Doctoral candidate in Biostatistics, Tianmeng Lyu, will present:
Ph.D. Adviser: Xianghua Luo
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. The additive rates model permits time-dependent covariates, yet model estimation 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, and naive individual level imputation such as last covariate value carried forward (LCCF) can result in biased estimation. In this talk, we introduce the proposed method which kernel smooths 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 outperforms the simple LCCF method or the linear interpolation method. In addition, we extend the kernel smoothing approach to the additive-multiplicative rates model with intermittently observed time-dependent covariates. The additive-multiplicative rates model combines the proportional rates model and the additive rates model, and hence allows some covariates to have additive effects and others to have multiplicative effects on the risk of recurrent events. The proposed method is illustrated by analyzing data from an epidemiologic study with the goal to evaluate the effect of streptococcal infections on recurrent pharyngitis episodes.
Refreshments will be served prior to the presentation.