  Medical investigations focusing on patient survival often generate not only a
failure time for each patient but also a sequence of measurements on patient
health at annual or semi-annual check-ups while the patient remains alive. Such
a sequence of random length accompanied by a survival time is called a survival
process. Ordinarily robust health is associated with longer survival, so the
two parts of a survival process cannot be assumed independent. This paper is
concerned with a general technique---time reversal---for constructing
statistical models for survival processes. A revival model is a regression
model in the sense that it incorporates covariate and treatment effects into
both the distribution of survival times and the joint distribution of health
outcomes. It also allows individual health outcomes to be used clinically for
predicting the subsequent survival time.
