A New Approach for Regression Analysis of Multivariate Current Status Data with Informative Censoring

Regression analysis of interval-censored failure time data has recently attracted a great deal of attention partly due to their increasing occurrences in many fields. In this paper, we discuss a type of such data, multivariate current status data, where in addition to the complex interval data structure, one also faces dependent or informative censoring. For inference, a sieve maximum likelihood estimation procedure is developed and the proposed estimators of regression parameters are shown to be asymptotically consistent and efficient. For the implementation of the method, an EM algorithm is provided, and the results from an extensive simulation study demonstrate the validity and good performance of the proposed inference procedure. For an illustration, the proposed approach is applied to a tumorigenicity experiment.