Response-adaptive (RA) designs of clinical trials allow targeting a given objective by skewing the allocation of participants to treatments based on observed outcomes. RA designs face greater regulatory scrutiny due to potential type I error inflation, which limits their uptake in practice. Existing approaches to type I error control either only work for specific designs, have a risk of Monte Carlo/approximation error, are conservative, or computationally intractable. We develop a general and computationally tractable approach for exact analysis in two-arm RA designs with binary outcomes. We use the approach to construct exact tests applicable to designs that use either randomized or deterministic RA procedures, allowing for complexities such as delayed outcomes, early stopping or allocation of participants in blocks. Our efficient forward recursion implementation allows for testing of two-arm trials with 1,000 participants on a standard computer. Through an illustrative computational study of trials using randomized dynamic programming we show that, contrary to what is known for equal allocation, a conditional exact test has, almost uniformly, higher power than the unconditional test. Two real-world trials with the above-mentioned complexities are re-analyzed to demonstrate the value of our approach in controlling type I error and/or improving the statistical power.

翻译：响应自适应（RA）临床试验设计允许根据观察到的结果，通过偏斜参与者向不同治疗组的分配来达成特定目标。由于可能增加第一类错误的风险，RA设计面临更严格的监管审查，这限制了其在实践中的应用。现有的第一类错误控制方法要么仅适用于特定设计，存在蒙特卡洛/近似误差风险，要么过于保守，或计算上不可行。我们开发了一种通用且计算可行的方法，用于对具有二元结果的二臂RA设计进行精确分析。我们利用该方法构建了适用于使用随机化或确定性RA程序的设计的精确检验，并允许处理诸如结果延迟、提前终止或按区块分配参与者等复杂情况。我们高效的前向递归实现使得在标准计算机上对多达1000名参与者的二臂试验进行检验成为可能。通过对使用随机化动态规划的试验进行示例性计算研究，我们发现，与已知的等额分配情况相反，条件精确检验几乎在所有情况下都比无条件检验具有更高的检验功效。我们重新分析了两个具有上述复杂性的真实世界试验，以证明我们的方法在控制第一类错误和/或提高统计功效方面的价值。