Meta-Analysis and Partial Correlation Coefficients: A Matter of Weights

This study builds on the simulation framework of a recent paper by Stanley & Doucouliagos (2023). S&D use simulations to make the argument that meta-analyses using partial correlation coefficients (PCCs) should employ a suboptimal estimator of the PCC standard error when constructing weights for fixed effect and random effects estimation. We address concerns that their simulations and subsequent recommendation may give meta-analysts a misleading impression. While the estimator they promote dominates the “correct” formula in their Monte Carlo framework, there are other estimators that perform even better. In addition, their simulations ignore how standard errors are used to test for publication bias. We show how the standard error estimator they recommend gives unsatisfactory results when used in Egger regressions/FAT-PET analyses. We conclude that more research is needed before best practice recommendations can be made for meta-analyses with PCCs.