Pierpaolo De Blasi (University of Turin)


TITLE: Semiparametric models for regression analysis of competing risks
data.

ABSTRACT: We develop a semiparametric formulation
of the competing risks model, where cause-specific hazards (CSHs) are
modelled via the conditional probability of a failure type and the overall
hazard rate. Such formulation is then adopted in a proportional regression
model on CSHs with a logistic relative risk function.
Frequentist estimation based on the partial
likelihood is described together with the derivation of large sample
properties. We also study the tail behaviour of the partial likelihood by
giving sufficient conditions for exponentially decreasing tails. For
illustration, we consider the estimation of the prevalence of risks in a
carcinogenesis experiment.