Bayesian Hierarchical Methods for Meta-Analysis Combining Randomized-Controlled and Single-Arm Studies

• Activity Number: 652 - Recent Innovation in Generalized Evidence Synthesis
• Type: Topic Contributed
• Date/Time: Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Jing Zhang* and Chia-Wen Ko and Lei Nie and Yong Chen and Ram Tiwari
• Companies: University of Maryland College Park and U.S. Food and Drug Administration and Division of Biometrics V, office of Biostatistics, CDER/FDA and University of Pennsylvania and Center for Devices and Radiologica Health, FDA
• Keywords: Evidence synthesis; Different types of studies; bias; efficiency; downweighting; MCMC

Abstract

Meta-analysis of interventions usually relies on randomized controlled trials (RCTs). However, when the dominant source of information comes from single-arm studies, or when the results from RCTs lack generalization due to strict inclusion and exclusion criteria, it is vital to synthesize both sources of evidence. One challenge of synthesizing both sources is that single-arm studies are usually less reliable than RCTs due to selection bias and confounding factors. In this paper, we propose a Bayesian hierarchical framework for the purpose of bias reduction and efficiency gain. Design difference and potential biases are considered the proposed models. We illustrate our methods by applying all models to two motivating datasets and evaluate their performance through simulation studies. We finish with a discussion of the advantages and limitations of our methods, as well as directions for future research in this area.