A0434
Title: Semiparametric additive time-varying coefficients model for longitudinal data with censored time origin
Authors: Yanqing Sun - University of North Carolina at Charlotte (United States) [presenting]
Qiong Shou - Merck China (China)
Peter Gilbert - University of Washington and Fred Hutchinson Cancer Research Center (United States)
Fei Heng - University of North Florida (United States)
Xiyuan Qian - East China University of Science and Technology (China)
Abstract: Statistical analysis of longitudinal data often involves modelling treatment effects on clinically relevant longitudinal biomarkers since an initial event (the time origin). In some studies, including preventive HIV vaccine efficacy trials, some participants have biomarkers measured starting at the time origin. In contrast, others have biomarkers measured starting later with the time origin unknown The semiparametric additive time-varying coefficient model is investigated where the effects of some covariates vary nonparametrically with time while the effects of others remain constant Weighted profile least squares estimators coupled with kernel smoothing are developed The method uses the expectation maximization approach to deal with the censored time origin The Kaplan-Meier estimator and other failure time regression models, such as the Cox model, can be utilized to estimate the distribution and the conditional distribution of left-censored event time related to the censored time origin Asymptotic properties of the parametric and nonparametric estimators and consistent asymptotic variance estimators are derived A two-stage estimation procedure for choosing weight is proposed to improve estimation efficiency Numerical simulations are conducted to examine the finite sample properties of the proposed estimators The method is applied to analyze data from the Merck 023/HVTN 502 Step HIV vaccine study.
