Nonparametric identification and estimation of current status data in the presence of death

摘要

We present a nonparametric study of current status data in the presence of death. Such data arise from biomedical investigations in which patients are examined for the onset of a certain disease, for example, tumor progression, but may die before the examination. A key difference between such studies on human subjects and the survival-sacrifice model in animal carcinogenicity experiments is that, due to ethical and perhaps technical reasons, deceased human subjects are not examined, so that the information on their disease status is lost. We show that, for current status data with death, only the overall and disease-free survival functions can be identified, whereas the cumulative incidence of the disease is not identifiable. We describe a fast and stable algorithm to estimate the disease-free survival function by maximizing a pseudo-likelihood with plug-in estimates for the overall survival rates. It is then proved that the global rate of convergence for the nonparametric maximum pseudo-likelihood estimator is equal to O-p(n(-1/3)) or the convergence rate of the estimated overall survival function, whichever is slower. Simulation studies show that the nonparametric maximum pseudo-likelihood estimators are fairly accurate in small- to medium-sized samples. Real data from breast cancer studies are analyzed as an illustration.