Adaptive two-stage optimal designs for phase II clinical studies that allow early futility stopping

• Published in: Sequential analysis Vol. 38; no. 2; pp. 199 - 213
• Main Authors: Shan, Guogen, Zhang, Hua, Jiang, Tao
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 03.04.2019; Taylor & Francis Ltd
• Subjects: Adaptive design; branch-and-bound algorithm; Clinical trials; Computation; Design optimization; Medical research; optimal design; sample size; Search algorithms; Simon's optimal design
• ISSN: 0747-4946; 1532-4176
• DOI: 10.1080/07474946.2019.1611307

Adaptive designs play an important role in contemporary clinical trials to make designs flexible and efficient. In cancer clinical trials, given a relatively small sample size, it is important to obtain as much information as possible during this phase. We propose a new adaptive optimal design that stops for futility only in the first stage as Simon's two-stage design. The existing adaptive two-stage designs are often allowed to be stopped for futility or efficacy due to computational advantage. It is difficult to search for an optimal design with futility stopping only in the first stage by using efficient search algorithms; for example, the branch-and-bound algorithm. We have to use multiple computational techniques to search for the optimal design. The proposed adaptive design meets the important property of the monotonic property that the second stage sample size is a nonincreasing function of the number of responses from the first stage. In this article, we show that the proposed adaptive design always has a smaller expected sample size than Simon's optimal design. We recommend it for use in practice as an alternative to the commonly used Simon's design.