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Activity Number: 677
Type: Topic Contributed
Date/Time: Thursday, August 13, 2015 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #316706
Title: Bayesian Dose-Finding Designs for Combination of Molecularly Targeted Agents Assuming Partial Stochastic Ordering
Author(s): Beibei Guo and Yisheng Li*
Companies: Louisiana State University and MD Anderson Cancer Center
Keywords: Bayesian isotonic regression transformation ; dose-efficacy surface ; dose-toxicity surface ; matrix ordering ; plateau ; post processing
Abstract:

Molecularly targeted agent (MTA) combination therapy is in the early stages of development. When using a fixed dose of one agent in combinations of MTAs, toxicity and efficacy do not necessarily increase with an increasing dose of the other agent. Thus, in dose-finding trials for combinations of MTAs, interest may lie in identifying the optimal biological dose combinations (OBDCs), defined as the lowest dose combinations (in a certain sense) that are safe and have the highest efficacy level meeting a prespecified target. The limited existing designs for these trials use parametric dose-efficacy and dose-toxicity models. Motivated by a phase I/II clinical trial of a combination of two MTAs in patients with pancreatic, endometrial, or colorectal cancer, we propose Bayesian dose-finding designs to identify the OBDCs without parametric model assumptions. The proposed approach is based only on partial stochastic ordering assumptions for the effects of the combined MTAs and uses isotonic regression to estimate partially stochastically ordered marginal posterior distributions of the efficacy and toxicity probabilities. We demonstrate that our proposed method appropriately accounts for the


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