Bivariate beta-binomial model using Gaussian copula for bivariate meta-analysis of two binary outcomes with low incidence

摘要

In meta-analysis of rare-event outcomes, an additional statistical consideration is necessary due to the occurrence of studies with no event. The traditional approaches of adding a correction factor or omitting these studies are known to result in misleading conclusions. Furthermore, studies involved in the meta-analysis often report results for more than one outcome. Bivariate meta-analysis is known as a promising approach for jointly combining two outcomes whilst incorporating correlations between outcomes. However, there has not been sufficient discussion on a bivariate extension in the context of meta-analysis for rare-event outcomes. We consider a joint synthesis of two binary outcomes with low incidence, and propose a novel bivariate meta-analysis method using copula. The method assumes marginal beta-binomial distributions for the two outcomes, and links these margins by a bivariate copula which identifies an overall dependence structure between outcomes. A simulation study suggested that the method could provide a robust estimation for the incidence of rare events and have potential benefits of bivariate meta-analysis such as an improvement of precision of pooled estimates. We illustrated the method through an application to a meta-analysis of 48 studies that evaluated a potential risk of rosigli-tazone on myocardial infection and cardiovascular death.