Generalized Odds Rate Frailty Models for Current Status Data with Informative Censoring

Current status data occur in many areas and their analysis has recently attached a great deal of attention.In this paper, we consider regression analysis of current status data in the presence of informative censoring, for which most of the existing methods either apply only to limited situations or are computationally unstable.Corresponding to this, we propose a new sieve maximum likelihood estimation procedure under the class of semiparametric, generalized odds rate frailty models, and in the method, the latent variable is employed to describe the informative censoring or relationship between the failure time of interest and the censoring time.For the determination of the proposed estimators, a novel EM algorithm is developed, and the asymptotic consistency and normality of the proposed estimators are established.An extensive simulation study is conducted