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ABCMETAapp : R shiny application for simulation‐based estimation of mean and standard deviation for meta‐analysis via approximate Bayesian computation
Author(s) -
Kwon Deukwoo,
Reddy Roopesh Reddy Sadashiva,
Reis Isildinha M.
Publication year - 2021
Publication title -
research synthesis methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.376
H-Index - 35
eISSN - 1759-2887
pISSN - 1759-2879
DOI - 10.1002/jrsm.1505
Subject(s) - standard deviation , statistics , quartile , confidence interval , weibull distribution , log normal distribution , outcome (game theory) , computer science , variable (mathematics) , sample size determination , computation , normal distribution , bayesian probability , interval estimation , exponential distribution , standard error , mathematics , algorithm , mathematical economics , mathematical analysis
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.