B0164
Title: Generalized semiparametric varying-coefficient model for longitudinal data with applications to treatment switching
Authors: Li Qi - University of North Carolina at Charlotte (United States)
Yanqing Sun - University of North Carolina at Charlotte (United States) [presenting]
Peter Gilbert - University of Washington and Fred Hutchinson Cancer Research Center (United States)
Abstract: The aim is to investigate a generalized semiparametric varying-coefficient model for longitudinal data that can flexibly model three types of covariate effects: constant effects, time-varying effects, and covariate-varying effects. Different link functions can be selected to provide a rich family of models for longitudinal data. The model assumes that the time-varying effects are unspecified functions of time and the covariate-varying effects are parametric functions of an exposure variable specified up to a finite number of unknown parameters. The estimation procedure is developed using the local linear smoothing and the profile weighted least squares estimation techniques. The asymptotic distributions of the proposed estimators are established. A working formula for bandwidth selection is discussed and examined through simulations. Our simulation study shows that the proposed methods have satisfactory finite sample performance. The proposed methods are applied to the ACTG 244 AIDS clinical trial to examine the effects of treatment switching before and after developing 215-mutation. Our analysis shows the benefit of treatment switching before developing 215-mutation while no benefit is observed for patients who switching treatments after developing 215-mutation.
