Article first published online: 11 JAN 2012
Copyright © 2012 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 31, Issue 10, pages 901–914, 10 May 2012
How to Cite
Karuri, S. W. and Simon, R. (2012), A two-stage Bayesian design for co-development of new drugs and companion diagnostics. Statist. Med., 31: 901–914. doi: 10.1002/sim.4462
- Issue published online: 11 APR 2012
- Article first published online: 11 JAN 2012
- Manuscript Accepted: 14 OCT 2011
- Manuscript Received: 3 JUN 2011
- clinical trials design;
- predictive biomarkers;
- Bayesian inference;
- prior distribution;
- type I error probabilities
Most new drug development in oncology is based on targeting specific molecules. Genomic profiles and deregulated drug targets vary from patient to patient making new treatments likely to benefit only a subset of patients traditionally grouped in the same clinical trials. Predictive biomarkers are being developed to identify patients who are most likely to benefit from a particular treatment; however, their biological basis is not always conclusive. The inclusion of marker-negative patients in a trial is therefore sometimes necessary for a more informative evaluation of the therapy. In this paper, we present a two-stage Bayesian design that includes both marker-positive and marker-negative patients in a clinical trial. We formulate a family of prior distributions that represent the degree of a priori confidence in the predictive biomarker. To avoid exposing patients to a treatment to which they may not be expected to benefit, we perform an interim analysis that may stop accrual of marker-negative patients or accrual of all patients. We demonstrate with simulations that the design and priors used control type I errors, give adequate power, and enable the early futility analysis of test-negative patients to be based on prior specification on the strength of evidence in the biomarker. Copyright © 2012 John Wiley & Sons, Ltd.