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Activity Number: 332 - Synthetic Clinical Trials Design to Accelerate FDA Approvals
Type: Invited
Date/Time: Thursday, August 12, 2021 : 10:00 AM to 11:50 AM
Sponsor: Biopharmaceutical Section
Abstract #316825
Title: Incorporating External Data into the Analysis of Clinical Trials via Bayesian Additive Regression Trees
Author(s): Tianjian Zhou* and Yuan Ji
Companies: Colorado State University and The University of Chicago
Keywords: Bayesian inference; Hierarchical model; Historical control; Real-world data; Treatment effect

Most clinical trials involve the comparison of a new treatment to a control arm (e.g., the standard of care) and the estimation of a treatment effect. External data, including historical clinical trial data and real-world observational data, are commonly available for the control arm. Borrowing information from external data holds the promise of improving the estimation of relevant parameters and increasing the power of detecting a treatment effect if it exists. In this paper, we propose to use Bayesian additive regression trees (BART) for incorporating external data into the analysis of clinical trials, with a specific goal of estimating the conditional or population average treatment effect. BART naturally adjusts for patient-level covariates and captures potentially heterogeneous treatment effects across different data sources, achieving flexible borrowing. Simulation studies demonstrate that BART compares favorably to a hierarchical linear model and a normal-normal hierarchical model. We illustrate the proposed method with an acupuncture trial.

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

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