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Title: Semiparametric transformation models for interval-censored data in the presence of a cure fraction Authors:  Chyong-Mei Chen - National Yang-Ming University, Institute of Public Health (Taiwan) [presenting]
Abstract: Mixed case interval censored data arises when the event of interest is known only to occur within an interval induced by a sequence of random examination times. Such data are commonly encountered in disease research with longitudinal follow-up. Furthermore, the medical treatment has progressed over the last decade with an increasing proportion of patients being cured for many types of diseases. Thus, interest has grown in cure models for survival data which hypothesizes a certain proportion of subjects in the population are not expected to experience the events of interest. A two-component mixture cure model for regression analysis of mixed interval censored data is considered. The first component is a logistic regression model that describes the cure rate, and the second component is a semiparametric transformation model that describes the distribution of event time for the uncured subjects. Semiparametric maximum likelihood estimation for the considered model is proposed. An EM type algorithm is developed for obtaining the semiparametric maximum likelihood estimators (SPMLE) of regression parameters and establishes the large sample properties. Extensive simulation studies indicate that the SPMLE performs satisfactorily in a wide variety of settings. A medical study is provided for illustration.