A UNIFIED APPROACH FOR MODELING LONGITUDINAL AND FAILURE TIME DATA, WITH APPLICATION IN MEDICAL MONITORING

This paper considers biomedical problems in which a sample of subjects , for example clinical patients, is monitored through time for purpose s of individual prediction. Emphasis is on situations in which the mon itoring generates data both in the form of a time series and in the fo rm of events (development of a disease, death, etc.) observed on each subject over specified intervals of time. A Bayesian approach to the c ombined modeling of both types of data for purposes of prediction is p resented. The proposed method merges ideas of Bayesian hierarchical mo deling, nonparametric smoothing of time series data, survival analysis , and forecasting into a unified framework. Emphasis is on flexible mo deling of the time series data based on stochastic process theory. The use of Markov Chain Monte Carlo simulation to calculate the predictio ns of interest is discussed. Conditional independence graphs are used throughout for a clear presentation of the models. An application in t he monitoring of transplant patients is presented.