How Robust Are Meta-Analyses to Publication Bias? Sensitivity Analysis Methods and Empirical Findings

The Department of Epidemiology Seminar Series Presents

Speaker:

Maya Mathur, PhD
Assistant Professor, Quantitative Sciences Unit and
the Department of Pediatrics, Stanford University

Abstract: Publication bias can distort meta-analytic results, sometimes justifying considerable skepticism toward meta-analyses. This talk will discuss recently developed statistical sensitivity analyses for publication bias, which enable statements such as: “For publication bias to shift the observed point estimate to the null, significant results would need to be at least 10-fold more likely to be published than negative or non-significant results” or “no amount of publication bias could explain away the average effect.” Additionally, a meta-analytic point estimate corrected for “worst-case” publication bias can be obtained simply by conducting a standard meta-analysis of only the negative and nonsignificant studies; this method sometimes indicates that no amount of such publication bias could explain away the results. I will describe the results of applying the methods to a systematic sample of 58 meta-analyses across multiple scientific disciplines. All methods are implemented in the R package PublicationBias.

Bio: Maya Mathur is an Assistant Professor at the Stanford University Quantitative Sciences Unit and the Associate Director of the Stanford Center for Open and Reproducible Science. She is a statistician whose methodological research focuses on advancing methods for meta-analysis, replication studies, and sensitivity analysis.