An improved method for bivariate meta-analysis when within-study correlations are unknown.

ABSTRACT: Multivariate meta-analysis, which jointly analyzes multiple and possibly correlated outcomes in a single analysis, is becoming increasingly popular in recent years. An attractive feature of the multivariate meta-analysis is its ability to account for the dependence between multiple estimates from the same study. However, standard inference procedures for multivariate meta-analysis require the knowledge of within-study correlations, which are usually unavailable. This limits standard inference approaches in practice. Riley et al proposed a working model and an overall synthesis correlation parameter to account for the marginal correlation between outcomes, where the only data needed are those required for a separate univariate random-effects meta-analysis. As within-study correlations are n

SUBMITTER: Hong C 

PROVIDER: S-EPMC6071677 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature