A General Estimator of the Treatment Effect When the Data are Heavily Censored
提出一种广义Hodges-Lehmann型估计量，用于右删失两样本问题中的处理效应估计，基于逆分位数思想，在Kaplan-Meier估计量一致子空间上使用截断版本，模拟显示在严重不等删失下优于现有方法。
A generalized Hodges-Lehmann type estimator for the treatment effect in the two-sample problem with right censoring, is proposed based on an inverse-quantile-type idea using truncated versions of the Kaplan-Meier estimators over the subspace where they are consistent. Its strong consistency and asymptotic normality can be obtained, under no conditions on the uninformative censorings, and the resulting variance is easily estimable from the data. In simulation studies the proposed estimator is superior to existing procedures in the presence of heavy unequal censoring.