Speaker Biography:

Bin Nan, Chancellor's Professor of Statistics. Bin Nan’s research interests are in various areas of statistics and biostatistics. He studies semiparametric inference, failure time and survival analysis, longitudinal data, missing data and two-phase sampling designs, high-dimensional data analysis, and machine learning methodology. He is also collaborating on projects in the areas of epidemiology, bioinformatics, and brain imaging.

Abstract:

We propose a nonparametric bivariate time-varying coefficient model for longitudinal measurements with the occurrence of a terminal event that is subject to right censoring. The time-varying coefficients capture the longitudinal trajectories of covariate effects along with both the followup time and the residual lifetime. The proposed model extends the parametric conditional approach given terminal event time in recent literature, and thus avoids potential model misspecification. We consider a kernel smoothing method for estimating regression coefficients in our model and use cross-validation for bandwidth selection, applying undersmoothing in the final analysis to eliminate the asymptotic bias of the kernel estimator. We show that the kernel estimates follow a finite-dimensional normal distribution asymptotically under mild regularity conditions, and provide an easily computable sandwich covariance matrix estimator. We conduct extensive simulations that show desirable performance of the proposed approach, and apply the method to analyzing the medical cost data for patients with end-stage renal disease.