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Multivariate meta-analysis of multiple outcomes: characteristics and predictors of borrowing of strength from Cochrane reviews

OBJECTIVES: Multivariate meta-analysis allows the joint synthesis of multiple outcomes accounting for their correlation. This enables borrowing of strength (BoS) across outcomes, which may lead to greater efficiency and even different conclusions compared to separate univariate meta-analyses. Howeve...

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Autores principales: Hattle, Miriam, Burke, Danielle L., Trikalinos, Thomas, Schmid, Christopher H., Chen, Yong, Jackson, Dan, Riley, Richard D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9316363/
https://www.ncbi.nlm.nih.gov/pubmed/35883187
http://dx.doi.org/10.1186/s13643-022-01999-0
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author Hattle, Miriam
Burke, Danielle L.
Trikalinos, Thomas
Schmid, Christopher H.
Chen, Yong
Jackson, Dan
Riley, Richard D.
author_facet Hattle, Miriam
Burke, Danielle L.
Trikalinos, Thomas
Schmid, Christopher H.
Chen, Yong
Jackson, Dan
Riley, Richard D.
author_sort Hattle, Miriam
collection PubMed
description OBJECTIVES: Multivariate meta-analysis allows the joint synthesis of multiple outcomes accounting for their correlation. This enables borrowing of strength (BoS) across outcomes, which may lead to greater efficiency and even different conclusions compared to separate univariate meta-analyses. However, multivariate meta-analysis is complex to apply, so guidance is needed to flag (in advance of analysis) when the approach is most useful. STUDY DESIGN AND SETTING: We use 43 Cochrane intervention reviews to empirically investigate the characteristics of meta-analysis datasets that are associated with a larger BoS statistic (from 0 to 100%) when applying a bivariate meta-analysis of binary outcomes. RESULTS: Four characteristics were identified as strongly associated with BoS: the total number of studies, the number of studies with the outcome of interest, the percentage of studies missing the outcome of interest, and the largest absolute within-study correlation. Using these characteristics, we then develop a model for predicting BoS in a new dataset, which is shown to have good performance (an adjusted R(2) of 50%). Applied examples are used to illustrate the use of the BoS prediction model. CONCLUSIONS: Cochrane reviewers mainly use univariate meta-analysis methods, but the identified characteristics associated with BoS and our subsequent prediction model for BoS help to flag when a multivariate meta-analysis may also be beneficial in Cochrane reviews with multiple binary outcomes. Extension to non-Cochrane reviews and other outcome types is still required. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13643-022-01999-0.
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spelling pubmed-93163632022-07-27 Multivariate meta-analysis of multiple outcomes: characteristics and predictors of borrowing of strength from Cochrane reviews Hattle, Miriam Burke, Danielle L. Trikalinos, Thomas Schmid, Christopher H. Chen, Yong Jackson, Dan Riley, Richard D. Syst Rev Research OBJECTIVES: Multivariate meta-analysis allows the joint synthesis of multiple outcomes accounting for their correlation. This enables borrowing of strength (BoS) across outcomes, which may lead to greater efficiency and even different conclusions compared to separate univariate meta-analyses. However, multivariate meta-analysis is complex to apply, so guidance is needed to flag (in advance of analysis) when the approach is most useful. STUDY DESIGN AND SETTING: We use 43 Cochrane intervention reviews to empirically investigate the characteristics of meta-analysis datasets that are associated with a larger BoS statistic (from 0 to 100%) when applying a bivariate meta-analysis of binary outcomes. RESULTS: Four characteristics were identified as strongly associated with BoS: the total number of studies, the number of studies with the outcome of interest, the percentage of studies missing the outcome of interest, and the largest absolute within-study correlation. Using these characteristics, we then develop a model for predicting BoS in a new dataset, which is shown to have good performance (an adjusted R(2) of 50%). Applied examples are used to illustrate the use of the BoS prediction model. CONCLUSIONS: Cochrane reviewers mainly use univariate meta-analysis methods, but the identified characteristics associated with BoS and our subsequent prediction model for BoS help to flag when a multivariate meta-analysis may also be beneficial in Cochrane reviews with multiple binary outcomes. Extension to non-Cochrane reviews and other outcome types is still required. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13643-022-01999-0. BioMed Central 2022-07-26 /pmc/articles/PMC9316363/ /pubmed/35883187 http://dx.doi.org/10.1186/s13643-022-01999-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Hattle, Miriam
Burke, Danielle L.
Trikalinos, Thomas
Schmid, Christopher H.
Chen, Yong
Jackson, Dan
Riley, Richard D.
Multivariate meta-analysis of multiple outcomes: characteristics and predictors of borrowing of strength from Cochrane reviews
title Multivariate meta-analysis of multiple outcomes: characteristics and predictors of borrowing of strength from Cochrane reviews
title_full Multivariate meta-analysis of multiple outcomes: characteristics and predictors of borrowing of strength from Cochrane reviews
title_fullStr Multivariate meta-analysis of multiple outcomes: characteristics and predictors of borrowing of strength from Cochrane reviews
title_full_unstemmed Multivariate meta-analysis of multiple outcomes: characteristics and predictors of borrowing of strength from Cochrane reviews
title_short Multivariate meta-analysis of multiple outcomes: characteristics and predictors of borrowing of strength from Cochrane reviews
title_sort multivariate meta-analysis of multiple outcomes: characteristics and predictors of borrowing of strength from cochrane reviews
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9316363/
https://www.ncbi.nlm.nih.gov/pubmed/35883187
http://dx.doi.org/10.1186/s13643-022-01999-0
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