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AuthorLin, Lifeng
AuthorXu, Chang
Available date2021-02-07T10:13:55Z
Publication Date2020-09-01
Publication NameHealth Science Reports
Identifierhttp://dx.doi.org/10.1002/hsr2.178
CitationLin, L, Xu, C. Arcsine‐based transformations for meta‐analysis of proportions: Pros, cons, and alternatives. Health Sci Rep. 2020; 9999:e178. https://doi.org/10.1002/hsr2.178
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85091010819&origin=inward
URIhttp://hdl.handle.net/10576/17560
Abstract© 2020 The Authors. Health Science Reports published by Wiley Periodicals LLC. 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.
Languageen
PublisherWiley Open Access
Subjectarcsine-based transformation
Bayesian model
generalized linear mixed model
meta-analysis
proportion
TitleArcsine-based transformations for meta-analysis of proportions: Pros, cons, and alternatives
TypeArticle
Issue Number3
Volume Number3
ESSN2398-8835
dc.accessType Open Access


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