A Hierarchical Bayesian Design for Phase I Trials of Novel Combinations of Cancer Therapeutic Agents
Article first published online: 30 NOV 2009
© 2009, The International Biometric Society
Volume 66, Issue 3, pages 805–812, September 2010
How to Cite
Braun, T. M. and Wang, S. (2010), A Hierarchical Bayesian Design for Phase I Trials of Novel Combinations of Cancer Therapeutic Agents. Biometrics, 66: 805–812. doi: 10.1111/j.1541-0420.2009.01363.x
- Issue published online: 30 NOV 2009
- Article first published online: 30 NOV 2009
- Received February 2009. Revised September 2009. Accepted September 2009.
- Adaptive design;
- Bayesian statistics;
- Dose-escalation study;
- Dose-finding study;
- Two dimensional
Summary We propose a hierarchical model for the probability of dose-limiting toxicity (DLT) for combinations of doses of two therapeutic agents. We apply this model to an adaptive Bayesian trial algorithm whose goal is to identify combinations with DLT rates close to a prespecified target rate. We describe methods for generating prior distributions for the parameters in our model from a basic set of information elicited from clinical investigators. We survey the performance of our algorithm in a series of simulations of a hypothetical trial that examines combinations of four doses of two agents. We also compare the performance of our approach to two existing methods and assess the sensitivity of our approach to the chosen prior distribution.