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Abstract:
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It is sparse in literature for analysis of joint modeling of longitudinal outcome and interval-censored survival time, in which, subjects are followed-up intermittently such that the longitudinal variables of interest are repeatedly measured and the event status is examined at the same inspection time. In this talk, we have equal interest in both longitudinal variables and failure time and propose a joint model incorporating a random effect to account for the dependence between the repeated measurements and failure time. A semiparametric regression model and the Cox frailty model are adopted for longitudinal data and survival data, respectively. The unknown baseline mean function and baseline cumulative hazard function are approximated by splines. The EM algorithm is applied to obtain the maximum likelihood estimators. Extensive simulation studies are conducted to assess the performance of the proposed estimators and a real data set is analyzed to illustrate the approach.
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