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Title: Regression analysis of a semi-competing risks model when all transition times are interval-censored Authors:  Jinheum Kim - University of Suwon (Korea, South) [presenting]
Abstract: In biomedical or clinical studies, semi-competing risks data in which one type of event may censor an other event, but not vice versa, are often encountered. We propose a multi-state model for analyzing these semi-competing risks data in the presence of interval censoring on both intermediate and terminal events. The proposed model can reflect diversities for which real data might frequently possess. We utilize the Cox proportional hazards model with a frailty effect to incorporate dependency between transitions of states. Weight allocations on sub-intervals of censored intervals are also used to construct the modified likelihood functions. Marginalization of the full likelihood is accomplished using adaptive importance sampling, and the optimal solution of the regression parameters is achieved through the iterative quasi-Newton algorithm. The proposed methodology is illustrated on several simulation studies and real data.