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Bayesian Adaptive Designs for Identifying Maximum Tolerated Combinations of Two Agents
http://sms.cam.ac.uk/media/1165739
Braun, T (Michigan)
Monday 15 August 2011, 15:2516:05
40
Bayesian Adaptive Designs for Identifying Maximum Tolerated Combinations of Two Agents
http://sms.cam.ac.uk/media/1165739
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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
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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.
Steve Greenham
http://sms.cam.ac.uk/person/sg438
20110818T08:35:07+01:00
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