Frailty model approach for the clustered interval-censored data with informative censoring

Interval censoring is frequently encountered in many clinical trials with periodic follow-up as the time of a specific event, such as death, is determined within an interval. Most existing methodologies with regression analysis were extended and developed under the assumption of non-informative censoring mechanism. However, this assumption sometimes does not hold. Subsequently, it is impossible to test the dependence or independence assumption of the censoring mechanism. One remedy to circumvent these difficulties is to impose extra assumptions or modeling. In this article, we employ the Cox proportional hazards models with a shared frailty effect incorporated with clustered interval-censored data for which there exists a dependency between the failure and visiting times. The parameters are estimated via the EM algorithm. Simulations are performed to investigate the finite-sample properties of the proposed method. Finally, two real datasets are analyzed to demonstrate our methodologies.