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Bayesian Adaptive Designs for Identifying Maximum Tolerated Combinations of Two Agents

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If you have a question about this talk, please contact Mustapha Amrani.

Design and Analysis of Experiments

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 dose-limiting 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 follow-up 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.

This talk is part of the Isaac Newton Institute Seminar Series series.

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