Bayesian meta-analytical methods to incorporate multiple surrogate endpoints in drug development process.

ABSTRACT: A number of meta-analytical methods have been proposed that aim to evaluate surrogate endpoints. Bivariate meta-analytical methods can be used to predict the treatment effect for the final outcome from the treatment effect estimate measured on the surrogate endpoint while taking into account the uncertainty around the effect estimate for the surrogate endpoint. In this paper, extensions to multivariate models are developed aiming to include multiple surrogate endpoints with the potential benefit of reducing the uncertainty when making predictions. In this Bayesian multivariate meta-analytic framework, the between-study variability is modelled in a formulation of a product of normal univariate distributions. This formulation is particularly convenient for including multiple surrogate endpoi

SUBMITTER: Bujkiewicz S 

PROVIDER: S-EPMC4950070 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature