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Activity Number: 7 - Bayesian Nonparametrics in Causal Inference
Type: Invited
Date/Time: Sunday, July 29, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #326860 Presentation
Title: Bayesian Population Finding Using Counterfactual Modeling of Responses
Author(s): Peter Müller* and Satoshi Morita
Companies: University of Texas Austin and Kyoto University
Keywords: nonparametric Bayes; decision theory; clinical trial

The identification of good predictive biomarkers allows investigators to optimize the target population for a new treatment. We propose a utility-based Bayesian population finding (BaPoFi) method to analyze data from a randomized clinical trial with the aim of finding a sensitive patient population. Our approach casts the problem as a formal decision problem, using a decision criterion based on a patient-specific predictive conditional treatment effect (PCTE). We evaluate enhanced treatment effects in patient subpopulations based on this counter-factual modeling of responses to new treatment and control for each patient. In extensive simulation studies, we examine the operating characteristics of the proposed method. We apply the method to a randomized clinical trial.

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

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