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Activity Number: 430
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #319507 View Presentation
Title: Generalized Semiparametric Varying-Coefficient Models for Longitudinal Data
Author(s): Li Qi* and Yanqing Sun and Peter Gilbert
Companies: Sanofi US and The University of North Carolina at Charlotte and Fred Hutchinson Cancer Research Center
Keywords: Longitudinal data analysis ; Covariate-varying effects ; Link function ; The ACTG 244 AIDS clinical trial ; Treatment switching ; multivariate local linear smoothing

In this paper, we investigate the generalized semiparametric varying-coefficient models for longitudinal data that can flexibly model three types of covariate effects: time-constant effects, time-varying effects, and covariate-varying effects, i.e., the covariate effects that depend on other possibly time-dependent exposure variables.

The estimation procedures are developed using multivariate local linear smoothing and generalized weighted least squares estimation techniques. The asymptotic properties of the proposed estimators are established. The simulation studies show that the proposed methods have satisfactory finite sample performance. ACTG 244 clinical trial of HIV infected patients are applied to examine the effects of antiretroviral treatment switching before and after HIV developsing the 215-mutation.

Authors who are presenting talks have a * after their name.

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