Nonparametric estimation of marked survival data in the presence of dependent censoring

We consider nonparametrically estimating the joint distribution of a survival time and mark variable, where the survival time is subject to right censoring and the mark variable is only observed when the survival time is not censored. The possibility of dependent censoring is allowed for using inverse probability of censoring weights. The proposed estimator is shown to be consistent and asymptotically normal. Finite sample behavior of the proposed methods are investigated via simulation study. Finally, we illustrate the nonparametric estimator from a recent HIV vaccine efficacy trial.

Automated summary

This paper considers the nonparametric estimation of the joint distribution of a survival time and a mark variable, where the survival time is subject to right censoring and the mark variable is only observed when the survival time is not censored. Dependent censoring is allowed for by using inverse probability of censoring weights. The proposed estimator is shown to be consistent and asymptotically normal. The finite sample behavior of the proposed methods is investigated through simulation study. Finally, the nonparametric estimator is illustrated using a recent HIV vaccine efficacy trial.