Polynomial Spline Estimation for Partially Linear Single-Index Additive Hazards Models with Current Status Data

• Activity Number: 538
• Type: Contributed
• Date/Time: Wednesday, August 7, 2013 : 10:30 AM to 12:20 PM
• Sponsor: IMS
• Author(s): Pooneh Pordeli*+ and Xuewen Lu and Murray Burke and Peter X.K. Song
• Companies: University of Calgary and University of Calgary and University of Calgary and University of Michigan
• Keywords: Additive hazards model ; Backfitting algorithm ; B-Spline ; Current status data ; Partially linear single-index model

Abstract

Current status data arise in such areas as demography, economics, epidemiology and survival models. We propose a partially linear single-index additive hazards regression model for current status data. The proposed model can model both linear and nonlinear covariate effects on the hazard and it is a parsimonious model, since it does not use too many parameters. This is particularly important for high dimensional data, which might suffer from "the curse of dimensionality". Our model reduces the dimension of the data through a single-index term. For the estimation, we use B-splines to model the unknown cumulative baseline hazard function and the nonparametric covariate functions. Asymptotic properties of the estimators are derived using the theory of counting processes. Simulation studies are presented to evaluate our method. A renal recovery data set is analyzed to illustrate the usefulness of our proposed model.