A matrix-based method of moments for fitting multivariate network meta-analysis models with multiple outcomes and random inconsistency effects.
Biometrics
; 74(2): 548-556, 2018 06.
Article
em En
| MEDLINE
| ID: mdl-28806485
ABSTRACT
Random-effects meta-analyses are very commonly used in medical statistics. Recent methodological developments include multivariate (multiple outcomes) and network (multiple treatments) meta-analysis. Here, we provide a new model and corresponding estimation procedure for multivariate network meta-analysis, so that multiple outcomes and treatments can be included in a single analysis. Our new multivariate model is a direct extension of a univariate model for network meta-analysis that has recently been proposed. We allow two types of unknown variance parameters in our model, which represent between-study heterogeneity and inconsistency. Inconsistency arises when different forms of direct and indirect evidence are not in agreement, even having taken between-study heterogeneity into account. However, the consistency assumption is often assumed in practice and so we also explain how to fit a reduced model which makes this assumption. Our estimation method extends several other commonly used methods for meta-analysis, including the method proposed by DerSimonian and Laird (). We investigate the use of our proposed methods in the context of both a simulation study and a real example.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Modelos Estatísticos
/
Metanálise em Rede
Tipo de estudo:
Clinical_trials
/
Prognostic_studies
/
Risk_factors_studies
/
Systematic_reviews
Limite:
Humans
Idioma:
En
Ano de publicação:
2018
Tipo de documento:
Article