论文标题
非比例危害下的生存分析：研究事件时间数据的非效率或等效性
Survival analysis under non-proportional hazards: investigating non-inferiority or equivalence in time-to-event data
论文作者
论文摘要
分析事件时间数据的经典方法，例如在临床试验中，要拟合Kaplan-Meier曲线，该曲线产生治疗效果作为治疗组之间的危害比。之后，通常进行对数秩检验，以研究生存是否存在差异，或者根据其他协变量，使用COX比例危害模型。但是，在许多试验中，由于存在非比例危害，这些方法失败了，导致了解释危险比和功率损失的困难。在考虑等效性或非效率试验时，基于对数的基于对数的测试通常会受到违反此假设的影响。在这里，我们提出了一个参数框架，以评估生存数据的等效性或不效率。我们在危险比和生存曲线的差异方面得出了点上的置信带。此外，我们提出了一个测试程序，通过直接比较在特定时间点或整个时间范围内的生存函数来解决非劣效率和等效性。我们通过临床试验示例和许多模拟结果证明了该方法的有效性。
The classical approach to analyze time-to-event data, e.g. in clinical trials, is to fit Kaplan-Meier curves yielding the treatment effect as the hazard ratio between treatment groups. Afterwards commonly a log-rank test is performed in order to investigate whether there is a difference in survival, or, depending on additional covariates, a Cox proportional hazard model is used. However, in numerous trials these approaches fail due to the presence of non-proportional hazards, resulting in difficulties of interpreting the hazard ratio and a loss of power. When considering equivalence or non-inferiority trials, the commonly performed log-rank based tests are similarly affected by a violation of this assumption. Here we propose a parametric framework to assess equivalence or non-inferiority for survival data. We derive pointwise confidence bands for both, the hazard ratio and the difference of the survival curves. Further we propose a test procedure addressing non-inferiority and equivalence by directly comparing the survival functions at certain time points or over an entire range of time. We demonstrate the validity of the methods by a clinical trial example and by numerous simulation results.
