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Activity Number: 522 - Contributed Poster Presentations: Biometrics Section
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #303007
Title: Sensitivity Analysis for Publication Bias in Meta-Analyzes
Author(s): Maya B Mathur* and Tyler VanderWeele
Companies: Harvard University and Harvard University
Keywords: publication bias; meta-analysis; file drawer; sensitivity analysis

We propose sensitivity analyses for meta-analyses in which “statistically significant” positive results are more likely to be published than negative or “nonsignificant” results by an unknown ratio. Using inverse probability weighting and accommodating non-normal true effects, small meta-analyses, and clustering, we develop sensitivity analyses enabling statements such as: “For publication bias to shift the observed point estimate to the null, positive results would need to be at least 30-fold more likely to be published than negative or ‘nonsignificant’ results.” Comparable statements can be made regarding shifting to a chosen non-null value or shifting the confidence interval. To aid interpretation, we empirically benchmark plausible values of the publication ratio across disciplines. We show that a worst-case meta-analytic point estimate under maximal publication bias can be obtained simply by conducting a standard meta-analysis of only the negative and “nonsignificant” studies; this sometimes indicates that no amount of publication bias could “explain away” the results. We illustrate the proposed methods using real meta-analyses and provide an R package, PublicationBias.

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

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