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Simulation-based estimation of mean and standard deviation for meta-analysis via Approximate Bayesian Computation (ABC)

BACKGROUND: When conducting a meta-analysis of a continuous outcome, estimated means and standard deviations from the selected studies are required in order to obtain an overall estimate of the mean effect and its confidence interval. If these quantities are not directly reported in the publications...

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Autores principales: Kwon, Deukwoo, Reis, Isildinha M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4542106/
https://www.ncbi.nlm.nih.gov/pubmed/26264850
http://dx.doi.org/10.1186/s12874-015-0055-5
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author Kwon, Deukwoo
Reis, Isildinha M.
author_facet Kwon, Deukwoo
Reis, Isildinha M.
author_sort Kwon, Deukwoo
collection PubMed
description BACKGROUND: When conducting a meta-analysis of a continuous outcome, estimated means and standard deviations from the selected studies are required in order to obtain an overall estimate of the mean effect and its confidence interval. If these quantities are not directly reported in the publications, they must be estimated from other reported summary statistics, such as the median, the minimum, the maximum, and quartiles. METHODS: We propose a simulation-based estimation approach using the Approximate Bayesian Computation (ABC) technique for estimating mean and standard deviation based on various sets of summary statistics found in published studies. We conduct a simulation study to compare the proposed ABC method with the existing methods of Hozo et al. (2005), Bland (2015), and Wan et al. (2014). RESULTS: In the estimation of the standard deviation, our ABC method performs better than the other methods when data are generated from skewed or heavy-tailed distributions. The corresponding average relative error (ARE) approaches zero as sample size increases. In data generated from the normal distribution, our ABC performs well. However, the Wan et al. method is best for estimating standard deviation under normal distribution. In the estimation of the mean, our ABC method is best regardless of assumed distribution. CONCLUSION: ABC is a flexible method for estimating the study-specific mean and standard deviation for meta-analysis, especially with underlying skewed or heavy-tailed distributions. The ABC method can be applied using other reported summary statistics such as the posterior mean and 95 % credible interval when Bayesian analysis has been employed. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12874-015-0055-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-45421062015-08-21 Simulation-based estimation of mean and standard deviation for meta-analysis via Approximate Bayesian Computation (ABC) Kwon, Deukwoo Reis, Isildinha M. BMC Med Res Methodol Research Article BACKGROUND: When conducting a meta-analysis of a continuous outcome, estimated means and standard deviations from the selected studies are required in order to obtain an overall estimate of the mean effect and its confidence interval. If these quantities are not directly reported in the publications, they must be estimated from other reported summary statistics, such as the median, the minimum, the maximum, and quartiles. METHODS: We propose a simulation-based estimation approach using the Approximate Bayesian Computation (ABC) technique for estimating mean and standard deviation based on various sets of summary statistics found in published studies. We conduct a simulation study to compare the proposed ABC method with the existing methods of Hozo et al. (2005), Bland (2015), and Wan et al. (2014). RESULTS: In the estimation of the standard deviation, our ABC method performs better than the other methods when data are generated from skewed or heavy-tailed distributions. The corresponding average relative error (ARE) approaches zero as sample size increases. In data generated from the normal distribution, our ABC performs well. However, the Wan et al. method is best for estimating standard deviation under normal distribution. In the estimation of the mean, our ABC method is best regardless of assumed distribution. CONCLUSION: ABC is a flexible method for estimating the study-specific mean and standard deviation for meta-analysis, especially with underlying skewed or heavy-tailed distributions. The ABC method can be applied using other reported summary statistics such as the posterior mean and 95 % credible interval when Bayesian analysis has been employed. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12874-015-0055-5) contains supplementary material, which is available to authorized users. BioMed Central 2015-08-12 /pmc/articles/PMC4542106/ /pubmed/26264850 http://dx.doi.org/10.1186/s12874-015-0055-5 Text en © Kwon and Reis. 2015 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/ (http://creativecommons.org/licenses/by/4.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Kwon, Deukwoo
Reis, Isildinha M.
Simulation-based estimation of mean and standard deviation for meta-analysis via Approximate Bayesian Computation (ABC)
title Simulation-based estimation of mean and standard deviation for meta-analysis via Approximate Bayesian Computation (ABC)
title_full Simulation-based estimation of mean and standard deviation for meta-analysis via Approximate Bayesian Computation (ABC)
title_fullStr Simulation-based estimation of mean and standard deviation for meta-analysis via Approximate Bayesian Computation (ABC)
title_full_unstemmed Simulation-based estimation of mean and standard deviation for meta-analysis via Approximate Bayesian Computation (ABC)
title_short Simulation-based estimation of mean and standard deviation for meta-analysis via Approximate Bayesian Computation (ABC)
title_sort simulation-based estimation of mean and standard deviation for meta-analysis via approximate bayesian computation (abc)
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4542106/
https://www.ncbi.nlm.nih.gov/pubmed/26264850
http://dx.doi.org/10.1186/s12874-015-0055-5
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