Bivariate Competing Risks Models Under Random Left Truncation and Right Censoring

In survival or reliability studies, it is common to have truncated data due to the limited time span of the study or dropouts of the subjects for vari-ous reasons. The estimation of survivor function under left truncation was first discussed by Kaplan and Meier by extending the well known product-limit estimator of the survivor function. The focus of this paper is on the nonparametric estimation of the survivor function and the cause-specific sub-distribution functions in bivariate competing risks set up, when the obser-vations are subject to random left truncation and right censoring. Various asymptotic properties of the estimators are discussed. A simulation study discussing the empirical behaviour of the estimator is carried out. We illus-trate the procedure by a data set. AMS (2000) subject classification. Primary 62G05; 62P10