A nonparametric multiplicative hazard model for event history analysis

A major issue in exploring and analyzing life history data with
multiple states and events is the development and availability of
flexible methods that allow simultaneous incorporation and
estimation of baseline hazards, detection and modelling of
nonlinear functional forms of covariates and time-varying effects,
and the possibility to include time-dependent covariates. In this
paper we consider a nonparametric multiplicative hazard model that
takes into account these aspects. Embedded in the counting process
approach, estimation is based on penalized likelihoods and splines.
The methods are illustrated by two real data applications, one to a
more conventional survival data set with two absorbing states, and
one to more complex sleep-electroencephalography data with multiple
recurrent states of sleep.