The adaptive seamless design combining phases II and III into a single trial has been shown growing interest for improving the efficiency of drug development, becoming the most frequent adaptive design type. It typically consists of two stages, the trial objectives being often different in each stage. The primary objectives are to select optimal experimental treatment group(s) in the first stage and compare the efficacy between the selected treatment and control groups in the second stage. In this article, we focus on a two-stage adaptive seamless design, for which treatment selection is based on the short-term binary endpoint and treatment comparison is based on the long-term binary endpoint. We thus propose an exact conditional test as a final analysis, based on the bivariate binomial distribution and given the selected treatment with the most promising short-term endpoint response rate from an interim analysis. Additionally, the mid-$p$ approach is incorporated to improve conservativeness for an exact test. Simulation studies were conducted to compare the proposed methods with a method based on the combination test. The proposed exact method controlled for type I error rate at the nominal level, regardless of the number of initial treatments or the correlation between short- and long-term endpoints. In terms of the treatment comparison power, the proposed methods are more powerful than that based on the combination test in the scenarios, with only one treatment being effective.

中文翻译：

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使用短期和长期二元结果的两阶段自适应无缝设计的统计测试

将 II 期和 III 期结合到一个试验中的自适应无缝设计已显示出对提高药物开发效率的兴趣日益浓厚，成为最常见的自适应设计类型。它通常由两个阶段组成，每个阶段的试验目标通常不同。主要目标是在第一阶段选择最佳实验治疗组，并在第二阶段比较所选治疗组和对照组之间的疗效。在本文中，我们关注的是两阶段自适应无缝设计，治疗选择基于短期二元终点，治疗比较基于长期二元终点。因此，我们提出了一个精确的条件测试作为最终分析，基于二元二项分布，并根据中期分析给出具有最有希望的短期终点反应率的选定治疗。此外，中$p$方法被纳入以提高精确测试的保守性。进行了仿真研究，以将所提出的方法与基于组合测试的方法进行比较。无论初始治疗的数量或短期和长期终点之间的相关性如何，所提出的精确方法都控制了名义水平的 I 类错误率。在处理比较能力方面，所提出的方法比基于场景组合测试的方法更强大，只有一种处理有效。