Bivariate network meta-analysis for surrogate endpoint evaluation.

ABSTRACT: Surrogate endpoints are very important in regulatory decision making in healthcare, in particular if they can be measured early compared to the long-term final clinical outcome and act as good predictors of clinical benefit. Bivariate meta-analysis methods can be used to evaluate surrogate endpoints and to predict the treatment effect on the final outcome from the treatment effect measured on a surrogate endpoint. However, candidate surrogate endpoints are often imperfect, and the level of association between the treatment effects on the surrogate and final outcomes may vary between treatments. This imposes a limitation on methods which do not differentiate between the treatments. We develop bivariate network meta-analysis (bvNMA) methods, which combine data on treatment effects on the sur

SUBMITTER: Bujkiewicz S 

PROVIDER: S-EPMC6618064 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature