Seminar by Prof. Peter McCullagh

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 talk is concerned with a general technique--temporal realignment--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 the sequence of health outcomes to be used clinically for predicting the subsequent trajectory, including the residual survival time. Details available at
http://www.stat.uchicago.edu/~pmcc/reports/revival.pdf