竞争风险两个累积发生函数相等性的近似检验
Approximative Tests for the Equality of Two Cumulative Incidence Functions of a Competing Risk
摘要：
在广泛使用的竞争风险设置的背景下，我们讨论了不同的推理程序来检验两个累积发生率函数的相等性，其中数据可能会受到独立的右删失或左截断。为此，我们比较两个样本Kolmogorov-Smirnov和Cramer-von Mises型检验统计量。由于，在一般情况下，其相应的渐近极限分布取决于未知量，我们利用野生的自举响应以及近似技术来构建足够的测试决策。在这里，后者的程序的动机从异方差析因设计的测试程序，但尚未提出的生存环境。模拟研究显示了所有考虑的测试在各种设置下的性能，最后一个真实的数据例子，在中性粒细胞减少症的血流感染来说明他们的应用。
英文摘要：
In the context of the widely used competing risks set-up we discuss different inference procedures for testing equality of two cumulative incidence functions, where the data may be subject to independent right-censoring or left-truncation. To this end we compare two-sample Kolmogorov-Smirnov- and Cramer-von Mises-type test statistics. Since, in general, their corresponding asymptotic limit distributions depend on unknown quantities, we utilize wild bootstrap resampling as well as approximation techniques to construct adequate test decisions. Here the latter procedures are motivated from testing procedures for heteroscedastic factorial designs but have not yet been proposed in the survival context. A simulation study shows the performance of all considered tests under various settings and finally a real data example about bloodstream infection during neutropenia is used to illustrate their application.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
