Empirical comparison of univariate and multivariate meta-analyses in Cochrane Pregnancy and Childbirth reviews with multiple binary outcomes.
Anesthesia, Conduction
Anesthesiology
/ methods
Cesarean Section
/ methods
Female
Humans
Labor, Obstetric
Likelihood Functions
Meta-Analysis as Topic
Models, Theoretical
Multivariate Analysis
Odds Ratio
Parturition
Pregnancy
Research Design
Review Literature as Topic
Serotonin Antagonists
/ chemistry
Treatment Outcome
comparison
evidence synthesis
multivariate meta-analysis
univariate meta-analysis
Journal
Research synthesis methods
ISSN: 1759-2887
Titre abrégé: Res Synth Methods
Pays: England
ID NLM: 101543738
Informations de publication
Date de publication:
Sep 2019
Sep 2019
Historique:
received:
30
07
2018
revised:
04
04
2019
accepted:
13
04
2019
pubmed:
7
5
2019
medline:
29
4
2020
entrez:
7
5
2019
Statut:
ppublish
Résumé
Multivariate meta-analysis (MVMA) jointly synthesizes effects for multiple correlated outcomes. The MVMA model is potentially more difficult and time-consuming to apply than univariate models, so if its use makes little difference to parameter estimates, it could be argued that it is redundant. We assessed the applicability and impact of MVMA in Cochrane Pregnancy and Childbirth (CPCB) systematic reviews. We applied MVMA to CPCB reviews published between 2011 and 2013 with two or more binary outcomes with at least three studies and compared findings with results of univariate meta-analyses. Univariate random effects meta-analysis models were fitted using restricted maximum likelihood estimation (REML). Eighty CPCB reviews were published. MVMA could not be applied in 70 of these reviews. MVMA was not feasible in three of the remaining 10 reviews because the appropriate models failed to converge. Estimates from MVMA agreed with those of univariate analyses in most of the other seven reviews. Statistical significance changed in two reviews: In one, this was due to a very small change in P value; in the other, the MVMA result for one outcome suggested that previous univariate results may be vulnerable to small-study effects and that the certainty of clinical conclusions needs consideration. MVMA methods can be applied only in a minority of reviews of interventions in pregnancy and childbirth and can be difficult to apply because of missing correlations or lack of convergence. Nevertheless, clinical and/or statistical conclusions from MVMA may occasionally differ from those from univariate analyses.
Sections du résumé
BACKGROUND
BACKGROUND
Multivariate meta-analysis (MVMA) jointly synthesizes effects for multiple correlated outcomes. The MVMA model is potentially more difficult and time-consuming to apply than univariate models, so if its use makes little difference to parameter estimates, it could be argued that it is redundant.
METHODS
METHODS
We assessed the applicability and impact of MVMA in Cochrane Pregnancy and Childbirth (CPCB) systematic reviews. We applied MVMA to CPCB reviews published between 2011 and 2013 with two or more binary outcomes with at least three studies and compared findings with results of univariate meta-analyses. Univariate random effects meta-analysis models were fitted using restricted maximum likelihood estimation (REML).
RESULTS
RESULTS
Eighty CPCB reviews were published. MVMA could not be applied in 70 of these reviews. MVMA was not feasible in three of the remaining 10 reviews because the appropriate models failed to converge. Estimates from MVMA agreed with those of univariate analyses in most of the other seven reviews. Statistical significance changed in two reviews: In one, this was due to a very small change in P value; in the other, the MVMA result for one outcome suggested that previous univariate results may be vulnerable to small-study effects and that the certainty of clinical conclusions needs consideration.
CONCLUSIONS
CONCLUSIONS
MVMA methods can be applied only in a minority of reviews of interventions in pregnancy and childbirth and can be difficult to apply because of missing correlations or lack of convergence. Nevertheless, clinical and/or statistical conclusions from MVMA may occasionally differ from those from univariate analyses.
Identifiants
pubmed: 31058440
doi: 10.1002/jrsm.1353
pmc: PMC6771837
doi:
Substances chimiques
Serotonin Antagonists
0
Types de publication
Comparative Study
Journal Article
Meta-Analysis
Langues
eng
Sous-ensembles de citation
IM
Pagination
440-451Subventions
Organisme : Medical Research Council
ID : MC_UU_12023/21
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/J013595/1
Pays : United Kingdom
Organisme : NIHR CLAHRC West Midlands+
Informations de copyright
© 2019 The Authors Research Synthesis Methods Published by John Wiley & Sons Ltd.
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