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