A GENERAL ESTIMATOR OF THE TREATMENT EFFECT WHEN THE DATA ARE HEAVILY CENSORED

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.