A basic introduction to fixed-effect and random-effects models for meta-analysis
A basic introduction to fixed-effect and random-effects models for meta-analysis
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DOI：
10.1002/jrsm.12
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发表时间：
2010-04-01
影响因子：
9.8
通讯作者：
Rothstein, Hannah R.
中科院分区：
文献类型：
--
作者：
Borenstein, Michael;Hedges, Larry V.;Rothstein, Hannah R.
有两种常用的元分析统计模型，固定效应模型和随机效应模型。这两种模型采用相似的公式集来计算统计数据，有时对各种参数产生相似的估计，这一事实可能使人们相信这两种模型是可以互换的。然而，事实上，这些模型代表了对数据的根本不同的假设。选择合适的模型对于确保正确估计各种统计数据非常重要。此外，更基本的是，模型用于将分析置于上下文中。它为分析的目标以及统计数据的解释提供了一个框架。在本文中，我们解释了每个模型的关键假设，然后概述了模型之间的差异。最后，我们讨论了在两种模型之间进行选择时要考虑的因素。版权所有John Wiley & Sons， Ltd。
There are two popular statistical models for meta-analysis, the fixed-effect model and the random-effects model. The fact that these two models employ similar sets of formulas to compute statistics, and sometimes yield similar estimates for the various parameters, may lead people to believe that the models are interchangeable. In fact, though, the models represent fundamentally different assumptions about the data. The selection of the appropriate model is important to ensure that the various statistics are estimated correctly. Additionally, and more fundamentally, the model serves to place the analysis in context. It provides a framework for the goals of the analysis as well as for the interpretation of the statistics.In this paper we explain the key assumptions of each model, and then outline the differences between the models. We conclude with a discussion of factors to consider when choosing between the two models. Copyright (C) 2010 John Wiley & Sons, Ltd.