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ABCMETAapp: R shiny application for simulation‐based estimation of mean and standard deviation for meta‐analysis via approximate Bayesian computation
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 li...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8596912/ https://www.ncbi.nlm.nih.gov/pubmed/34148300 http://dx.doi.org/10.1002/jrsm.1505 |
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author | Kwon, Deukwoo Reddy, Roopesh Reddy Sadashiva Reis, Isildinha M. |
author_facet | Kwon, Deukwoo Reddy, Roopesh Reddy Sadashiva Reis, Isildinha M. |
author_sort | Kwon, Deukwoo |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8596912 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85969122021-11-22 ABCMETAapp: R shiny application for simulation‐based estimation of mean and standard deviation for meta‐analysis via approximate Bayesian computation Kwon, Deukwoo Reddy, Roopesh Reddy Sadashiva Reis, Isildinha M. Res Synth Methods Computational Tools and Methods 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. John Wiley and Sons Inc. 2021-06-24 2021-11 /pmc/articles/PMC8596912/ /pubmed/34148300 http://dx.doi.org/10.1002/jrsm.1505 Text en © 2021 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Computational Tools and Methods Kwon, Deukwoo Reddy, Roopesh Reddy Sadashiva Reis, Isildinha M. ABCMETAapp: R shiny application for simulation‐based estimation of mean and standard deviation for meta‐analysis via approximate Bayesian computation |
title |
ABCMETAapp: R shiny application for simulation‐based estimation of mean and standard deviation for meta‐analysis via approximate Bayesian computation |
title_full |
ABCMETAapp: R shiny application for simulation‐based estimation of mean and standard deviation for meta‐analysis via approximate Bayesian computation |
title_fullStr |
ABCMETAapp: R shiny application for simulation‐based estimation of mean and standard deviation for meta‐analysis via approximate Bayesian computation |
title_full_unstemmed |
ABCMETAapp: R shiny application for simulation‐based estimation of mean and standard deviation for meta‐analysis via approximate Bayesian computation |
title_short |
ABCMETAapp: R shiny application for simulation‐based estimation of mean and standard deviation for meta‐analysis via approximate Bayesian computation |
title_sort | abcmetaapp: r shiny application for simulation‐based estimation of mean and standard deviation for meta‐analysis via approximate bayesian computation |
topic | Computational Tools and Methods |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8596912/ https://www.ncbi.nlm.nih.gov/pubmed/34148300 http://dx.doi.org/10.1002/jrsm.1505 |
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