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.
Sharples
L D
LD
MRC Biostatistics Unit, Cambridge, UK.
Taylor
G I
GI
Faddy
M
M
D000465:Algorithms; D018450:Disease Progression; D006801:Humans; D016013:Likelihood Functions; D016040:Lung Transplantation / Q000009:adverse effects* / Q000191:economics; D008390:Markov Chains; D011183:Postoperative Complications / Q000453:epidemiology*; D012307:Risk Factors