Dealing with effect size multiplicity in systematic reviews and meta-analyses
Dealing with effect size multiplicity in systematic reviews and meta-analyses
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DOI：
10.1002/jrsm.1310
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发表时间：
2018-09-01
影响因子：
9.8
通讯作者：
Higgins, Julian P. T.
中科院分区：
文献类型：
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作者：
Lopez-Lopez, Jose A.;Page, Matthew J.;Higgins, Julian P. T.
系统评价经常遇到基于相同参与者的数据报告多个效应量的主要研究。这些有可能将统计依赖性引入元分析数据集。在本文中，我们提供了一个教程，在干预和关联研究的荟萃分析的背景下处理研究中的效应量多重性，推荐了一个三步法。第一步是定义研究问题，并考虑它在多大程度上主要反映了对平均效应量的兴趣（我们称之为收敛方法）或对探索异质性的兴趣（我们称之为发散方法）。第二步是识别与研究问题相关的效应量的初始数据库中出现的多重性类型，并提出一个分类方案来区分它们。第三步是选择一种策略来处理每种类型的多样性。研究人员可以选择还原主义的荟萃分析方法，其特点是每个研究包含一个单一的效应量，和一个综合的方法，其特点是每个研究包含多个效应量。我们提出了一个可用的分析策略，处理研究中的效应量多重性的概述，并提供建议，旨在帮助研究人员决定哪种策略可能是更可取的，在特定情况下。最后，我们提出了关于应对多重性对系统评价和荟萃分析带来的挑战的警告和注意事项。
Systematic reviews often encounter primary studies that report multiple effect sizes based on data from the same participants. These have the potential to introduce statistical dependency into the meta-analytic data set. In this paper, we provide a tutorial on dealing with effect size multiplicity within studies in the context of meta-analyses of intervention and association studies, recommending a three-step approach. The first step is to define the research question and consider the extent to which it mainly reflects interest in mean effect sizes (which we term a convergent approach) or an interest in exploring heterogeneity (which we term a divergent approach). A second step is to identify the types of multiplicities that appear in the initial database of effect sizes relevant to the research question, and we propose a categorization scheme to differentiate them. The third step is to select a strategy for dealing with each type of multiplicity. The researcher can choose between a reductionist meta-analytic approach, which is characterized by inclusion of a single effect size per study, and an integrative approach, characterized by inclusion of multiple effect sizes per study. We present an overview of available analysis strategies for dealing with effect size multiplicity within studies and provide recommendations intended to help researchers decide which strategy might be preferable in particular situations. Last, we offer caveats and cautions about addressing the challenges multiplicity poses for systematic reviews and meta-analyses.