A Generalization of a Nonparametric Test for Stochastically Ordered Distributions to Censored Survival Data
该文将一种用于随机递增生存时间的非参数趋势检验推广到右删失和左截断生存数据，蒙特卡洛研究表明其经验功效至少与线性趋势检验相当，且在特定情形下显著更优。
SUMMARY Currently used test statistics for stochastically increasing survival times in a k-sample problem are linear combinations of either Gehan (generalized Wilcoxon) or generalized Savage score sums. The power of these tests depends on a priori knowledge about the true form of the alternative. When isotonic regression is applied to these score sums, it yields a new trend test for right-censored survival data which generalizes to left-truncated survival data and to counting processes. In a Monte Carlo study, its empirical power is at least comparable with that of the linear trend tests in their standard form (without prior information), and it is markedly superior if only the last survival distribution turns out to be stochastically smaller or larger than the control distribution.