New models for describing outliers in meta-analysis.
New models for describing outliers in meta-analysis.
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DOI：
10.1002/jrsm.1191
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发表时间：
2016-09
影响因子：
9.8
通讯作者：
Jackson D
中科院分区：
文献类型：
--
作者：
Baker R;Jackson D
未观察到的随机效应通常用于描述Meta分析数据集中明显的研究间差异。正态分布的随机效应通常用于此目的。当分析中包含离群值或其他异常估计值时，先前已提出使用替代随机效应分布。我们没有采用通常的分层方法来建模研究间变异，而是直接建模研究特定的真实基础效应，我们提出了两个新的边际分布来建模异质数据集。建议使用这两种分布，因为不需要数值积分来评估可能性。这使得拟合模型时所需的计算更加稳健。新的分布的属性进行了描述，并举例说明拟合模型的四个数据集的方法。© 2015作者。由John Wiley & Sons，Ltd.出版的研究合成方法。
An unobserved random effect is often used to describe the between‐study variation that is apparent in meta‐analysis datasets. A normally distributed random effect is conventionally used for this purpose. When outliers or other unusual estimates are included in the analysis, the use of alternative random effect distributions has previously been proposed. Instead of adopting the usual hierarchical approach to modelling between‐study variation, and so directly modelling the study specific true underling effects, we propose two new marginal distributions for modelling heterogeneous datasets. These two distributions are suggested because numerical integration is not needed to evaluate the likelihood. This makes the computation required when fitting our models much more robust. The properties of the new distributions are described, and the methodology is exemplified by fitting models to four datasets. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.