CURE MODEL WITH CURRENT STATUS DATA

Current status data arise when only random censoring time and event status at censoring are observable. We consider current status data under the cure model, where a proportion of the subjects are not susceptible to the event of interest. We assume a generalized linear model for the cure probability. For subjects not cured, the linear and partly linear Cox proportional hazards models are used to model the survival risk. We propose estimation using the (penalized) maximum likelihood approach. It is shown that estimates of the parametric regression coefficients are root n consistent, asymptotically normal and efficient. The nonparametric baseline function and nonparametric covariate effect can be estimated with n(1/3) convergence rate. We propose inference for estimates of the regression coefficients using the weighted bootstrap. Simulation studies are used to assess finite sample performance of the proposed estimates. We analyze the Calcification data using the proposed approach.