Is the Freeman‐Tukey double arcsine transformation a reliable approach? for proportion meta-analysis
Ariel Bardach, Argentina Cochrane Center, Institute for Clinical Effectiveness and Health Policy
2Reproductive Medicine, CEGYR (Centro de Estudios en Genética y Reproducción), Argentina
Background: Proportion meta-analyses are frequently used in epidemiology to estimate the burden of disease. They are usually based on transformed proportions using Freeman‐Tukey double arcsine transformations (FTT). Schwarzer et al. generated some controversy because they considered that this method produces seriously misleading results and proposed the generalized linear mixed models (GLMM) as a more elaborate approach. However, Suhail et al., using the same set of studies, reanalyzed the data and concluded that the FTT is the most reliable approach and remains the preferred transformation in proportion meta-analysis.
Objectives: To compare the reliability and robustness of FTT with GLMM in a large set of proportion meta-analyses.
Results: Results will be shown at the colloquium.
Methods: We will conduct GLMM and FTT over a large set of proportions from a living systematic review and meta-analysis about safety, immunogenicity, and effectiveness of COVID-19 vaccines for pregnant people (https://safeinpregnancy.org/lsr/) applying recommended safeguards: a) avoiding the use of the average of the double arcsine and its variance for synthesis; b) using the inverse of the variance of the pooled FTT proportion; and c) modifying the confidence intervals to prevent numerical inaccuracies.
Conclusions: It is important to verify the best approach to undertake proportions meta-analysis, which is critical for estimations in epidemiology and decision-making. References 1. Schwarzer G, Chemaitelly H, Abu-Raddad LJ, Rücker G. Seriously misleading results using inverse of Freeman-Tukey double arcsine transformation in meta-analysis of single proportions. Res Synth Methods. 2019;10: 476–483. 2. Doi SA, Xu C. The Freeman-Tukey double arcsine transformation for the meta-analysis of proportions: Recent criticisms were seriously misleading. J Evid Based Med. 2021;14: 259–261.