Outlier and influence diagnostics for meta-analysis

TLDR

Outliers and influential cases can compromise the validity of meta‑analysis conclusions, yet few studies detail how to compute such diagnostics. This study extends linear‑regression diagnostic methods to fixed‑ and random‑effects meta‑analysis models. The authors demonstrate the procedures with three examples and discuss related issues in meta‑analysis. © 2010 John Wiley & Sons, Ltd.

Abstract

The presence of outliers and influential cases may affect the validity and robustness of the conclusions from a meta-analysis. While researchers generally agree that it is necessary to examine outlier and influential case diagnostics when conducting a meta-analysis, limited studies have addressed how to obtain such diagnostic measures in the context of a meta-analysis. The present paper extends standard diagnostic procedures developed for linear regression analyses to the meta-analytic fixed- and random/mixed-effects models. Three examples are used to illustrate the usefulness of these procedures in various research settings. Issues related to these diagnostic procedures in meta-analysis are also discussed. Copyright © 2010 John Wiley & Sons, Ltd.