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Activity Number: 247 - Causal Inference and Statistical Learning of Intervention and Policy Effects
Type: Contributed
Date/Time: Wednesday, August 11, 2021 : 10:00 AM to 11:50 AM
Sponsor: Health Policy Statistics Section
Abstract #317789
Title: Bridging Randomized Controlled Trials and Single-Arm Trials Using Commensurate Priors in Arm-Based Network Meta-Analysis
Author(s): Zhenxun Wang* and Lifeng Lin and Thomas Murray and James Hodges and Haitao Chu
Companies: University of Minnesota Twin Cities and Florida State University and Division of Biostatistics, University of Minnesota and University of Minnesota Twin Cities and University of Minnesota Twin Cities
Keywords: Bayesian inference; commensurate prior; network meta-analysis; randomized controlled trial; single-arm trial

Network meta-analysis (NMA) is a powerful tool to compare multiple treatments directly and indirectly by combining and contrasting multiple independent clinical trials. Because many NMAs collect only a few eligible randomized controlled trials (RCTs), there is an urgent need to synthesize different sources of information, e.g., from both RCTs and single-arm trials. However, single-arm trials and RCTs may have different populations and quality, so that assuming they are exchangeable may be inappropriate. This article presents a novel method using a commensurate prior on variance (CPV) to borrow variance (rather than mean) information from single-arm trials in an arm-based (AB) Bayesian NMA. We illustrate the advantages of this CPV method by reanalyzing an NMA of immune checkpoint inhibitors in cancer patients. Comprehensive simulations investigate the impact on statistical inference of including single-arm trials. The simulation results show that the CPV method provides efficient and robust estimation even when the two sources of information are moderately inconsistent.

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

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