Statistical models for meta-analysis: A brief tutorial.
Statistical models for meta-analysis: A brief tutorial.
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DOI：
10.5662/wjm.v2.i4.27
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发表时间：
2012-08-26
期刊：
影响因子：
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通讯作者：
Kelley, Kristi S
中科院分区：
文献类型：
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作者：
Kelley, George A;Kelley, Kristi S
汇总数据荟萃分析是目前最常用的方法，用于结合不同研究的结果，对相同的结果感兴趣。在本文中，我们提供了一个简要的介绍荟萃分析，包括对总体和个人参与者数据荟萃分析的描述。然后，我们将把本教程的其余部分集中在聚合数据元分析上。我们首先描述固定效应和随机效应荟萃分析之间的区别，特别关注后者。接下来是一个使用随机效应矩方法的示例，包括仅截距模型以及具有一个预测因子的模型。然后，我们描述了替代的随机效应的方法，如最大似然法，限制最大似然法和配置文件的可能性，以及非参数的方法。简要描述了选定的统计程序可用于进行随机效应汇总数据荟萃分析，仅限于那些只允许截取以及至少有一个预测模型，给出。这些描述包括现有的一般统计软件包中的描述，以及专门为汇总数据荟萃分析开发的描述。在此之后，一些随机效应荟萃分析的缺点进行了说明。然后，我们描述了最近提出的替代模型进行汇总数据元分析，包括变系数模型。最后，我们对未来的研究提出了一些建议和方向。这些建议包括继续使用更常用的随机效应模型，直到新的模型得到更彻底的测试，以及及时将新的和经过良好测试的模型整合到传统的和元分析专用的软件包中。
Aggregate data meta-analysis is currently the most commonly used method for combining the results from different studies on the same outcome of interest. In this paper, we provide a brief introduction to meta-analysis, including a description of aggregate and individual participant data meta-analysis. We then focus the rest of the tutorial on aggregate data meta-analysis. We start by first describing the difference between fixed and random-effects meta-analysis, with particular attention devoted to the latter. This is followed by an example using the random-effects, method of moments approach and includes an intercept-only model as well as a model with one predictor. We then describe alternative random-effects approaches such as maximum likelihood, restricted maximum likelihood and profile likelihood as well as a non-parametric approach. A brief description of selected statistical programs available to conduct random-effects aggregate data meta-analysis, limited to those that allow both an intercept-only as well as at least one predictor in the model, is given. These descriptions include those found in an existing general statistics software package as well as one developed specifically for an aggregate data meta-analysis. Following this, some of the disadvantages of random-effects meta-analysis are described. We then describe recently proposed alternative models for conducting aggregate data meta-analysis, including the varying coefficient model. We conclude the paper with some recommendations and directions for future research. These recommendations include the continued use of the more commonly used random-effects models until newer models are more thoroughly tested as well as the timely integration of new and well-tested models into traditional as well as meta-analytic-specific software packages.