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Metaprop: a Stata command to perform meta-analysis of binomial data

BACKGROUND: Meta-analyses have become an essential tool in synthesizing evidence on clinical and epidemiological questions derived from a multitude of similar studies assessing the particular issue. Appropriate and accessible statistical software is needed to produce the summary statistic of interes...

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Autores principales: Nyaga, Victoria N, Arbyn, Marc, Aerts, Marc
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4373114/
https://www.ncbi.nlm.nih.gov/pubmed/25810908
http://dx.doi.org/10.1186/2049-3258-72-39
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author Nyaga, Victoria N
Arbyn, Marc
Aerts, Marc
author_facet Nyaga, Victoria N
Arbyn, Marc
Aerts, Marc
author_sort Nyaga, Victoria N
collection PubMed
description BACKGROUND: Meta-analyses have become an essential tool in synthesizing evidence on clinical and epidemiological questions derived from a multitude of similar studies assessing the particular issue. Appropriate and accessible statistical software is needed to produce the summary statistic of interest. METHODS: Metaprop is a statistical program implemented to perform meta-analyses of proportions in Stata. It builds further on the existing Stata procedure metan which is typically used to pool effects (risk ratios, odds ratios, differences of risks or means) but which is also used to pool proportions. Metaprop implements procedures which are specific to binomial data and allows computation of exact binomial and score test-based confidence intervals. It provides appropriate methods for dealing with proportions close to or at the margins where the normal approximation procedures often break down, by use of the binomial distribution to model the within-study variability or by allowing Freeman-Tukey double arcsine transformation to stabilize the variances. Metaprop was applied on two published meta-analyses: 1) prevalence of HPV-infection in women with a Pap smear showing ASC-US; 2) cure rate after treatment for cervical precancer using cold coagulation. RESULTS: The first meta-analysis showed a pooled HPV-prevalence of 43% (95% CI: 38%-48%). In the second meta-analysis, the pooled percentage of cured women was 94% (95% CI: 86%-97%). CONCLUSION: By using metaprop, no studies with 0% or 100% proportions were excluded from the meta-analysis. Furthermore, study specific and pooled confidence intervals always were within admissible values, contrary to the original publication, where metan was used. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/2049-3258-72-39) contains supplementary material, which is available to authorized users.
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spelling pubmed-43731142015-03-26 Metaprop: a Stata command to perform meta-analysis of binomial data Nyaga, Victoria N Arbyn, Marc Aerts, Marc Arch Public Health Methodology BACKGROUND: Meta-analyses have become an essential tool in synthesizing evidence on clinical and epidemiological questions derived from a multitude of similar studies assessing the particular issue. Appropriate and accessible statistical software is needed to produce the summary statistic of interest. METHODS: Metaprop is a statistical program implemented to perform meta-analyses of proportions in Stata. It builds further on the existing Stata procedure metan which is typically used to pool effects (risk ratios, odds ratios, differences of risks or means) but which is also used to pool proportions. Metaprop implements procedures which are specific to binomial data and allows computation of exact binomial and score test-based confidence intervals. It provides appropriate methods for dealing with proportions close to or at the margins where the normal approximation procedures often break down, by use of the binomial distribution to model the within-study variability or by allowing Freeman-Tukey double arcsine transformation to stabilize the variances. Metaprop was applied on two published meta-analyses: 1) prevalence of HPV-infection in women with a Pap smear showing ASC-US; 2) cure rate after treatment for cervical precancer using cold coagulation. RESULTS: The first meta-analysis showed a pooled HPV-prevalence of 43% (95% CI: 38%-48%). In the second meta-analysis, the pooled percentage of cured women was 94% (95% CI: 86%-97%). CONCLUSION: By using metaprop, no studies with 0% or 100% proportions were excluded from the meta-analysis. Furthermore, study specific and pooled confidence intervals always were within admissible values, contrary to the original publication, where metan was used. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/2049-3258-72-39) contains supplementary material, which is available to authorized users. BioMed Central 2014-11-10 /pmc/articles/PMC4373114/ /pubmed/25810908 http://dx.doi.org/10.1186/2049-3258-72-39 Text en © Nyaga et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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 Methodology
Nyaga, Victoria N
Arbyn, Marc
Aerts, Marc
Metaprop: a Stata command to perform meta-analysis of binomial data
title Metaprop: a Stata command to perform meta-analysis of binomial data
title_full Metaprop: a Stata command to perform meta-analysis of binomial data
title_fullStr Metaprop: a Stata command to perform meta-analysis of binomial data
title_full_unstemmed Metaprop: a Stata command to perform meta-analysis of binomial data
title_short Metaprop: a Stata command to perform meta-analysis of binomial data
title_sort metaprop: a stata command to perform meta-analysis of binomial data
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4373114/
https://www.ncbi.nlm.nih.gov/pubmed/25810908
http://dx.doi.org/10.1186/2049-3258-72-39
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