Abstract
In survival analysis, information of covariates has been used to evaluate their importance 1Z1 predicting the survival probability oif a given individual. This paper develops a stochastic survival model which incorporates covar&tes and allows two states of health and several coss1peting risks of death. The transition intensity functions can have an exponential or Weibull form but depend upon the covariates. Other generalizations of the model are presented. The model of Lagakos (1976) is a special case of the models proposed here. The asymptotic theory of the maximum likelihood estimates and a goodness-of-fit procedure is diseussed along with tS1e estimation of the survival, transition and competing risks probabilities. These models are applicable to data collected in a clinical trial or prospective study and can distinguish between end-ofstudy and loss-to-follow-up censoring. An application is given which analyzes the survival of patients in a heart transplant program.
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