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

Stat Med. 1999 Feb 28;18(4):441-54. doi: 10.1002/(sici)1097-0258(19990228)18:4<441::aid-sim23>3.0.co;2-m.


The interest in estimating the probability of cure has been increasing in cancer survival analysis as the curability of many cancer diseases is becoming a reality. Mixture survival models provide a way of modelling time to death when cure is possible, simultaneously estimating death hazard of fatal cases and the proportion of cured case. In this paper we propose an application of a parametric mixture model to relative survival rates of colon cancer patients from the Finnish population-based cancer registry, and including major survival determinants as explicative covariates. Disentangling survival 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 probability of cure, and, from the other side, the life expectancy of fatal cases. The results support the hypothesis that observed survival trends are really due to a real prognostic gain for more recently diagnosed patients.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Age Factors
  • Biometry
  • Colonic Neoplasms / mortality*
  • Finland / epidemiology
  • Follow-Up Studies
  • Humans
  • Life Expectancy
  • Likelihood Functions
  • Probability
  • Registries
  • Regression Analysis
  • Sensitivity and Specificity
  • Survival Analysis*