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Sequential methods for random-effects meta-analysis

Although meta-analyses are typically viewed as retrospective activities, they are increasingly being applied prospectively to provide up-to-date evidence on specific research questions. When meta-analyses are updated account should be taken of the possibility of false-positive findings due to repeat...

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Detalles Bibliográficos
Autores principales: Higgins, Julian P T, Whitehead, Anne, Simmonds, Mark
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Ltd. 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3107948/
https://www.ncbi.nlm.nih.gov/pubmed/21472757
http://dx.doi.org/10.1002/sim.4088
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author Higgins, Julian P T
Whitehead, Anne
Simmonds, Mark
author_facet Higgins, Julian P T
Whitehead, Anne
Simmonds, Mark
author_sort Higgins, Julian P T
collection PubMed
description Although meta-analyses are typically viewed as retrospective activities, they are increasingly being applied prospectively to provide up-to-date evidence on specific research questions. When meta-analyses are updated account should be taken of the possibility of false-positive findings due to repeated significance tests. We discuss the use of sequential methods for meta-analyses that incorporate random effects to allow for heterogeneity across studies. We propose a method that uses an approximate semi-Bayes procedure to update evidence on the among-study variance, starting with an informative prior distribution that might be based on findings from previous meta-analyses. We compare our methods with other approaches, including the traditional method of cumulative meta-analysis, in a simulation study and observe that it has Type I and Type II error rates close to the nominal level. We illustrate the method using an example in the treatment of bleeding peptic ulcers. Copyright © 2010 John Wiley & Sons, Ltd.
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spelling pubmed-31079482011-06-14 Sequential methods for random-effects meta-analysis Higgins, Julian P T Whitehead, Anne Simmonds, Mark Stat Med Research Article Although meta-analyses are typically viewed as retrospective activities, they are increasingly being applied prospectively to provide up-to-date evidence on specific research questions. When meta-analyses are updated account should be taken of the possibility of false-positive findings due to repeated significance tests. We discuss the use of sequential methods for meta-analyses that incorporate random effects to allow for heterogeneity across studies. We propose a method that uses an approximate semi-Bayes procedure to update evidence on the among-study variance, starting with an informative prior distribution that might be based on findings from previous meta-analyses. We compare our methods with other approaches, including the traditional method of cumulative meta-analysis, in a simulation study and observe that it has Type I and Type II error rates close to the nominal level. We illustrate the method using an example in the treatment of bleeding peptic ulcers. Copyright © 2010 John Wiley & Sons, Ltd. John Wiley & Sons, Ltd. 2011-04-30 2010-12-28 /pmc/articles/PMC3107948/ /pubmed/21472757 http://dx.doi.org/10.1002/sim.4088 Text en Copyright © 2011 John Wiley & Sons, Ltd. http://creativecommons.org/licenses/by/2.5/ Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation.
spellingShingle Research Article
Higgins, Julian P T
Whitehead, Anne
Simmonds, Mark
Sequential methods for random-effects meta-analysis
title Sequential methods for random-effects meta-analysis
title_full Sequential methods for random-effects meta-analysis
title_fullStr Sequential methods for random-effects meta-analysis
title_full_unstemmed Sequential methods for random-effects meta-analysis
title_short Sequential methods for random-effects meta-analysis
title_sort sequential methods for random-effects meta-analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3107948/
https://www.ncbi.nlm.nih.gov/pubmed/21472757
http://dx.doi.org/10.1002/sim.4088
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