  In biomedical studies, we are often interested in the association between
different types of covariates and the times to disease events. Because the
relationship between the covariates and event times is often complex, standard
survival models that assume a linear covariate effect are inadequate. A
flexible class of models for capturing complex interaction effects among types
of covariates is the varying coefficient models, where the effects of a type of
covariates can be modified by another type of covariates. In this paper, we
study kernel-based estimation methods for varying coefficient additive hazards
models. Unlike many existing kernel-based methods that use a local neighborhood
of subjects for the estimation of the varying coefficient function, we propose
a novel global approach that is generally more efficient. We establish
theoretical properties of the proposed estimators and demonstrate their
superior performance compared with existing local methods through large-scale
simulation studies. To illustrate the proposed method, we provide an
application to a motivating cancer genomic study.
