Semiparametric Bayesian commensurate survival model for post-market medical device surveillance with non-exchangeable historical data.

ABSTRACT: Trial investigators often have a primary interest in the estimation of the survival curve in a population for which there exists acceptable historical information from which to borrow strength. However, borrowing strength from a historical trial that is non-exchangeable with the current trial can result in biased conclusions. In this article we propose a fully Bayesian semiparametric method for the purpose of attenuating bias and increasing efficiency when jointly modeling time-to-event data from two possibly non-exchangeable sources of information. We illustrate the mechanics of our methods by applying them to a pair of post-market surveillance datasets regarding adverse events in persons on dialysis that had either a bare metal or drug-eluting stent implanted during a cardiac revasculari

SUBMITTER: Murray TA 

PROVIDER: S-EPMC3954409 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature