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96年3月02日(五) 3:30 ~4:30 P.M.
演講日期： 96年3月2日(五) 3:30 ~4:30 P.M.
演講者姓名： 王清雲 教授
演講者服務單位：Fred Hutchingson Cancer Research Center, Washington, U.S.A
演講題目：Semiparametric Approaches for Joint modeling of Longitudinal and Survival Data with Time Varying Coefficients
摘要：We study joint modeling of survival and longitudinal data. There are two regression models of interest. The primary model is for survival outcomes, which are assumed to follow a time varying coefficient proportional hazards model. The second model is for longitudinal data, which are assumed to follow a random effects model. Based on the trajectory of a subject's longitudinal data, some covariates in the survival model are functions of the unobserved random effects. Estimated random effects are generally different from the unobserved random effects and hence this leads to covariate measurement error. To deal with covariate measurement error, we propose a local corrected score estimator and a local conditional score estimator. Both approaches are semiparametric methods in the sense that there is no distributional assumption needed for the underlying true covariates. The estimators are shown to be consistent and asymptotically normal. Finite sample properties are assessed via simulation. The approaches are demonstrated by an application to data from an HIV clinical trial.Key words:partial likelihood; Measurement error; Survival
