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ABCMETAapp: R shiny application for simulation-based estimation of mean and standard deviation for meta-analysis via
Deukwoo Kwon1,2, Roopesh Reddy Sadashiva Reddy1, Isildinha M Reis1,2
1Sylvester Comprehensive Cancer Center, University of Miami, Miami, Florida, USA.
This study introduces ABCMETAapp, an R Shiny application for estimating means and standard deviations in meta-analysis when data are incomplete. It uses approximate Bayesian computation (ABC) and supports various outcome distributions for enhanced flexibility.
Area of Science:
- Biostatistics
- Medical Informatics
- Computational Statistics
Background:
- Meta-analysis of continuous outcomes requires means and standard deviations, which are often not directly reported in studies.
- Alternative summary statistics like median, quartiles, and range are frequently available, necessitating methods for estimating required parameters.
- Existing analytical methods for estimating these parameters can be inflexible regarding outcome variable distributions.
Purpose of the Study:
- To develop and present an interactive R Shiny application, ABCMETAapp, for estimating means and standard deviations from available summary statistics.
- To implement a novel method based on approximate Bayesian computation (ABC) for robust parameter estimation in meta-analysis.
- To provide a user-friendly tool that accommodates various outcome distributions beyond the normal distribution.
Main Methods:
- Development of an R Shiny application named ABCMETAapp.
- Utilized approximate Bayesian computation (ABC) for estimating means and standard deviations.
- Incorporated flexibility by allowing users to select from five different outcome distributions: Normal, Lognormal, Exponential, Weibull, and Beta.
Main Results:
- ABCMETAapp provides an interactive and user-friendly interface for applying the ABCMETA method.
- The application successfully estimates means and standard deviations from commonly reported summary statistics.
- Demonstrated the application's utility through illustrative examples, showcasing its practical implementation.
Conclusions:
- ABCMETAapp offers a flexible and accessible solution for meta-analysis when standard deviations and means are not directly reported.
- The ability to account for non-normal outcome distributions enhances the accuracy and applicability of meta-analytic findings.
- This tool facilitates more comprehensive meta-analyses by leveraging a wider range of reported summary statistics.
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