Two-stage response adaptive randomization designs for multi-arm trials with binary outcome

ABSTRACT

In recent years, adaptive randomization methods have gained significant popularity in clinical research and trial design due to their ability to provide both efficiency and flexibility in adjusting the statistical procedures of ongoing clinical trials. For a study to compare multiple treatments, a multi-arm two-stage design could be utilized to select the best treatment from the first stage and further compare that treatment with control in the second stage. The traditional design used equal randomization in both stages. To better utilize the interim results from the first stage, we propose to develop response adaptive randomization two-stage designs for a multi-arm clinical trial with binary outcome. Two allocation methods are considered: (1) an optimal allocation based on a sequential design; (2) the play-the-winner rule. Optimal multi-arm two-stage designs are obtained under three criteria: minimizing the expected number of failures, minimizing the average expected sample size, and minimizing the expected sample size under the null hypothesis. Simulation studies show that the proposed adaptive design based on the play-the-winner rule has good performance. A phase II trial for patients with pancreas adenocarcinoma and a germline BRCA$/$PALB2 mutation was used to illustrate the application of the proposed response adaptive randomization designs.

KEYWORDS:

• Binary endpoint
• multi-arm two-stage design
• number of failures
• optimal design
• response adaptive randomization

Acknowledgements

The authors are very grateful to the Editor, Associate Editor, and two reviewers for their insightful comments that helped improve the manuscript. Shan’s research is partially supported by the National Institutes of Health under Award Number R01AG070849 and R03CA248006.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

Shan’s research is partially supported by the National Institutes of Health under Award Number R01AG070849 and R03CA248006