Generalized semiparametric varying-coefficient model for longitudinal data with applications to adaptive treatment randomizations.

ABSTRACT: This article investigates a generalized semiparametric varying-coefficient model for longitudinal data that can flexibly model three types of covariate effects: time-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 local linear smoothing and profile weighted least squares estimation techniques. Hypothesis testing procedures are developed to test the parametric functions of the covariate-varying effects. The asym

SUBMITTER: Qi L 

PROVIDER: S-EPMC5459686 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature