Using odds ratios as effect sizes for meta-analysis of dichotomous data: A primer on methods and issues
Using odds ratios as effect sizes for meta-analysis of dichotomous data: A primer on methods and issues
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DOI：
10.1037/1082-989x.3.3.339
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发表时间：
1998-09-01
影响因子：
7
通讯作者：
Shadish, WR
中科院分区：
文献类型：
--
作者：
Haddock, CK;Rindskopf, D;Shadish, WR
许多元分析师错误地使用相关性或标准化均值差统计量来计算二分数据的效应量。比值比和他们的算法应该几乎总是首选这样的数据本文回顾了这些问题，并显示了如何使用比值比在荟萃分析数据，无论是单独和与其他效应量估计。示例说明了估计这些效应量的加权平均值的程序以及计算方差估计值、置信区间和同质性检验的方法。固定和随机效应模型的描述有助于确定效应大小是否是研究特征的函数，并描述了以前未用于比值比数据的随机效应回归模型。尽管除了后者之外，所有这些程序在医学和流行病学等领域都已经广为人知，但它们在心理学中的缺乏表明需要这种描述。
Many meta-analysts incorrectly use correlations or standardized mean difference statistics to compute effect sizes on dichotomous data. Odds ratios and their logarithms should almost always be preferred for such data This article reviews the issues and shows how to use odds ratios in meta-analytic data, both alone and in combination with other effect size estimators. Examples illustrate procedures for estimating the weighted average of such effect sizes and methods for computing variance estimates, confidence intervals, and homogeneity tests. Descriptions of fixed- and random-effects models help determine whether effect sizes are functions of study characteristics, and a random-effects regression model, previously unused for odds ratio data, is described. Although all but the latter of these procedures are already widely known in areas such as medicine and epidemiology, the absence of their use in psychology suggests a need for this description.