ABCMETAapp: R shiny application for simulation‐based estimation of mean and standard deviation for meta‐analysis via approximate Bayesian computation. (24th June 2021)

• Record Type: Journal Article
• Authors: Kwon, Deukwoo; Reddy, Roopesh Reddy Sadashiva; Reis, Isildinha M.
• Is Part Of: Research synthesis methods. Volume 12:Number 6(2021)
• Page Start: 842
• Page End: 848
• Publication Date: 2021-06-24
• Subjects: approximate Bayesian computation -- meta‐analysis -- R shiny application -- sample mean -- sample standard deviation; Research -- Methodology -- Periodicals; Research -- Statistical methods -- Periodicals; 507.2
• DOI: 10.1002/jrsm.1505
• Languages: English
• ISSNs: 1759-2879

Abstract

Abstract: In meta‐analysis based on continuous outcome, estimated means and corresponding standard deviations from the selected studies are key inputs to obtain a pooled estimate of the mean and its confidence interval. We often encounter the situation that these quantities are not directly reported in the literatures. Instead, other summary statistics are reported such as median, minimum, maximum, quartiles, and study sample size. Based on available summary statistics, we need to estimate estimates of mean and standard deviation for meta‐analysis. We developed an R Shiny code based on approximate Bayesian computation (ABC), ABCMETA, to deal with this situation. In this article, we present an interactive and user‐friendly R Shiny application for implementing the proposed method (named ABCMETAapp). In ABCMETAapp, users can choose an underlying outcome distribution other than the normal distribution when the distribution of the outcome variable is skewed or heavy tailed. We show how to run ABCMETAapp with examples. ABCMETAapp provides an R Shiny implementation. This method is more flexible than the existing analytical methods since estimation can be based on five different distributions (Normal, Lognormal, Exponential, Weibull, and Beta) for the outcome variable.