Identification of Prognostic Factors with Censored Data

A major issue in the analysis of diseases is the identification and
assessment of prognostic factors relevant to the development of the
illness. Statistical analyses within the proportional hazards
framework suffer from a lack flexibility due to stringent model
assumptions such as additivity and time-constancy of effects. In
this paper we use tree based models and varying coefficient models
to allow for detectability of prognostic factors with possibly
nonadditive, nonlinear and time-varying impact on disease
development. Questions concerning model and smoothing-parameter
selection are addressed. An analysis of a dataset of breast cancer
patients demonstrates the ability of these methods to reveal
additional insight into the disease influencing mechanisms.