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Activity Number:
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536
- Advanced Bayesian Methods for Modern Clinical Trials
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Type:
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Invited
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Date/Time:
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Thursday, August 11, 2022 : 10:30 AM to 12:20 PM
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Sponsor:
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International Society for Bayesian Analysis (ISBA)
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Abstract #320687
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Title:
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Bayesian Nonparametric Common Atoms Regression for Generating Synthetic Controls in Clinical Trials
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Author(s):
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Peter Mueller* and Noirrit Kiran Chandra and Abhra Sarkar
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Companies:
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The University of Texas at Austin and The University of Texas at Austin and The University of Texas at Austin
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Keywords:
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real world data;
nonparametric Bayes;
clinical trial
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Abstract:
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We develop a Bayesian nonparametric approach for creating synthetic controls from real world data (RWD) to supplement treatment-only single arm trials. We introduce a Bayesian common atoms regression model that clusters covariates with similar values across different treatment arms. Exploiting the common atoms structure, we propose a density free importance sampling scheme to sample a subpopulation of the RWD such that the covariates in the subpopulation have the same distribution as the actual patients, allowing for a valid treatment comparison. Inference under the proposed common atoms mixture model can be characterized as a stochastic stratification by propensity score (for selection into control or treatment arm). The proposed design is implemented for glioblastoma trials.
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Authors who are presenting talks have a * after their name.
