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Activity Number: 460 - Causal Methods for Discovery, Confirmation and Mechanistic Evaluation
Type: Contributed
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #312359
Title: A Bayesian Approach to Assessing Publication Bias in Meta-Analysis of a Binary Outcome with Controlled False Positive Rate
Author(s): Linyu Shi* and Haitao CHU and Lifeng Lin
Companies: Florida State University and the University of Minnesota Twin Cities and Florida State University
Keywords: Bayesian analysis; false positive rate; meta-analysis; publication bias; statistical power; systematic review

Publication bias (PB) is a major threat to the validity of meta-analysis. Egger’s regression test is the most widely-used tool to assess PB. It can be easily implemented and generally has satisfactory statistical power. It examines the association between the observed effect sizes and their sample standard errors (SEs) among the collected studies; a strong association indicates the presence of PB. However, Egger’s regression may have a seriously inflated false positive rate caused by this association even when no PB appears, particularly in meta-analysis of a binary outcome. Although various alternative methods are available to deal with this problem, they are powerful in specific cases and are less intuitive than Egger’s regression. This article proposes a new approach to assessing PB in meta-analyses of ORs via Bayesian hierarchical models. It reduces false positive rates by using the latent “true” SEs, rather than the observed sample SEs, to perform Egger-type regression; those “true” SEs can be feasibly estimated with Markov chain Monte Carlo algorithm. We present extensive simulations and three case studies to illustrate the performance of the proposed method.

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

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