Bayesian interim analysis in clinical trials

Academic Article

Abstract

  • We propose a Bayesian approach to monitor clinical trials with clustered binary outcomes using multivariate probit models. Our monitoring is based on the calculated probability of the reduced incidence rate using a new treatment compared with the standard treatment greater than a target improvement under different prior scenarios for the treatment effect. We develop a Bayesian sampling algorithm for posterior inference allowing missing values in the outcomes. We illustrate our method using a published early trail of inhaled nitric oxide therapy in premature infants. © 2008 Elsevier Inc. All rights reserved.
  • Authors

    Published In

    Digital Object Identifier (doi)

    Author List

  • Zhang X; Cutter G
  • Start Page

  • 751
  • End Page

  • 755
  • Volume

  • 29
  • Issue

  • 5