Harvard-MIT Center for Regulatory Science, Harvard Medical School, Boston, Massachusetts.
Department of Data Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts.
Biometrics. 2021 Jun;77(2):587-598. doi: 10.1111/biom.13315. Epub 2020 Jun 24.
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 nonproportional 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 that control the frequentist Type I error rate at a fixed α 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 nonproportional hazards, for example, weighted log-rank tests. We illustrate this approach in simulations based on data from a published cancer immunotherapy phase III trial.
大多数用于生存结局的随机临床试验的治疗效果统计检验都是基于比例风险假设，但该假设在实际中经常失效。早期探索性研究的数据可能提供非比例风险的证据，这可以指导在改变实践的确证性试验的设计中选择替代检验。我们开发了一种在后期试验中检测治疗效果的检验方法，该方法考虑了早期数据提示的比例风险偏离。在早期数据条件下，在所有控制固定α水平下的频率型Ⅰ类错误率的检验中，我们的检验程序最大化了研究将证明实验治疗效果的贝叶斯预测概率。因此，该检验为存在非比例风险时常用的其他检验（例如加权对数秩检验）提供了一个有用的基准。我们基于已发表的癌症免疫治疗 III 期试验的数据进行了模拟，说明了这种方法。
