coefficients are estimated together with their distribution. The WTP and the corresponding heterogeneity distribution of WTP is derived from these results. The other approach reformulates the utility in terms of WTP (called WTPspace) and estimates the WTP and the heterogeneity distribution of WTP directly. Though often used, working in preference space has severe drawbacks as it often leads to WTPdistributions with long flat tails, infinite moments and therefore many extreme values.
By moving to WTPspace, authors have tried to improve the estimation of WTP and its distribution from a modeling perspective. In this paper we will further improve the estimation of individual level WTP and corresponding heterogeneity distribution by designing the choice sets more efficiently. We will generate individual sequential choice designs in WTP space. The use of this sequential approach is motivated by findings of Yu et al. (2011) who show that this approach allows for superior estimation of the utility coefficients and their distribution. The key feature of this approach is that it uses Bayesian methods to generate individually optimized choice sets sequentially based on prior information of each individual which is further updated after each choice made. Based on a simulation study in which we compare the efficiency of this sequential design procedure with several nonsequential choice designs, we can conclude that the sequential approach improves the estimation results substantially.
20110901T15:10:43+01:00
1897
1168734
true
16x9
false
no

Ethical issues posed by cluster randomized trials in health research
ucs_sms_125_1165387
http://sms.cam.ac.uk/media/1165387
Ethical issues posed by cluster randomized trials in health research
Weijer, C (Western Ontario)
Monday 15 August 2011, 17:1517:55
Wed, 17 Aug 2011 14:48:36 +0100
Weijer, C
Isaac Newton Institute
Weijer, C
e51ae25355213c402287c75b728e8d1e
f75c2685c4a906b55e72d46974f10844
648e8321ae966ade477c6ba506d62f90
96c881f2777cec0a009a8a70e6965f6b
db5e067a298560f8765da43efb3f263e
Weijer, C (Western Ontario)
Monday 15 August 2011, 17:1517:55
Weijer, C (Western Ontario)
Monday 15 August 2011, 17:1517:55
Cambridge University
2951
http://sms.cam.ac.uk/media/1165387
Ethical issues posed by cluster randomized trials in health research
Weijer, C (Western Ontario)
Monday 15 August 2011, 17:1517:55
The cluster randomized trial (CRT) is used increasingly in knowledge translation research, quality improvement research, community based intervention studies, public health research, and research in developing countries. While there is a small but growing literature on the subject, ethical issues raised by CRTs require further analysis. CRTs only partly fit within the current paradigm of research ethics. They pose difficult ethical issues for two basic reasons related to their design. First, CRTs involve the randomization of groups rather than individuals, and our understanding of the moral status of groups in incomplete. As a result, the answers to pivotal ethical questions, such as who may speak in behalf of a particular group and on what authority they may do so, are unclear. Second, in CRTs the units of randomization, experimentation, and observation may differ, meaning, for instance, that the group that receives the experimental intervention may not be the same as the group from which data are collected. The implications for the ethics of trials of experimental interventions with (solely) indirect effects on patients and others is not currently well understood. Here I lay out some basic considerations on who is a research subject, from whom one must obtain informed consent, and the use of gatekeepers in CRTs in health research (Trials 2011; 12(1): 100).
20110817T14:48:44+01:00
2951
1165387
true
16x9
false
no

Evaluating Peterborough's no cold calling initiative using spacetime Bayesian hierarchical modelling
ucs_sms_125_1178402
http://sms.cam.ac.uk/media/1178402
Evaluating Peterborough's no cold calling initiative using spacetime Bayesian hierarchical modelling
Haining, B (Geography)
Monday 26 September 2011, 16:4516:55
Tue, 04 Oct 2011 11:32:16 +0100
Isaac Newton Institute
Haining, B
19dd3b0d7a72111ad94e3247ceae0d32
469f0d8b4cb6e95ff2fc8c462d572463
6f05d5eb1d349c2f5718337ea8eba0c7
12e22d54b35180d99342cfa88303fad3
9854c00a41db113e4d0e4d93ca373e23
Haining, B (Geography)
Monday 26 September 2011, 16:4516:55
Haining, B (Geography)
Monday 26 September 2011, 16:4516:55
Cambridge University
1027
http://sms.cam.ac.uk/media/1178402
Evaluating Peterborough's no cold calling initiative using spacetime Bayesian hierarchical modelling
Haining, B (Geography)
Monday 26 September 2011, 16:4516:55
As part of a wider Neighbourhood Policing strategy, Cambridgeshire Constabulary in stituted "No Cold Calling" (NCC) zones to reduce cold calling (unsolicited visits to sell products/services), which is often associated with rogue trading and distraction bur glary. We evaluated the NCCtargeted areas chosen in 20056 and report whether they experienced a measurable impact on burglary rates in the period up to 2008. Time series data for burglary at the Census Output Area level is analysed using a Bayesian hierarchical modelling approach, addressing issues often encountered in small area quan titative policy evaluation. Results reveal a positive NCC impact on stabilising burglary rates in the targeted areas.
20111004T11:32:26+01:00
1027
1178402
true
16x9
false
no

Evaluation of the Fisher information matrix in nonlinear mixed effects models using Monte Carlo Markov Chains
ucs_sms_125_2025473
http://sms.cam.ac.uk/media/2025473
Evaluation of the Fisher information matrix in nonlinear mixed effects models using Monte Carlo Markov Chains
Riviere, MK (INSERM, Paris)
Tuesday 7th July 2015, 11:30  12:00
Fri, 10 Jul 2015 13:06:45 +0100
Isaac Newton Institute
Riviere, MK
f84217c3e5b51ef13c3c88410722d954
3e3d580d1d06fa531f6ef1ceb857fda9
4878ad2ef00c73deb1f881e778bf7abd
2ff58d24172b3296bb7de5d7edcf83cb
Riviere, MK (INSERM, Paris)
Tuesday 7th July 2015, 11:30  12:00
Riviere, MK (INSERM, Paris)
Tuesday 7th July 2015, 11:30  12:00
Cambridge University
2597
http://sms.cam.ac.uk/media/2025473
Evaluation of the Fisher information matrix in nonlinear mixed effects models using Monte Carlo Markov Chains
Riviere, MK (INSERM, Paris)
Tuesday 7th July 2015, 11:30  12:00
For the analysis of longitudinal data, and especially in the field of pharmacometrics, nonlinear mixed effect models (NLMEM) are used to estimate population parameters and the interindividual variability. To design these studies, optimal design based on the expected Fisher information matrix (FIM) can be used instead of performing timeconsuming clinical trials simulations. Until recently, the FIM in NLMEM was mostly evaluated with firstorder linearization (FO). We propose an approach to evaluate the exact FIM using Monte Carlo (MC) approximation and Markov Chains Monte Carlo (MCMC). Our approach is applicable to continuous as well as discrete data and was implemented in R using the probabilistic programming language STAN. This language enables to efficiently draw MCMC samples and to calculate the partial derivatives of the conditional loglikelihood directly from the model. The method requires several minutes for a FIM evaluation but yields an asymptotically exact FIM. Furthermore, computation time remains similar even for complex models with many parameters. We compare our approach to clinical trials simulation for various continuous and discrete examples.
20150710T13:06:45+01:00
2597
2025473
true
16x9
false
no

Experiences in optimal design for population PK/PD models
ucs_sms_125_1164696
http://sms.cam.ac.uk/media/1164696
Experiences in optimal design for population PK/PD models
Waterhouse, T (Eli Lilly & Company)
Friday 12 August 2011, 14:0014:45
Tue, 16 Aug 2011 18:43:39 +0100
Waterhouse, T
Steve Greenham
Isaac Newton Institute
Waterhouse, T
6ce4c11cddece26b6c49c28d88808dd2
8ffa3637c289d2e97289db08bfba5332
c1a7637c5948c5aeade2d4ae7f4d851d
24d9bcd5545621b130350cea3d946d7f
8dd73910efd96626c00b46b3b51fe410
Waterhouse, T (Eli Lilly & Company)
Friday 12 August 2011, 14:0014:45
Waterhouse, T (Eli Lilly & Company)
Friday 12 August 2011, 14:0014:45
Cambridge University
2547
http://sms.cam.ac.uk/media/1164696
Experiences in optimal design for population PK/PD models
Waterhouse, T (Eli Lilly & Company)
Friday 12 August 2011, 14:0014:45
In all stages of contemporary drug development, the use of mixed effects ("population") models has become crucial to the understanding of pharmacokinetic (PK) and pharmacodynamic (PD) data. Population PK/PD models allow for the use of sparse sampling (i.e., fewer samples per subject), and they can be used to explain different sources of variability, ultimately leading to the possibility of dose optimisation for special populations or even individuals.
The design of trials involving population PK/PD models is often assessed via simulation, although the use of optimal design is gaining prominence. In recent years there have been a number of methodological advances in this area, but this talk will focus on more practical considerations of optimal design in the setting of a pharmaceutical company, from time and cost constraints to awareness and acceptance of optimal design methods. Several examples will be presented for illustration.
20110816T18:43:49+01:00
2547
1164696
true
16x9
false
no

Experimental design challenges in fuel & lubricant R & D
ucs_sms_125_1191833
http://sms.cam.ac.uk/media/1191833
Experimental design challenges in fuel & lubricant R & D
Zemroch, P
Wednesday 30 November 2011, 15:0015:30
Thu, 01 Dec 2011 12:39:08 +0000
Zemroch, P
Steve Greenham
Isaac Newton Institute
Zemroch, P
13fc46f0784dbc5cd22362c880e16e49
dc5946a5aa8df24c470b06bb1aa76b6c
13a1e34706c2e15b2e3d784f6bb361f6
862984f459fc82231e3567e8640df8e9
533f93360ecefa48ac52621710cac0a5
Zemroch, P
Wednesday 30 November 2011, 15:0015:30
Zemroch, P
Wednesday 30 November 2011, 15:0015:30
Cambridge University
2070
http://sms.cam.ac.uk/media/1191833
Experimental design challenges in fuel & lubricant R & D
Zemroch, P
Wednesday 30 November 2011, 15:0015:30
20111202T09:41:35+00:00
2070
1191833
true
16x9
false
no

Experimental design issues for gene expression microarrays
ucs_sms_125_1440
http://sms.cam.ac.uk/media/1440
Experimental design issues for gene expression microarrays
Kerr, K (Washington)
Tuesday 12 August 2008, 14:3015:00
Wed, 10 Sep 2008 13:00:56 +0100
Kerr, K
Isaac Newton Institute
Kerr, K
7b0931a47f9ae8836f33eea54da029b8
82df013af7e3aa5ab16c2ee0e5d0618a
775205f84cb4750c00af1b75e5dd27fd
bbe0b718f77810b9533bc0e6a37b8b24
2c9264c3852f0bf4dfe5b3c1549098c0
8dbb14acf8340eefeb23f69f10119163
5e311a1f5908e4046a6e5a6c8c43323a
Kerr, K (Washington)
Tuesday 12 August 2008, 14:3015:00
Kerr, K (Washington)
Tuesday 12 August 2008, 14:3015:00
Cambridge University
1568
http://sms.cam.ac.uk/media/1440
Experimental design issues for gene expression microarrays
Kerr, K (Washington)
Tuesday 12 August 2008, 14:3015:00
The reference design is a practical and popular choice for microarray studies using twocolor platforms. A "reference" RNA is the linchpin of the design, so an important question is what to use as the reference RNA. I will propose a novel method for evaluating reference RNAs and present the results of an experiment that was designed to evaluate three common choices of reference RNA. I will also discuss advantages of reference designs, and issues on the interpretation of the microarray signal.
http://www.newton.ac.uk/programmes/DOE/seminars/index.html
20130228T12:32:25+00:00
1568
1440
true
4x3
false
no

Experimental designs for estimating variance components
ucs_sms_125_1162238
http://sms.cam.ac.uk/media/1162238
Experimental designs for estimating variance components
Donev, A; LoezaSerrano, S (Manchester)
Tuesday 09 August 2011, 11:4512:30
Wed, 10 Aug 2011 13:46:13 +0100
Donev, A
Steve Greenham
LoezaSerrano, S
Isaac Newton Institute
Donev, A; LoezaSerrano, S
ec32964976c51b3c4a52a08a20a11ee9
ddc21e3f9e4d0dc720c7e83b5a3fa097
218c94245a2179ab3cc5047ee86f95e5
2916f895a2e4a34469db5988c4d31eb3
08d3c907d5f40ff033bbce9d64158b13
Donev, A; LoezaSerrano, S (Manchester)
Tuesday 09 August 2011, 11:4512:30
Donev, A; LoezaSerrano, S (Manchester)
Tuesday 09 August 2011, 11:4512:30
Cambridge University
2670
http://sms.cam.ac.uk/media/1162238
Experimental designs for estimating variance components
Donev, A; LoezaSerrano, S (Manchester)
Tuesday 09 August 2011, 11:4512:30
Many experiments are designed to estimate as precise as possible the fixed parameters in the required models. For example, Doptimum designs ensure that the volume of the confidence ellipsoid for these parameters is minimized. In some cases, only some of the fixed parameters are of interest. DSoptimality is then required. However, little attention has been given to the accuracy of the estimation of the variance components in the models, while they are very important for the interpretation of the results and in some cases it is their estimation that is the reason for the studies to be carried out. We give examples of such studies and focus on the design of experiments where only the variance components are important. The resulting DVoptimum designs are useful to use in crossed or splitplot validation experiments where fixed effects can be regarded as nuisance parameters. We conclude with some considerations about the implications of our results on the design of experiments where both the fixed parameters and the variance components are important.
20110810T13:46:22+01:00
2670
1162238
true
16x9
false
no

Experiments assessing the effects of preanalytical variables on molecular research
ucs_sms_125_1449
http://sms.cam.ac.uk/media/1449
Experiments assessing the effects of preanalytical variables on molecular research
Speed, T (UC, Berkeley)
Tuesday 12 August 2008, 12:0012:30
Thu, 11 Sep 2008 11:44:27 +0100
Speed, T
Isaac Newton Institute
Speed, T
ee5087d1b2830d23074815635f89fb31
d4e2f13ded8faeeff96562aa1ebb2eac
314b36521898020ddb1403c5567f2385
04a0ed96ef9b9c35562ffc6e64a75038
91ca839b9ca68b8c733eecf758594d86
a1fac86fdc5e84eb51b02c9b1ff02e54
7570b3af1a542eb31a8fc747b10189fe
Speed, T (UC, Berkeley)
Tuesday 12 August 2008, 12:0012:30
Speed, T (UC, Berkeley)
Tuesday 12 August 2008, 12:0012:30
Cambridge University
2038
http://sms.cam.ac.uk/media/1449
Experiments assessing the effects of preanalytical variables on molecular research
Speed, T (UC, Berkeley)
Tuesday 12 August 2008, 12:0012:30
When the abundance of mRNA, proteins or metabolites in cell samples is measured using a genomic, proteomic or metabolomic assay, it may happen that the measurement is more influenced by uncontrolled preanalytical variables than by the measurement process itself. For example, if the cells are from a tissue sample taken during surgery, variables such as drugs, type or duration of anesthesia, and arterial clamp time can greatly affect the final molecular measurements, as can a host of postacquisition variables such as time at room temperature, temperature of the room prior to fixing, type of fixative, time in fixative, rate of freezing, and so on. Lack of awareness of these possible effects can lead to incorrect diagnosis, incorrect treatment, and irreproducible results in research. How do we determine which of these variables matter for a given assay, and how do we derive standard procedures for sample acquisition, handling, processing and storage, prior to the assay? The answer is, of course, through experimentation. We will need to combine screening experiments, as the number of potentially important variables is large, with later experiments to determine robust combinations of factors which might become new standard operating procedures. The experiments must be on human tissue, we'd like replicates, and we'd like to be able to distinguish intraperson and interperson variability. There are significant practical and ethical constraints surrounding such experiments. Nevertheless, the US National Cancer Institute's Office of Biorepositories and Biospecimen Research is committed to carrying out such experiments, to address the problems mentioned above. In this talk I will discuss some of the design challenges they are meeting, illustrating my discussion with an example concerning blood drawing.
http://www.newton.ac.uk/programmes/DOE/seminars/index.html
20130228T12:35:06+00:00
2038
1449
true
4x3
false
no

Experiments for Enzyme Kinetic Models
ucs_sms_125_1165163
http://sms.cam.ac.uk/media/1165163
Experiments for Enzyme Kinetic Models
Atkinson, A
Monday 15 August 2011, 11:4512:30
Wed, 17 Aug 2011 14:05:28 +0100
Atkinson, A
Steve Greenham
Isaac Newton Institute
Atkinson, A
122c0becf63d92d2c9feeeda09b2dd25
a2744416b96a3fc29c84f2b045a16bb6
6c66619fc3ce0dca0a27c316a8eea005
0e8969670ba943b94482fda781facf85
de87e33139ef88dae83a6ac5289d3641
Atkinson, A
Monday 15 August 2011, 11:4512:30
Atkinson, A
Monday 15 August 2011, 11:4512:30
Cambridge University
2232
http://sms.cam.ac.uk/media/1165163
Experiments for Enzyme Kinetic Models
Atkinson, A
Monday 15 August 2011, 11:4512:30
Enzymes are biological catalysts that act on substrates. The speed of reaction as a function of substrate concentration typically follows the nonlinear MichaelisMenten model. The reactions can be modified by the presence of inhibitors, which can act by several different mechanisms, leading to a variety of models, all also nonlinear.
The talk will describe the models and derive optimum experimental designs for model building. When the model is known these include Doptimum designs for all the parameters for which we obtain analytical solutions. Dsoptimum designs for the inhibition constant are also of scientific importance.
When the model is not known, the choice is often between two threeparameter models. These can be combined in a single fourparameter model. Dsoptimum designs for the parameter of combination provide a means of establishing which model is true. However, Toptimum designs for departures from the individual models provide tests of maximum power for departures from the models. With two models on an equal footing, compound Toptimum designs are required. Their properties are compared with those of the Dsoptimum designs in the combined model, which have the advantage of being easier to compute.
20110817T14:05:38+01:00
2232
1165163
true
16x9
false
no

Experiments in blocks for a nonnormal response
ucs_sms_125_1433
http://sms.cam.ac.uk/media/1433
Experiments in blocks for a nonnormal response
Woods, D (Southampton)
Monday 11 August 2008, 16:3017:00
Tue, 09 Sep 2008 12:55:26 +0100
Woods, D
Isaac Newton Institute
Woods, D
b55d0aeb62ef254d0240d0cbb771ce10
46070ca4f08e543618a0574b49581110
2455a113d3dd93bed836a998e9be01e1
947af86fa373e90869322ef0d7351acb
99b6b92a4b8818a5e648981d357ba2da
16e095d1ec7555a55b5ca9b66d779460
6d43d4d5d2ccead49dde70e48a9a4e7f
Woods, D (Southampton)
Monday 11 August 2008, 16:3017:00
Woods, D (Southampton)
Monday 11 August 2008, 16:3017:00
Cambridge University
1832
http://sms.cam.ac.uk/media/1433
Experiments in blocks for a nonnormal response
Woods, D (Southampton)
Monday 11 August 2008, 16:3017:00
Many industrial experiments measure a response that cannot be adequately described by a linear model with normally distributed errors. An example is an experiment in aeronautics to investigate the cracking of bearing coatings where a binary response was observed, success (no cracking) or failure (cracked). A further complication which often occurs in practice is the need to run the experiment in blocks, for example, to account for different operators or batches of experimental units. To produce more efficient experiments, block effects are often included in the model for the response. When the block effects can be considered as nuisance variables, a marginal (or population averaged) model may be appropriate, where the effect of individual blocks are not explicitly modelled. We discuss block designs for experiments where the response is described by a marginal model fitted using Generalised Estimating Equations (GEEs). GEEs are an extension of Generalised Linear Models (GLMs) that incorporate a correlation structure between experiment units in the same block; the marginal response for each observation follows an appropriate GLM. This talk will describe some design strategies for such models in an industrial context.
http://www.newton.ac.uk/programmes/DOE/seminars/index.html
20130228T12:34:53+00:00
1832
1433
true
4x3
false
no

Factorial designs for cDNA microarray experiments: results and some open issues
ucs_sms_125_1445
http://sms.cam.ac.uk/media/1445
Factorial designs for cDNA microarray experiments: results and some open issues
Mukerjee, R (Indian Institute of Management, Calcutta)
Tuesday 12 August 2008, 15:0015:30
Thu, 11 Sep 2008 07:55:51 +0100
Mukerjee, R
Isaac Newton Institute
Mukerjee, R
7aeda15b926e57ccb085179781a60bb6
41e45d78ffe8b68ff64b4dbc941882d2
7c343538722b85b03c2686a7f0e25f0e
dbe7674e6f611995f8f827e293ce7967
7fc438460697d224209810021aacab9b
38223f9a91d2870ec8cb54fb7d8fcbc3
b81aabbf95468284eebb419fed54c8c3
Mukerjee, R (Indian Institute of Management, Calcutta)
Tuesday 12 August 2008,...
Mukerjee, R (Indian Institute of Management, Calcutta)
Tuesday 12 August 2008, 15:0015:30
Cambridge University
2081
http://sms.cam.ac.uk/media/1445
Factorial designs for cDNA microarray experiments: results and some open issues
Mukerjee, R (Indian Institute of Management, Calcutta)
Tuesday 12 August 2008, 15:0015:30
We consider factorial designs for cDNA microarray experiments under a baseline parametrization where the objects of interest differ from those under the more common orthogonal parametrization. Complete factorials are discussed first and some optimality results are given, including those pertaining to the saturated and nearly saturated cases. The case of models with dyecoloring effects is also covered. The technical tools include approximate theory and use of unimodular matrices. The more complex issue of fractional replication is then taken up and several open problems are indicated.
http://www.newton.ac.uk/programmes/DOE/seminars/index.html
20130228T12:34:44+00:00
2081
1445
true
4x3
false
no

Fitting Latent Variable Models for Paired Comparisons and Ranking Studies  An Application of Optimal Design Theory
ucs_sms_125_1169592
http://sms.cam.ac.uk/media/1169592
Fitting Latent Variable Models for Paired Comparisons and Ranking Studies  An Application of Optimal Design Theory
Torsney, B (University of Glasgow)
Thursday 01 September 2011, 10:0010:30
Mon, 05 Sep 2011 10:47:18 +0100
Torsney, B
Steve Greenham
Isaac Newton Institute
Torsney, B
d96ba34e6a743dbfccf3d8b921512ca1
397d2d4e3bcad5366610310cf235d532
a6aae13695bce7e4f148366041d5338c
47b9e38a2534321c27e555cd58a99f5a
452ab7a8f8c9dd96c7d82f08c52b1324
Torsney, B (University of Glasgow)
Thursday 01 September 2011, 10:0010:30
Torsney, B (University of Glasgow)
Thursday 01 September 2011, 10:0010:30
Cambridge University
1723
http://sms.cam.ac.uk/media/1169592
Fitting Latent Variable Models for Paired Comparisons and Ranking Studies  An Application of Optimal Design Theory
Torsney, B (University of Glasgow)
Thursday 01 September 2011, 10:0010:30
In a paired comparisons experiment a subject has to indicate which of two 'treatments' Ti, Tj is preferred. We observe Oij, the frequency with which Ti is preferred to Tj.in nij comparisons. Under a class of models for such data, which include the Bradley Terry and Thurstone models, P(Ti is preferred to Tj) = F( i  j), where F(.) is a symmetric distribution function and ( i) is a treatment index. For identifiability purposes constraints must be imposed on parameters. One is to assume that ipi = 1, where pi = ln( i); an alternative is ipi = 1. Thus theorems identifying optimal design weights and algorithms for determining them carry over to the maximum likelihood estimation of these parameters.
Of course these tools can also be used to determine locally optimal designs for such models.
We will explore this fusion of topics, taking the opportunity to expand on the class of models, both for simple paired comparisons data and also for data consisting of orderings or rankings. In particular we will exploit multiplicative algorithms for maximum likelihood estimation.
20110905T10:47:27+01:00
1723
1169592
true
16x9
false
no

Flexible designs for late phase clinical trials
ucs_sms_125_1468
http://sms.cam.ac.uk/media/1468
Flexible designs for late phase clinical trials
Friede, T (Warwick)
Thursday 14 August 2008, 15:0015:30
Mon, 15 Sep 2008 11:47:41 +0100
Friede, T
Steve Greenham
Isaac Newton Institute
Friede, T
a27411a1c1cef30cb605a413661e4af2
42181357cf334c192b16bb3d7005d947
0cfe60abc4e9b3c1bb6e7b32fbda8669
48a90cd8de9e4d7a1f46704c390ba728
cdc74cb069e5601df4dde06efa83eb37
6ea5415cac98f4bd8c2796c9478d0c7d
e9ef886adfbf4bef7d8607bd22b457c7
Friede, T (Warwick)
Thursday 14 August 2008, 15:0015:30
Friede, T (Warwick)
Thursday 14 August 2008, 15:0015:30
Cambridge University
2085
http://sms.cam.ac.uk/media/1468
Flexible designs for late phase clinical trials
Friede, T (Warwick)
Thursday 14 August 2008, 15:0015:30
Socalled adaptive or flexible designs are recognised as one way of making clinical development of new treatments more efficient and more robust against misspecifications of parameters in the planning phase. In this presentation we give a brief introduction to flexible designs for clinical trials and then focus on two specific adaptations, namely sample size reestimation and treatment selection. Issues in the implementation of such designs will be discussed.
http://www.newton.ac.uk/programmes/DOE/seminars/index.html
20130228T12:34:33+00:00
2085
1468
true
4x3
false
no

Forward Smoothing and Online EM in changepoint systems
ucs_sms_125_1177930
http://sms.cam.ac.uk/media/1177930
Forward Smoothing and Online EM in changepoint systems
Yildirim, S (Stats Lab)
Monday 26 September 2011, 12:4012:50
Tue, 04 Oct 2011 09:06:39 +0100
Isaac Newton Institute
Yildirim, S
12de2cc9a5e7f289dbb3eddd9e381865
faaeaf68d2d993dc420cad885db174d8
e23ad8e6e8bd4b1c87371cd8056b9dc3
fe7e141df2488522decda19be047d0ea
5829bafd515d0339f048d0a065fa53b8
Yildirim, S (Stats Lab)
Monday 26 September 2011, 12:4012:50
Yildirim, S (Stats Lab)
Monday 26 September 2011, 12:4012:50
Cambridge University
805
http://sms.cam.ac.uk/media/1177930
Forward Smoothing and Online EM in changepoint systems
Yildirim, S (Stats Lab)
Monday 26 September 2011, 12:4012:50
In this talk, I will focus on forward smoothing in changepoint systems, which are gen erally used to model the heterogeneity in the statistical data. After showing the SMC implementation of forward smoothing, I will show how we can perform the online EM algorithm for parameter estimation in changepoint systems.
20111004T09:06:48+01:00
805
1177930
true
16x9
false
no

From Bench to Bedside: The Application of Differential Protein Networks on Bayesian Adaptive Designs for Trials with Targetted Therapies
ucs_sms_125_1166511
http://sms.cam.ac.uk/media/1166511
From Bench to Bedside: The Application of Differential Protein Networks on Bayesian Adaptive Designs for Trials with Targetted Therapies
Ji, Y (.D. Anderson Cancer Center)
Thursday 18 August 2011, 14:0014:45
Tue, 23 Aug 2011 13:46:21 +0100
Isaac Newton Institute
Ji, Y
a7085d6e2addfac0821960a6993ff87e
9d6bf91fe4336cf3b356325b77263498
e676d1b02b2afca5cdd3e11b22d942b8
d0138e02026dcb5e2dc06b97bb2a1092
b7634f9b7bafb90ad1ab43ffa609171a
Ji, Y (.D. Anderson Cancer Center)
Thursday 18 August 2011, 14:0014:45
Ji, Y (.D. Anderson Cancer Center)
Thursday 18 August 2011, 14:0014:45
Cambridge University
2727
http://sms.cam.ac.uk/media/1166511
From Bench to Bedside: The Application of Differential Protein Networks on Bayesian Adaptive Designs for Trials with Targetted Therapies
Ji, Y (.D. Anderson Cancer Center)
Thursday 18 August 2011, 14:0014:45
20110823T13:46:30+01:00
2727
1166511
true
16x9
false
no

From ideas to implementation
ucs_sms_125_1191778
http://sms.cam.ac.uk/media/1191778
From ideas to implementation
Owen, M; Llewellyn , K
Wednesday 30 November 2011, 14:0014:30
Thu, 01 Dec 2011 12:08:57 +0000
Owen, M; Llewellyn , K
Steve Greenham
Isaac Newton Institute
Owen, M; Llewellyn , K
f1b80e24cf42fb2d9c2af46a1856e7f5
5f3d2a7d18c1e06cb20123f3a51543fd
a85a850a5b9fbeefbcaaff6b72093114
50a48df20d339826584b6f715f4eaf49
25546f7e1cf891e330443bcb285f7b55
Owen, M; Llewellyn , K
Wednesday 30 November 2011, 14:0014:30
Owen, M; Llewellyn , K
Wednesday 30 November 2011, 14:0014:30
Cambridge University
1817
http://sms.cam.ac.uk/media/1191778
From ideas to implementation
Owen, M; Llewellyn , K
Wednesday 30 November 2011, 14:0014:30
At GlaxoSmithKline we use sequential experimental design to generate the process understanding that identifies critical process parameters and safe operating conditions for the manufacture of active pharmaceutical ingredients used in medicines. We are always looking for more efficient and effective experimental strategies and ways of managing uncertainty. At a recent conference, Stuart Hunter observed that "within the last ten years there has been some spectacular progress in the field of experimental design  the arena has completely changed". We want to show the decisionmaking progress around how much, and when to invest in experimental designs, what are the benefits, what risks are faced and are these acceptable? We will discuss how we have taken learnings from a couple of recently published papers on Supersaturated Designs and Definitive Designs and show how we have implemented these ideas to add value within GlaxoSmithKline References Marley, C. J. and Woods, D. C. (2010), "A comparison of design and model selection methods for supersaturated experiments," Computational Statistics and Data Analysis, 54, 31583167. A Class of ThreeLevel Designs for Definitive Screening in the Presence of SecondOrder Effects Brad Jones, SAS Institute, Christopher J. Nachsteim Journal of Quality Technology Vol. 43, No. 1, January 2011
20111202T09:42:24+00:00
1817
1191778
true
16x9
false
no

From parametric optimization to optimal experimental design: A new perspective in the context of partial differential equations
ucs_sms_125_1158408
http://sms.cam.ac.uk/media/1158408
From parametric optimization to optimal experimental design: A new perspective in the context of partial differential equations
Carraro, T (RuprechtKarlsUniversität Heidelberg)
Thursday 21 July 2011, 10:3011:00
Fri, 22 Jul 2011 15:54:12 +0100
Carraro, T
Steve Greenham
Isaac Newton Institute
Carraro, T
2ed48dee7a72fefbcb5ef0303d4fadaf
bdadf351aad35e3e3d16db28987419f7
61dc7278546960c3bad8736ace547d71
fac699310ec945b67314f0c0161afc54
f623ca664de3e59fca0bf759a35ecc5e
Carraro, T (RuprechtKarlsUniversität Heidelberg)
Thursday 21 July 2011,...
Carraro, T (RuprechtKarlsUniversität Heidelberg)
Thursday 21 July 2011, 10:3011:00
Cambridge University
1923
http://sms.cam.ac.uk/media/1158408
From parametric optimization to optimal experimental design: A new perspective in the context of partial differential equations
Carraro, T (RuprechtKarlsUniversität Heidelberg)
Thursday 21 July 2011, 10:3011:00
We propose a new perspective of the optimal experimental design problem (OED), whose several theoretical and computational aspects have been previously studied. The formal setting of parametric optimization leads to the definition of a generalized framework from which the OED problem can be derived. Although this approach does not have a direct impact on the computational aspects, it links the OED problem to a wider field of theoretical results ranging from optimal control problems to the stability of optimization problems. Following this approach, we derive the OED problem in the context of partial differential equations (PDE) and present a primaldual active set strategy to solve the constrained OED problem. Numerical examples are presented.
20110722T15:54:20+01:00
1923
1158408
true
16x9
false
no

Functional Bernsteintype inequalities via Rademacher Processess; with applications to statistics
ucs_sms_125_1177883
http://sms.cam.ac.uk/media/1177883
Functional Bernsteintype inequalities via Rademacher Processess; with applications to statistics
Nickl, R (Stats Lab)
Monday 26 September 2011, 12:2012:30
Mon, 03 Oct 2011 17:03:42 +0100
Isaac Newton Institute
Nickl, R
cb1e95e0b94b4026de8006796a4adbb6
c1bba453ebab853ece8bd3c0024d8e88
f0911b1ed10431c6665935b68ce875cc
4211f72834a61250cb38776243969cfe
015f764970bb8fb8081c1d3a4e12d96a
Nickl, R (Stats Lab)
Monday 26 September 2011, 12:2012:30
Nickl, R (Stats Lab)
Monday 26 September 2011, 12:2012:30
Cambridge University
743
http://sms.cam.ac.uk/media/1177883
Functional Bernsteintype inequalities via Rademacher Processess; with applications to statistics
Nickl, R (Stats Lab)
Monday 26 September 2011, 12:2012:30
20111003T17:03:53+01:00
743
1177883
true
16x9
false
no

Functional uniform prior distributions for nonlinear regression
ucs_sms_125_1166734
http://sms.cam.ac.uk/media/1166734
Functional uniform prior distributions for nonlinear regression
Bornkamp, B (Novartis)
Friday 19 August 2011, 09:4510:30
Wed, 24 Aug 2011 11:04:01 +0100
Isaac Newton Institute
Bornkamp, B
b30d598de4ff544900f5821be9c70770
8a6a54ce379d52cc2f99691353a63f35
1834566d5213f5cae89ad92d49baac09
ca818731a6715951153bbd5f8e27a396
731da32d3358f2b113e1524b79bc4546
Bornkamp, B (Novartis)
Friday 19 August 2011, 09:4510:30
Bornkamp, B (Novartis)
Friday 19 August 2011, 09:4510:30
Cambridge University
2780
http://sms.cam.ac.uk/media/1166734
Functional uniform prior distributions for nonlinear regression
Bornkamp, B (Novartis)
Friday 19 August 2011, 09:4510:30
In this talk I will consider the topic of finding prior distributions in nonlinear modelling situations, that is, when a major component of the statistical model depends on a nonlinear function. Making use of a functional change of variables theorem, one can derive a distribution that is uniform in the space of functional shapes of the underlying nonlinear function and then backtransform to obtain a prior distribution for the original model parameters. The primary application considered in this article is nonlinear regression in the context of clinical dosefinding trials. Here the so constructed priors have the advantage that they are parametrization invariant as opposed to uniform priors on parameter scale and can be calculated prior to data collection as opposed to the Jeffrey’s prior. I will investigate the priors for a real data example and for calculation of Bayesian optimal designs, which require the prior distribution to be available before data collection has started (so that classical objective priors such as Jeffreys priors cannot be used).
20110824T11:04:10+01:00
2780
1166734
true
16x9
false
no

Future Challenges Integrating Multiple Modes of Experimentation
ucs_sms_125_1172554
http://sms.cam.ac.uk/media/1172554
Future Challenges Integrating Multiple Modes of Experimentation
Wynn, HP; Higdon, D; Barton, R
Friday 09 September 2011, 14:0015:30
Thu, 15 Sep 2011 13:48:34 +0100
Isaac Newton Institute
Wynn, HP; Higdon, D; Barton, R
7d997ea9cab2086321635949571e0b8b
5e5333616b8fa12152e1eb8b35149ac1
6323c9744d6ea7f884cab9430f9c98d4
e6f45197ac774b720bbdf3473f7ffe6f
49c0e27dc3dc920eec7b7fbf2cbfa22b
Wynn, HP; Higdon, D; Barton, R
Friday 09 September 2011, 14:0015:30
Wynn, HP; Higdon, D; Barton, R
Friday 09 September 2011, 14:0015:30
Cambridge University
4897
http://sms.cam.ac.uk/media/1172554
Future Challenges Integrating Multiple Modes of Experimentation
Wynn, HP; Higdon, D; Barton, R
Friday 09 September 2011, 14:0015:30
A panel discussion from the Challenges of Largescale Computer Simulators session, as part of the Accelerating Industrial Productivity via Deterministic Computer Experiments & Stochastic Simulation conference.
20110915T13:48:43+01:00
4897
1172554
true
16x9
false
no

GLMs and GLMMs in the analysis of randomized experiments
ucs_sms_125_1162652
http://sms.cam.ac.uk/media/1162652
GLMs and GLMMs in the analysis of randomized experiments
Gilmour, S (Southampton)
Tuesday 09 August 2011, 14:0014:45
Thu, 11 Aug 2011 14:41:51 +0100
Isaac Newton Institute
Gilmour, S
563a030bc7da4091b7527b805c8e13b1
1ee68bd68b5d4faeafd6968a59151da1
784db60ba1c1029285da64fa56b10231
88d32841b207cb4dd9bc7b28d547a9c8
6e05381e922e34b7c8a0510c062181de
Gilmour, S (Southampton)
Tuesday 09 August 2011, 14:0014:45
Gilmour, S (Southampton)
Tuesday 09 August 2011, 14:0014:45
Cambridge University
2914
http://sms.cam.ac.uk/media/1162652
GLMs and GLMMs in the analysis of randomized experiments
Gilmour, S (Southampton)
Tuesday 09 August 2011, 14:0014:45
The Normal linear model analysis is usually used as an approximation to the exact randomization analysis and extended to structures, such as nonorthogonal splitplot designs, as a natural approximation. If the responses are counts or proportions a generalized linear model (GLM) is often used instead. It will be shown that GLMs do not in general arise as natural approximations to the randomization model. Instead the natural approximation is a generalized linear mixed model (GLMM).
20110811T14:42:01+01:00
2914
1162652
true
16x9
false
no

Group sensor scheduling for parameter estimation of randomeffects distributed systems
ucs_sms_125_1162840
http://sms.cam.ac.uk/media/1162840
Group sensor scheduling for parameter estimation of randomeffects distributed systems
Patan, M (Zielona Gora, Poland)
Thursday 11 August 2011, 11:0011:45
Thu, 11 Aug 2011 16:18:32 +0100
Patan, M
Steve Greenham
Isaac Newton Institute
Patan, M
cf22904a60d7030bedd3a6cc426b262c
014d6b5617fdc498c00753aca75f5b34
598da51c09fd31193220079f13afc6f9
82d9062d575ffd79d986758a4cfb8ae7
4802ca9706d993b67490cd7de819c11c
Patan, M (Zielona Gora, Poland)
Thursday 11 August 2011, 11:0011:45
Patan, M (Zielona Gora, Poland)
Thursday 11 August 2011, 11:0011:45
Cambridge University
2711
http://sms.cam.ac.uk/media/1162840
Group sensor scheduling for parameter estimation of randomeffects distributed systems
Patan, M (Zielona Gora, Poland)
Thursday 11 August 2011, 11:0011:45
The problem of sensor location for monitoring network with stationary nodes used for estimating unknown parameters of distributedparameter system is addressed. In particular, the situation is considered, when the system parameters at the experimentation stage may randomly change according to the slight fluctuations of experimental conditions or differences in individual properties of observed distributed systems. A proper theoretical formulation of the sensor scheduling problem is provided together with a characterization of the optimal solutions. The theory is applicable to those practical situations in which a distributed system is sensitive to sampling or gives a different response at each run of the experiment. In the presented approach, some results from experimental design theory for dynamic systems are extended for the purpose of configuring a sensor grid in order to obtain practical and numerically tractable representation of optimum designs for estimation of the mean values of the parameters. A suitable computational scheme is illustrated by numerical example on a sensor scheduling problem for a twodimensional example of dynamical distributed process representing the performance of magnetic brake.
20110811T16:18:41+01:00
2711
1162840
true
16x9
false
no

Identifying the effect of treatment on the treated
ucs_sms_125_1178166
http://sms.cam.ac.uk/media/1178166
Identifying the effect of treatment on the treated
Ramsahai, R (Stats Lab)
Monday 26 September 2011, 16:0516:15
Tue, 04 Oct 2011 10:41:31 +0100
Isaac Newton Institute
Ramsahai, R
93016d3404f6270dcdd015518a4aea52
36be9f89e301701d355e2dbb29dca07f
c569d9637a1c4a8dc007aeda460644b2
56c456af1739840ce8f55a99f45acb4a
68f7485f347ef1fcec551e7354f4003d
Ramsahai, R (Stats Lab)
Monday 26 September 2011, 16:0516:15
Ramsahai, R (Stats Lab)
Monday 26 September 2011, 16:0516:15
Cambridge University
960
http://sms.cam.ac.uk/media/1178166
Identifying the effect of treatment on the treated
Ramsahai, R (Stats Lab)
Monday 26 September 2011, 16:0516:15
In the counterfactual literature, the effect of treatment on the treated (ETT) is often branded as the effect on the treated group. This definition of ETT is vague and poten tially misleading because ETT is the effect on those who would normally be treated. A more transparent definition of ETT is given within the decision theoretic framework. The proposed definition of ETT is used to highlight misuse of terminology in the lit erature and discuss the types of studies that can be used for identifying ETT. Criteria for identifying ETT from observational data, when there are unobserved confounders, are given. The criteria are compared to those formulated within the counterfactual framework.
20111004T10:41:42+01:00
960
1178166
true
16x9
false
no

Improved conditional approximations of the population Fisher information matrix
ucs_sms_125_1164672
http://sms.cam.ac.uk/media/1164672
Improved conditional approximations of the population Fisher information matrix
Nyberg, J (Uppsala)
Friday 12 August 2011, 11:4512:30
Tue, 16 Aug 2011 18:34:03 +0100
Nyberg, J
Steve Greenham
Isaac Newton Institute
Nyberg, J
abab140df43c78bcb61d657dfa402e45
cb394040b3ce2ace88c9255d858f0b70
881b9a8245ecef4f1f69db84717c2e70
1c2d584f10d28301ca821d4d280182d5
e47dee7a040f078fe1b38ab81d870479
Nyberg, J (Uppsala)
Friday 12 August 2011, 11:4512:30
Nyberg, J (Uppsala)
Friday 12 August 2011, 11:4512:30
Cambridge University
2084
http://sms.cam.ac.uk/media/1164672
Improved conditional approximations of the population Fisher information matrix
Nyberg, J (Uppsala)
Friday 12 August 2011, 11:4512:30
We present an extended approximation of the Fisher Information Matrix (FIM) for nonlinear mixed effects models based on a first order conditional (FOCE) approximation of the population likelihood. Unlike previous FOCE based FIM, we use the empirical Bayes estimates to derive the FIM. In several examples, compared to the old FOCE based FIM, the improved FIM predicts parameter uncertainty much closer to simulation based empirical parameter uncertainty. Furthermore, this approach seems more robust against other approximations of the FIM, i.e. (Full/Reduced FIM). Finally, the new FOCE derived FIM is slightly closer to the simulated empirical precision than the FO based FIM.
20110909T15:07:18+01:00
2084
1164672
true
16x9
false
no

Improving dosefinding methods in clinical development: design, adaptation, and modeling
ucs_sms_125_1165482
http://sms.cam.ac.uk/media/1165482
Improving dosefinding methods in clinical development: design, adaptation, and modeling
Pinheiro, J (Johnson & Johnson)
Tuesday 16 August 2011, 12:0012:30
Wed, 17 Aug 2011 15:40:54 +0100
Pinheiro, J
Isaac Newton Institute
Pinheiro, J
f293ab353d3dbf98d6ed9ab4be5f17fe
18deab61e554a025478af506fac420aa
a1457a0b68a588ac3d5620350ac9e4b6
feeb7b9470522c532593c9c78603fb78
c518b12040c85cd55ed4cde6be92ff9f
Pinheiro, J (Johnson & Johnson)
Tuesday 16 August 2011, 12:0012:30
Pinheiro, J (Johnson & Johnson)
Tuesday 16 August 2011, 12:0012:30
Cambridge University
1850
http://sms.cam.ac.uk/media/1165482
Improving dosefinding methods in clinical development: design, adaptation, and modeling
Pinheiro, J (Johnson & Johnson)
Tuesday 16 August 2011, 12:0012:30
The pharmaceutical industry experiences increasingly challenging conditions, with a combination of escalating development costs, tougher regulatory environment, expiring patents on important drugs, and fewer promising drugs in late stage of development. Part of this pipeline problem is attributed to poor dose selection for confirmatory trials leading to high attrition rates (estimated at 50%) for Phase 3 programs. Improving the efficiency of drug development, in general, and of dosefinding studies in particular, is critical for the survival of the industry. A variety of methods have been proposed to improve dose selection and, more broadly, understanding of the doseresponse relationship for a compound. Among them: adaptive designs, modeling and simulation approaches, optimal designs, and clinical utility indices. In this talk we’ll discuss and illustrate the utilization of some of those approaches in the context of dosefinding trials. The results of a comprehensive se t of simulation studies conducted by the PhRMA working group on Adaptive DoseRanging Studies will be used to discuss the relative merits of the various approaches and to motivate recommendations on their use in practice.
20110831T13:42:38+01:00
1850
1165482
true
16x9
false
no

Improving the efficiency of individualized designs for the mixed logit model by including covariates
ucs_sms_125_1168767
http://sms.cam.ac.uk/media/1168767
Improving the efficiency of individualized designs for the mixed logit model by including covariates
Crabbe, M (KU, Leuven)
Wednesday 31 August 2011, 17:0017:30
Thu, 01 Sep 2011 15:24:46 +0100
Isaac Newton Institute
Crabbe, M
e2b8cd522961ae46edb53b3701559790
9323d964acb1f3652075717236f24199
bd8a511adea6a866d1e4e9da23082ac6
cbf4bf87a25d05b29dd78116a88d9e93
99deb7a6e31727216a71fe467729d344
Crabbe, M (KU, Leuven)
Wednesday 31 August 2011, 17:0017:30
Crabbe, M (KU, Leuven)
Wednesday 31 August 2011, 17:0017:30
Cambridge University
1769
http://sms.cam.ac.uk/media/1168767
Improving the efficiency of individualized designs for the mixed logit model by including covariates
Crabbe, M (KU, Leuven)
Wednesday 31 August 2011, 17:0017:30
Conjoint choice experiments have become an established tool to get a deeper insight in the choice behavior of consumers. Recently, the discrete choice literature focused attention on the use of covariates like demographics, socioeconomic variables or other individualspecific characteristics in design and estimation of discrete choice models, more specifically on whether the incorporation of such choice related respondent information aids in increasing estimation and prediction accuracy. The discrete choice model considered in this paper is the panel mixed logit model. This randomeffects choice model accommodates preference heterogeneity and moreover, accounts for the correlation between individuals’ successive choices. Efficient choice data for the panel mixed logit model is obtained by individually adapted sequential Bayesian designs, which are customized to the specific preferences of a respondent, and reliable estimates for the model parameters are acquired by means of a hierarchical Bayes estimation approach. This research extends both experimental design and model estimation for the panel mixed logit model to include covariate information. Simulation studies of various experimental settings illustrate how the inclusion of influential covariates yields more accurate estimates for the individual parameters in the panel mixed logit model. Moreover, we show that the efficiency loss in design and estimation resulting from including choice unrelated respondent characteristics is negligible.
20110901T15:24:56+01:00
1769
1168767
true
16x9
false
no

Incorporating pharmacokinetic information in phase I studies in small populations
ucs_sms_125_2025518
http://sms.cam.ac.uk/media/2025518
Incorporating pharmacokinetic information in phase I studies in small populations
Ursino, M (INSERM, Paris)
Tuesday 7th July 2015, 15:00  15:30
Fri, 10 Jul 2015 13:26:58 +0100
Isaac Newton Institute
Ursino, M
af414321d7a9d75a1ad37f417f3c1d17
13fcde9eb4a3c15a48c209ddaeb74ed0
c94d6f7f44330e14cb077e4dc7be4898
5fcdfc2845916f108d33872d53ea2c05
Ursino, M (INSERM, Paris)
Tuesday 7th July 2015, 15:00  15:30
Ursino, M (INSERM, Paris)
Tuesday 7th July 2015, 15:00  15:30
Cambridge University
2028
http://sms.cam.ac.uk/media/2025518
Incorporating pharmacokinetic information in phase I studies in small populations
Ursino, M (INSERM, Paris)
Tuesday 7th July 2015, 15:00  15:30
Objectives: To review and extend existing methods which take into account PK measurements in sequential adaptive designs for early dosefinding studies in small populations, and to evaluate the impact of PK measurements on the selection of the maximum tolerated dose (MTD). Methods: This work is set in the context of phase I dosefinding studies in oncology, where the objective is to determine the MTD while limiting the number of patients exposed to high toxicity. We assume toxicity to be related to a PK measure of exposure, and consider 6 possible dose levels. Three Bayesian phase I methods from the literature were modified and compared to the standard continual reassessment method (CRM) through simulations. In these methods PK measurement, more precisely the AUC, is present as covariate for a link function of probability of toxicity (Piantadosi and Liu (1996), Whitehead et al. (2007)) and/or as dependent variable in linear regression versus dose (Patterson et al. (1999), Whitehead et al. (2007)). We simulated trials based on a model for the TGF inhibitor LY2157299 in patients with glioma (Gueorguieva et al., 2014). The PK model was reduced to a onecompartment model with firstorder absorption as in Lestini et al. (2014) in order to achieve a close solution for the probability of toxicity. Toxicity was assumed to occur when the value of a function of AUC was above a given threshold, either in the presence or without interindividual variability (IIV). For each scenario, we simulated 1000 trials with 30, 36 and 42 patients. Results: Methods which incorporate PK measurements had good performance when informative prior knowledge was available in term of Bayesian prior distribution on parameters. On the other hand, keeping fixed the priors information, methods that included PK values as covariate were less flexible and lead to trials with more toxicities than the same trials with CRM. Conclusion: Incorporating PK values as covariate did not alter the efficiency of estimation of MTD when the prior was well specified. The next step will be to assess the impact on the estimation of the doseconcentrationtoxicity curve for the different approaches and to explore the introduction of fully modelbased PK/PD in dose allocation rules.
20150710T13:26:58+01:00
2028
2025518
true
16x9
false
no

Individuals are different: Implications on the design of experiments
ucs_sms_125_1157475
http://sms.cam.ac.uk/media/1157475
Individuals are different: Implications on the design of experiments
Schwabe, R (OttovonGuerickeUniversität Magdeburg)
Monday 18 July 2011, 15:3016:30
Wed, 20 Jul 2011 18:27:06 +0100
Schwabe, R
Steve Greenham
Isaac Newton Institute
Schwabe, R
946862b2b95b6548c45b250519374695
c688ef524822dd94798bd24f20d5858a
d5ab00d258189b09a0cdfeb9d0b9dee2
2b11b5db559367df6b3ac7185720e252
f43deeda0ed89078883595caf1d84e6c
Schwabe, R (OttovonGuerickeUniversität Magdeburg)
Monday 18 July 2011,...
Schwabe, R (OttovonGuerickeUniversität Magdeburg)
Monday 18 July 2011, 15:3016:30
Cambridge University
3473
http://sms.cam.ac.uk/media/1157475
Individuals are different: Implications on the design of experiments
Schwabe, R (OttovonGuerickeUniversität Magdeburg)
Monday 18 July 2011, 15:3016:30
If dynamics is measured repeatedly in biological entities like human beings or animals, the diversity of individuals may have a crucial impact on the outcomes of the measurements. An adequate approach for this situation is to assume random coefficients for each individual. This leads to nonlinear mixed models, which have attracted an increasing popularity in many fields of applications in recent years due to advanced computer facilities. In such studies main emphasis is laid to the estimation of population (location) parameters for the mean behaviour of the individuals, but besides that also interest may be in the prediction of further response for the specific individuals under investigation. Here we will indicate the problems and implications of this approach to the design of experiments and illustrate various consequences by the simple example of an exponential decay. However, it remains unsolved, what is the "correct" measure of performance of a design in this setting.
20110720T18:27:17+01:00
3473
1157475
true
16x9
false
no

Industry Day Panel Discussion
ucs_sms_125_1191841
http://sms.cam.ac.uk/media/1191841
Industry Day Panel Discussion
Lewis, S (Southampton)
Wednesday 30 November 2011, 16:3017:30
Thu, 01 Dec 2011 12:51:43 +0000
Lewis, S
Steve Greenham
Isaac Newton Institute
Lewis, S
9b35a54daf52f75d55f9f270f24bcf9e
252020aa719c75d4b1a217f3b195dc18
048410c68af0fc484215826224cac225
66e892c68a55749f956eeed49b8da13e
4f202c9279d1ad375dc822f44292cd1f
Lewis, S (Southampton)
Wednesday 30 November 2011, 16:3017:30
Lewis, S (Southampton)
Wednesday 30 November 2011, 16:3017:30
Cambridge University
3782
http://sms.cam.ac.uk/media/1191841
Industry Day Panel Discussion
Lewis, S (Southampton)
Wednesday 30 November 2011, 16:3017:30
20111202T09:36:54+00:00
3782
1191841
true
16x9
false
no

Inference and ethics in clinical trials for comparing two treatments in the presence of covariates
ucs_sms_125_1484
http://sms.cam.ac.uk/media/1484
Inference and ethics in clinical trials for comparing two treatments in the presence of covariates
Giovagnoli, A (Bologna)
Thursday 14 August 2008, 14:3015:00
Tue, 16 Sep 2008 08:57:57 +0100
Giovagnoli, A
Isaac Newton Institute
Giovagnoli, A
e35a9a608e7da663833e55ef0157b993
4799d3b6b13a345f397602483d51f0c3
8f16b30a98f62ec034bea5fd9529e045
a0109cbd6d53196a560e0676c7b88596
d33782e6b898d128fbcce85172eaf444
3a0aa12239798bc20777e0b3ce51796c
03816def060c183912b338d92b1c6b51
Giovagnoli, A (Bologna)
Thursday 14 August 2008, 14:3015:00
Giovagnoli, A (Bologna)
Thursday 14 August 2008, 14:3015:00
Cambridge University
1857