Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada.
Department of Primary Education, School of Education, University of Ioannina, Ioannina, Greece.
Res Synth Methods. 2019 Mar;10(1):23-43. doi: 10.1002/jrsm.1319. Epub 2018 Oct 9.
Meta-analyses are an important tool within systematic reviews to estimate the overall effect size and its confidence interval for an outcome of interest. If heterogeneity between the results of the relevant studies is anticipated, then a random-effects model is often preferred for analysis. In this model, a prediction interval for the true effect in a new study also provides additional useful information. However, the DerSimonian and Laird method-frequently used as the default method for meta-analyses with random effects-has been long challenged due to its unfavorable statistical properties. Several alternative methods have been proposed that may have better statistical properties in specific scenarios. In this paper, we aim to provide a comprehensive overview of available methods for calculating point estimates, confidence intervals, and prediction intervals for the overall effect size under the random-effects model. We indicate whether some methods are preferable than others by considering the results of comparative simulation and real-life data studies.
荟萃分析是系统评价中用于估计感兴趣结局的总体效应大小及其置信区间的重要工具。如果预计相关研究结果之间存在异质性，则通常首选随机效应模型进行分析。在该模型中，对新研究中真实效应的预测区间也提供了额外的有用信息。然而，由于其不利的统计特性，德西曼和莱尔德方法（经常作为具有随机效应的荟萃分析的默认方法）长期以来一直受到挑战。已经提出了几种替代方法，它们在特定情况下可能具有更好的统计特性。本文旨在全面概述在随机效应模型下计算总体效应大小的点估计、置信区间和预测区间的可用方法。我们通过考虑比较模拟和真实数据研究的结果来表明某些方法是否比其他方法更可取。
