Random-effects meta-analysis: the number of studies matters
Random-effects meta-analysis: the number of studies matters
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DOI：
10.1177/0962280215583568
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发表时间：
2017-06-01
影响因子：
2.3
通讯作者：
Varin, Cristiano
中科院分区：
文献类型：
--
作者：
Guolo, Annamaria;Varin, Cristiano
本文探讨了随机效应模型框架内研究数量对荟萃分析和荟萃回归的影响。经常被忽视的是，在随机效应模型的推理需要大量的研究纳入荟萃分析，以保证可靠的结论。几位作者警告说，传统的DerSimonian和Laird方法存在结果不准确的风险，特别是在涉及有限数量研究的荟萃分析的常见情况下。本文提出了一种选择的可能性和非可能性的方法进行推断的荟萃分析提出，以克服DerSimonian和Laird程序的局限性，重点是研究的数量的影响。的适用性和性能的方法进行了调查，在第一类错误率和经验的力量来检测效果，根据实际利益的情况。模拟研究和应用真实的荟萃分析强调，它是不可能的，以确定一种方法一致上级的替代品。总体建议是避免DerSimonian和Laird方法时，荟萃分析研究的数量是适度的，更喜欢一个更全面的程序，比较替代的推理方法。根据本文中检查的所有推理方法，提供了荟萃分析的R代码。
This paper investigates the impact of the number of studies on meta-analysis and meta-regression within the random-effects model framework. It is frequently neglected that inference in random-effects models requires a substantial number of studies included in meta-analysis to guarantee reliable conclusions. Several authors warn about the risk of inaccurate results of the traditional DerSimonian and Laird approach especially in the common case of meta-analysis involving a limited number of studies. This paper presents a selection of likelihood and non-likelihood methods for inference in meta-analysis proposed to overcome the limitations of the DerSimonian and Laird procedure, with a focus on the effect of the number of studies. The applicability and the performance of the methods are investigated in terms of Type I error rates and empirical power to detect effects, according to scenarios of practical interest. Simulation studies and applications to real meta-analyses highlight that it is not possible to identify an approach uniformly superior to alternatives. The overall recommendation is to avoid the DerSimonian and Laird method when the number of meta-analysis studies is modest and prefer a more comprehensive procedure that compares alternative inferential approaches. R code for meta-analysis according to all of the inferential methods examined in the paper is provided.