Arcsine‐based transformations for meta‐analysis of proportions: Pros, cons, and alternatives

• Main Authors: Lifeng Lin, Chang Xu
• Format: Article
• Language: English
• Published: Wiley 2020-09-01
• Series: Health Science Reports
• Subjects: arcsine‐based transformation; Bayesian model; generalized linear mixed model; meta‐analysis; proportion
• Online Access: https://doi.org/10.1002/hsr2.178

Abstract 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.