This study builds on the simulation framework of a recent paper by Stanley and Doucouliagos (Research Synthesis Methods 2023;14;515–519). 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. We conclude that more research is needed before best practice recommendations can be made for meta-analyses with PCCs.

中文翻译：

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荟萃分析和偏相关系数：权重问题

这项研究建立在 Stanley 和 Doucouliagos 最近发表的一篇论文的模拟框架之上（研究综合方法2023;14;515–519）。S&D 使用模拟来论证，在构建固定效应和随机效应估计的权重时，使用偏相关系数 (PCC) 的荟萃分析应采用 PCC 标准误的“次优”估计量。我们担心他们的模拟和随后的建议可能会给元分析师留下误导性的印象。虽然他们推广的估计器在蒙特卡洛框架中主导了“正确”公式，但还有其他估计器表现得更好。我们的结论是，在为 PCC 荟萃分析提出最佳实践建议之前，需要进行更多的研究。