右删失和捆绑数据的Mann-Whitney效应的推断
Inference for the Mann-Whitney Effect for Right-Censored and Tied Data
摘要：
Mann-Whitney效应是区分两种生存分布的直观度量。在这里，我们分析了这个参数的各种推理技术在两个样本的生存设置独立的右删失，生存时间甚至允许离散分布。这允许数据中的关系，并需要引入Kaplan-Meier估计量的归一化版本，从中推导出足够的点估计值。从后者的渐近分析，渐近精确的推理程序的基础上，标准的正常，引导和置换分位数的开发和比较模拟。在这里，渐近鲁棒的，在相同的生存和删失分布的情况下，甚至是最精确的置换过程被证明是最好的。最后，所有程序都说明了使用一个真实的数据集。
英文摘要：
The Mann-Whitney effect is an intuitive measure for discriminating two survival distributions. Here we analyze various inference techniques for this parameter in a two-sample survival setting with independent right-censoring, where the survival times are even allowed to be discretely distributed. This allows for ties in the data and requires the introduction of normalized versions of Kaplan-Meier estimators from which adequate point estimates are deduced. From an asymptotic analysis of the latter, asymptotically exact inference procedures based on standard normal, bootstrap- and permutation-quantiles are developed and compared in simulations. Here, the asymptotically robust and, in case of equal survival and censoring distributions, even finitely exact permutation procedure turned out to be the best. Finally, all procedures are illustrated using a real data set.
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