Evaluating meta-analytic methods to detect selective reporting in the presence of dependent effect sizes.
Evaluating meta-analytic methods to detect selective reporting in the presence of dependent effect sizes.
复制标题
评估荟萃分析方法，以检测存在依赖性效应大小的选择性报告。
作者：
Melissa A. Rodgers;J. Pustejovsky
根据统计显着性选择性报告结果会威胁荟萃分析结果的有效性。有多种技术可用于检测选择性报告、发表偏倚或小规模研究效应，并且通常用于研究综合。大多数此类技术都是单变量的，因为它们假设每项研究都为荟萃分析贡献单一的、独立的效应大小估计。然而，在实践中，研究通常会提供多种、统计相关的效应大小估计，例如对共同结果结构的多种测量。许多方法可用于荟萃分析相关效应大小，但研究选择性报告同时处理效应大小相关性的方法需要进一步研究。使用蒙特卡罗模拟，我们评估了小研究效应或选择性报告的三种可用单变量检验，包括修剪和填充检验、Egger 回归检验以及三参数选择模型 (3PSM) 的似然比检验（当依赖性被忽略或使用临时技术处理时）。我们还研究了 Egger 回归测试的两种变体，它们结合了稳健方差估计 (RVE) 或多级荟萃分析 (MLMA) 来处理依赖性。模拟结果表明，忽略依赖性会增加所有单变量测试的 I 类错误率。当使用 RVE 或 MLMA 采样或处理相关效应大小时，Egger 回归的变体保持 I 类错误率。 3PSM 似然比检验不能完全控制 I 类错误率。除了 3PSM 之外，所有方法检测选择偏差的能力都有限，除非在统计显着效应的强选择下。 （PsycInfo 数据库记录 (c) 2020 APA，保留所有权利）。
Selective reporting of results based on their statistical significance threatens the validity of meta-analytic findings. A variety of techniques for detecting selective reporting, publication bias, or small-study effects are available and are routinely used in research syntheses. Most such techniques are univariate, in that they assume that each study contributes a single, independent effect size estimate to the meta-analysis. In practice, however, studies often contribute multiple, statistically dependent effect size estimates, such as for multiple measures of a common outcome construct. Many methods are available for meta-analyzing dependent effect sizes, but methods for investigating selective reporting while also handling effect size dependencies require further investigation. Using Monte Carlo simulations, we evaluate three available univariate tests for small-study effects or selective reporting, including the trim and fill test, Egger's regression test, and a likelihood ratio test from a three-parameter selection model (3PSM), when dependence is ignored or handled using ad hoc techniques. We also examine two variants of Egger's regression test that incorporate robust variance estimation (RVE) or multilevel meta-analysis (MLMA) to handle dependence. Simulation results demonstrate that ignoring dependence inflates Type I error rates for all univariate tests. Variants of Egger's regression maintain Type I error rates when dependent effect sizes are sampled or handled using RVE or MLMA. The 3PSM likelihood ratio test does not fully control Type I error rates. With the exception of the 3PSM, all methods have limited power to detect selection bias except under strong selection for statistically significant effects. (PsycInfo Database Record (c) 2020 APA, all rights reserved).