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How robust are meta-analyses to publication bias? Sensitivity analysis methods and empirical findings
mardi 07 mars 2023

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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.” The methods are based on inverse-probability weighted estimators and use robust estimation methods to accommodate non-normal population effects, small meta-analyses, and clustering. 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.

Support en PDF de la conférence de Maya Mathur.

Conférence de Maya Mathur ((Stanford University) dans le cadre du Colloquium du département d'Etudes Cognitives de l'ENS-PSL.


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Maya Mathur
Stanford university

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Maya Mathur est professeure adjointe à l'unité des sciences quantitatives et au département de pédiatrie de l'université de Stanford. Elle est directrice associée du Stanford Data Science's Center for Open and Reproducible Science (CORES).

Elle est statisticienne et ses recherches méthodologiques portent sur la méta-analyse et d'autres formes de synthèse des données, ainsi que sur l'inférence causale. Elle a reçu des prix de la Society for Epidemiologic Research (2022), de la Society for Research Synthesis Methods (2022) et de l'American Statistical Association (2018).

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Dernière mise à jour : 07/04/2023