Mixture models for cancer survival analysis: Application to population-based data with covariates

The interest in estimating the probability of cure has been increasing in c ancer survival analysis as the curability of many cancer diseases is becomi ng a reality. Mixture survival models provide a way of modelling time to de ath when cure is possible, simultaneously estimating death hazard of fatal cases and the proportion of cured case. In this paper we propose an applica tion of a parametric mixture model to relative survival rates of colon canc er patients from the Finnish population-based cancer registry, and includin g major survival determinants as explicative covariates. Disentangling surv ival into two different components greatly facilitates the analysis and the interpretation of the role of prognostic factors on survival patterns. For example, age plays a different role in determining, from one side, the pro bability of cure, and, from the other side, the life expectancy of fatal ca ses. The results support the hypothesis that observed survival trends are r eally due to a real prognostic gain for more recently diagnosed patients. C opyright (C) 1999 John Wiley & Sons, Ltd.