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Background and Objective: In meta-analysis based on continuous outcome,estimated means and corresponding standard deviations from the selected studiesare key inputs to obtain a pooled estimate of the mean and its confidenceinterval. We often encounter the situation that these quantities are notdirectly reported in the literatures. Instead, other summary statistics arereported such as median, minimum, maximum, quartiles, and study sample size.Based on available summary statistics, we need to estimate estimates of meanand standard deviation for meta-analysis. Methods: We developed a R Shiny codebased on approximate Bayesian computation (ABC), ABCMETA, to deal with thissituation. Results: In this article, we present an interactive anduser-friendly R Shiny application for implementing the proposed method (namedABCMETAapp). In ABCMETAapp, users can choose an underlying outcome distributionother than the normal distribution when the distribution of the outcomevariable is skewed or heavy tailed. We show how to run ABCMETAapp withexamples. Conclusions: ABCMETAapp provides a R Shiny implementation. Thismethod is more flexible than the existing analytical methods since estimationcan be based on five different distribution (Normal, Lognormal, Exponential,Weibull, and Beta) for the outcome variable.

ABCMETAapp: R Shiny Application for Simulation-based Estimation of Mean  and Standard Deviation for Meta-analysis via Approximate Bayesian Computation  (ABC)