title: Multivariate meta-analysis.
abstract: Meta-analysis is now a standard statistical tool for assessing the overall strength and interesting features of a relationship, on the basis of multiple independent studies. There is, however, recent acknowledgement of the fact that in many applications responses are rarely uniquely determined. Hence there has been some change of focus from a single response to the analysis of multiple outcomes. In this paper we propose and evaluate three Bayesian multivariate meta-analysis models: two multivariate analogues of the traditional univariate random effects models which make different assumptions about the relationships between studies and estimates, and a multivariate random effects model which is a Bayesian adaptation of the mixed model approach. Our preferred method is then illustrated through an analysis of a new data set on parental smoking and two health outcomes (asthma and lower respiratory disease) in children.
authors.affiliation: Queensland University of Technology, Australia.
authors.forename: In-Sun
authors.initials: IS
authors.lastname: Nam
authors.forename: Kerrie
authors.initials: K
authors.lastname: Mengersen
authors.forename: Paul
authors.initials: P
authors.lastname: Garthwaite
mesh_terms: D001249:Asthma; D001315:Australia; D001499:Bayes Theorem; D002648:Child; D005260:Female; D006801:Humans; D008297:Male; D015201:Meta-Analysis as Topic; D015999:Multivariate Analysis; D016017:Odds Ratio; D012141:Respiratory Tract Infections; D014028:Tobacco Smoke Pollution / Q000706:statistics & numerical data
chemical_list: D014028:Tobacco Smoke Pollution