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

机译：适用于Ⅱ期临床研究的自适应两阶段优化设计，可尽早停止徒劳

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摘要

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.
机译：自适应设计在当代临床试验中发挥重要作用，以使设计灵活高效。在癌症临床试验中，由于样本量相对较小，因此在此阶段中获取尽可能多的信息非常重要。我们提出了一种新的自适应最优设计，该设计仅在第一阶段就没有用，就像Simon的两阶段设计一样。由于计算上的优势，通常由于徒劳无用或效力而使现有的自适应两阶段设计停止。仅通过使用高效的搜索算法，很难在徒劳无功的情况下搜索最优设计。例如，分支定界算法。我们必须使用多种计算技术来搜索最佳设计。所提出的自适应设计满足了单调特性的重要特性，即第二阶段的样本大小是第一阶段响应数量的非递增函数。在本文中，我们证明了所提出的自适应设计总是比西蒙的最佳设计具有更小的预期样本量。我们建议在实践中使用它来替代常用的Simon设计。

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