A piecewise homogeneous Markov chain process of lung transplantation

BACKGROUND: Markov and semi-Markov models are increasingly used in clinical and public health epidemiology to represent disease processes. We present a Markov model of events following lung transplantation as a case study in clinical epidemiology. METHODS: A five-state discrete-time Markov model with two-way transitions between acute event states is applied to the analysis of 356 lung transplant patients. A two-state continuous time Markov model for chronic disease onset is fitted. Values of transition parameters are estimated by maximum likelihood using numerical methods. RESULTS: Accurate estimates of acute and chonic event rates, and survival probabilities are calculated from transition probabilities. Costs attributed to different acute and chronic states are calculated. CONCLUSIONS: Transition models provide a useful and flexible representation of acute and chronic events and can be used to explore the economic impact of changes in therapy.