Efficient and Ad Hoc Estimation in the Bivariate Censoring Model

Mark J. van der Laan, Division of Biostatistics, School of Public Health, University of California, Berkeley

Abstract

A large number of proposals for estimating the bivariate survival function under random censoring has been made. In this paper we discuss nonparametric maximum likelihood estimation and the bivariate Kaplan-Meier estimator of Dabrowska. We show how these estimators are computed, present their intuitive background and compare their practical performance under different levels of dependence and censoring, based on extensive simulation results, which leads to a practical advise.