Methods to calculate uncertainty in the estimated overall effect size from a random-effects meta-analysis
Methods to calculate uncertainty in the estimated overall effect size from a random-effects meta-analysis
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DOI：
10.1002/jrsm.1319
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发表时间：
2019-03-01
影响因子：
9.8
通讯作者：
Salantil, Georgia
中科院分区：
文献类型：
--
作者：
Veroniki, Areti Angeliki;Jackson, Dan;Salantil, Georgia
荟萃分析是系统评价中的一个重要工具，用于估计感兴趣结局的总体效应量及其置信区间。如果预期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。在这个模型中，新研究中真实效果的预测区间也提供了额外的有用信息。然而，DerSimonian和Laird方法-经常被用作随机效应荟萃分析的默认方法-由于其不利的统计特性而长期受到挑战。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。在本文中，我们的目的是提供一个全面的概述，用于计算点估计，置信区间和预测区间的随机效应模型下的总体效应量的可用方法。我们表明是否有些方法比其他人更可取，通过考虑比较模拟和现实生活中的数据研究的结果。
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.