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Activity Number: 32 - Statistical Methods in Dose-Finding Studies
Type: Contributed
Date/Time: Sunday, July 28, 2019 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract #304732 Presentation
Title: Flexible Bayesian Semiparametric Designs for Dose-Finding with Multiple Populations
Author(s): Jianchang Lin* and Mo Li and Rachael Liu and Veronica Bunn and Hongyu Zhao
Companies: Takeda Pharmaceuticals and Yale University and Takeda Pharmaceuticals and Takeda Pharmaceuticals and Yale
Keywords: Bayesian semiparametric; dose-finding; multiple populations; Oncology

With the evolution in the cancer drug development, it is of increasing interest to consider multiple strata (e.g. indications, regions or subgroups) within a single dose-finding study when identifying the maximum tolerated dose (MTD). To allow for adaptively dosing patients based on various toxicity profiles and efficient identification of the MTD for each stratum, we propose two Bayesian semi-parametric models (BSD) for dose-finding with multiple strata. We develop non-parametric priors based on the Dirichlet process to allow for a flexible prior distribution and negate the need for a pre-specified exchangeability parameter. The two BSD models are built under differing prior beliefs of strata heterogeneity and allow for appropriate borrowing of information across similar strata. Simulation studies are performed to evaluate the BSD model performance by comparing with existing methods, including the fully stratified, exchangeability, and exchangeability–non-exchangeability models. In general, our BSD models outperform the competing methods in correctly identifying the MTD for different strata and necessitate a smaller sample size to determine the MTD.

Authors who are presenting talks have a * after their name.

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