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Bayesian multivariate meta‐analysis of multiple factors
Author(s) -
Lin Lifeng,
Chu Haitao
Publication year - 2018
Publication title -
research synthesis methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.376
H-Index - 35
eISSN - 1759-2887
pISSN - 1759-2879
DOI - 10.1002/jrsm.1293
Subject(s) - multivariate statistics , meta analysis , bayesian probability , computer science , multivariate analysis , random effects model , statistics , econometrics , machine learning , artificial intelligence , medicine , mathematics
In medical sciences, a disease condition is typically associated with multiple risk and protective factors. Although many studies report results of multiple factors, nearly all meta‐analyses separately synthesize the association between each factor and the disease condition of interest. The collected studies usually report different subsets of factors, and the results from separate analyses on multiple factors may not be comparable because each analysis may use different subpopulation. This may impact on selecting most important factors to design a multifactor intervention program. This article proposes a new concept, multivariate meta‐analysis of multiple factors (MVMA‐MF), to synthesize all available factors simultaneously. By borrowing information across factors, MVMA‐MF can improve statistical efficiency and reduce biases compared with separate analyses when factors were missing not at random. As within‐study correlations between factors are commonly unavailable from published articles, we use a Bayesian hybrid model to perform MVMA‐MF, which effectively accounts for both within‐ and between‐study correlations. The performance of MVMA‐MF and the conventional methods are compared using simulations and an application to a pterygium dataset consisting of 29 studies on 8 risk factors.