Optimal dose selection accounting for patient subpopulations in a randomized Phase II trial to maximize the success probability of a subsequent Phase III trial

ABSTRACT

Phase II clinical trials are conducted to determine the optimal dose of the study drug for use in Phase III clinical trials while also balancing efficacy and safety. In conducting these trials, it may be important to consider subpopulations of patients grouped by background factors such as drug metabolism and kidney and liver function. Determining the optimal dose, as well as maximizing the effectiveness of the study drug by analyzing patient subpopulations, requires a complex decision-making process. In extreme cases, drug development has to be terminated due to inadequate efficacy or severe toxicity. Such a decision may be based on a particular subpopulation. We propose a Bayesian utility approach (BUART) to randomized Phase II clinical trials which uses a first-order bivariate normal dynamic linear model for efficacy and safety in order to determine the optimal dose and study population in a subsequent Phase III clinical trial. We carried out a simulation study under a wide range of clinical scenarios to evaluate the performance of the proposed method in comparison with a conventional method separately analyzing efficacy and safety in each patient population. The proposed method showed more favorable operating characteristics in determining the optimal population and dose.

Key Words:

• Bivariate normal dynamic linear model
• dose-finding study
• efficacy and safety
• patient subpopulation
• utility function

Acknowledgments

The authors would like to thank Dr. Kentaro Sakamaki for his helpful comments. The authors thank the Editor, the Associate Editor, and the two referees for their thoughtful and constructive comments and suggestions.

Supplemental material

Supplemental data for this article can be accessed on the publisher’s website.

Additional information

Funding

Satoshi Morita’s work was supported in part by a Grant-in-Aid for Scientific Research C-24500345 from the Ministry of Health, Labour and Welfare of Japan.