Multivariate meta-analysis with an increasing number of parameters.
Biom J
; 59(3): 496-510, 2017 May.
Article
em En
| MEDLINE
| ID: mdl-28195655
ABSTRACT
Meta-analysis can average estimates of multiple parameters, such as a treatment's effect on multiple outcomes, across studies. Univariate meta-analysis (UVMA) considers each parameter individually, while multivariate meta-analysis (MVMA) considers the parameters jointly and accounts for the correlation between their estimates. The performance of MVMA and UVMA has been extensively compared in scenarios with two parameters. Our objective is to compare the performance of MVMA and UVMA as the number of parameters, p, increases. Specifically, we show that (i) for fixed-effect (FE) meta-analysis, the benefit from using MVMA can substantially increase as p increases; (ii) for random effects (RE) meta-analysis, the benefit from MVMA can increase as p increases, but the potential improvement is modest in the presence of high between-study variability and the actual improvement is further reduced by the need to estimate an increasingly large between study covariance matrix; and (iii) when there is little to no between-study variability, the loss of efficiency due to choosing RE MVMA over FE MVMA increases as p increases. We demonstrate these three features through theory, simulation, and a meta-analysis of risk factors for non-Hodgkin lymphoma.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Metanálise como Assunto
/
Biometria
Tipo de estudo:
Etiology_studies
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Prognostic_studies
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Risk_factors_studies
/
Systematic_reviews
Limite:
Humans
Idioma:
En
Revista:
Biom J
Ano de publicação:
2017
Tipo de documento:
Article
País de afiliação:
Estados Unidos