Most statistical tests for treatment effects used in randomized clinical trials with survival outcomes are based on the proportional hazards assumption, which often fails in practice. Data from early exploratory studies may provide evidence of non-proportional hazards which can guide the choice of alternative tests in the design of practice-changing confirmatory trials. We developed a test to detect treatment effects in a late-stage trial which accounts for the deviations from proportional hazards suggested by early-stage data. Conditional on early-stage data, among all tests which control the frequentist Type I error rate at a fixed ± level, our testing procedure maximizes the Bayesian predictive probability that the study will demonstrate the efficacy of the experimental treatment. Hence, the proposed test provides a useful benchmark for other tests commonly used in the presence of non-proportional hazards, for example weighted log-rank tests. We illustrate this approach in simulations based on data from a published cancer immunotherapy phase III trial. This article is protected by copyright. All rights reserved.

中文翻译：

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非比例危险环境中生存数据测试程序的优化

在随机临床试验中使用的大多数治疗效果统计检验都是基于比例风险假设，这在实践中经常失败。早期探索性研究的数据可以提供非比例危害的证据，可以指导在设计改变实践的验证性试验时选择替代测试。我们开发了一个测试来检测后期试验中的治疗效果，该试验解释了早期数据所建议的比例风险的偏差。以早期数据为条件，在所有将频率论 I 类错误率控制在固定 ± 水平的测试中，我们的测试程序最大化了贝叶斯预测概率，该研究将证明实验治疗的有效性。因此，提议的测试为存在非比例危险时常用的其他测试提供了有用的基准，例如加权对数秩测试。我们根据已发表的癌症免疫治疗 III 期试验的数据在模拟中说明了这种方法。本文受版权保护。版权所有。