Additive rates model for recurrent event data with intermittently observed time-dependent covariates.

ABSTRACT: Various regression methods have been proposed for analyzing recurrent event data. Among them, the semiparametric additive rates model is particularly appealing because the regression coefficients quantify the absolute difference in the occurrence rate of the recurrent events between different groups. Estimation of the additive rates model requires the values of time-dependent covariates being observed throughout the entire follow-up period. In practice, however, the time-dependent covariates are usually only measured at intermittent follow-up visits. In this paper, we propose to kernel smooth functions involving time-dependent covariates across subjects in the estimating function, as opposed to imputing individual covariate trajectories. Simulation studies show that the proposed method out

SUBMITTER: Lyu T 

PROVIDER: S-EPMC8608417 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature