期刊
HEALTH SCIENCE REPORTS
卷 3, 期 3, 页码 -出版社
WILEY
DOI: 10.1002/hsr2.178
关键词
arcsine-based transformation; Bayesian model; generalized linear mixed model; meta-analysis; proportion
资金
- National Center for Advancing Translational Sciences [UL1 TR001427]
- U.S. National Library of Medicine [R01 LM012982]
Meta-analyses have been increasingly used to synthesize proportions (eg, disease prevalence) from multiple studies in recent years. Arcsine-based transformations, especially the Freeman-Tukey double-arcsine transformation, are popular tools for stabilizing the variance of each study's proportion in two-step meta-analysis methods. Although they offer some benefits over the conventional logit transformation, they also suffer from several important limitations (eg, lack of interpretability) and may lead to misleading conclusions. Generalized linear mixed models and Bayesian models are intuitive one-step alternative approaches, and can be readily implemented via many software programs. This article explains various pros and cons of the arcsine-based transformations, and discusses the alternatives that may be generally superior to the currently popular practice.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据