Least squares estimation of varying-coefficient hazard regression with application to breast cancer dose-intensity data

Abstract

To enhance modeling flexibility, the authors propose a nonparametric hazard regression model, for which the ordinary and weighted least squares estimation and inference procedures are studied. The proposed model does not assume any parametric specifications on the covariate effects, which is suitable for exploring the nonlinear interactions between covariates, time and some exposure variable. The authors propose the local ordinary and weighted least squares estimators for the varying-coefficient functions and establish the corresponding asymptotic normality properties. Simulation studies are conducted to empirically examine the finite-sample performance of the new methods, and a real data example from a recent breast cancer study is used as an illustration. © 2009 Statistical Society of Canada.