论文标题
阶乘设计中非属性危害的多个内核测试程序
A Multiple kernel testing procedure for non-proportional hazards in factorial designs
论文作者
论文摘要
在本文中，我们提出了多个内核测试程序，以同时感兴趣时，当几个因素（例如不同的治疗组，性别，病史）及其相互作用时，推断生存数据。我们的方法能够处理复杂的数据，当假设诸如相称性不能合理时，可以将其视为无所不在的COX模型的替代方法。我们的方法结合了来自生存分析，机器学习和多次测试的众所周知的概念：加权的对数秩检验，内核方法和多个对比度测试。这样，可以检测到超出经典比例危害设置以外的复杂危险替代方案。此外，通过充分利用单个测试程序的依赖性结构以避免功率损失来进行多个比较。总的来说，这为阶乘生存设计提供了灵活而强大的程序，其理论有效性通过Martingale论证和$ V $统计的理论证明。我们在广泛的模拟研究中评估了方法的性能，并通过真实的数据分析对其进行了说明。
In this paper we propose a Multiple kernel testing procedure to infer survival data when several factors (e.g. different treatment groups, gender, medical history) and their interaction are of interest simultaneously. Our method is able to deal with complex data and can be seen as an alternative to the omnipresent Cox model when assumptions such as proportionality cannot be justified. Our methodology combines well-known concepts from Survival Analysis, Machine Learning and Multiple Testing: differently weighted log-rank tests, kernel methods and multiple contrast tests. By that, complex hazard alternatives beyond the classical proportional hazard set-up can be detected. Moreover, multiple comparisons are performed by fully exploiting the dependence structure of the single testing procedures to avoid a loss of power. In all, this leads to a flexible and powerful procedure for factorial survival designs whose theoretical validity is proven by martingale arguments and the theory for $V$-statistics. We evaluate the performance of our method in an extensive simulation study and illustrate it by a real data analysis.
