Five electronic databases were searched by 29 April 2022, including representative samples of community-dwelling adults who were aged 60 years and older and lived in Asia. This study aimed to synthesize frailty prevalence among community-dwelling older adults in Asia and identify factors influencing prevalence estimates. Generic R code is provided for readers to use for their analyses. The presentation of the tutorial is conceptually oriented, the use of formulas is kept to a minimum, and a published meta-analysis of proportions is used as an example to illustrate how to implement the R code and interpret the results of the analysis. Three computational options are incorporated in the code for users to choose from to transform proportional data. In the last section (assessing publication bias), we argued that funnel plot analyses developed for investigating publication bias in randomized controlled trials may not be suitable for use with meta-analyses of proportions. The process is described in six stages: (1) setting up the R environment and getting a sense of the data being analyzed (2) calculating effect sizes (3) identifying and quantifying heterogeneity (4) constructing forest plots (5) explaining heterogeneity with moderator analysis and (6) assessing publication bias. The tutorial consists of two major components: (1) a comprehensive, critical review of the process of conducting a meta-analysis of proportions, in which a number of common practices that possibly lead to biased estimates and misleading inferences are highlighted (e.g., not taking study size and within-group estimates of between-study variance into consideration when calculating mean proportions in the presence of subgroups), and (2) a step-by-step guide to conducting the analysis using R.
Rarely have we seen a study or tutorial demonstrate how a meta-analysis of proportions should be performed using the R programming language. Meta-analysis of proportions is observational and non-comparative in nature.
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