Semiparametric varying-coefficient regression analysis of recurrent events with applications to treatment switching.

ABSTRACT: This paper investigates the semiparametric statistical methods for recurrent events. The mean number of the recurrent events are modeled with the generalized semiparametric varying-coefficient model that can flexibly model three types of covariate effects: time-constant effects, time-varying effects, and covariate-varying effects. We assume 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. Different link functions can be selected to provide a rich family of models for recurrent events data. The profile estimation methods are developed for the parametric and nonparametric components. The asymptotic properties are established. We also develop

SUBMITTER: Li Y 

PROVIDER: S-EPMC6754098 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature