Disease Modelling of chronic diseases such as diabetes or asthma plays an important role in medical decision making. State transition models are the most frequently used method. The objective is to illustrate the elements and the most important underlying procedures for designing a decision analytic Markov model with only three-states.
Method: Being "healthy" can be interpreted as a norm state, being "sick" as a temporary state and "dead" as an absorbing state. Transitions with accompanying transition probabilities that allow a cohort of model objects "to flow" between the cumulative exhaustive and mutually exclusive states complete the model structure. Half-cycle correction helps in overcoming the fitting problem of the discrete time valuation of Markov models. A model with the three states healthy, sick and dead is the easiest way to define a reasonable model that covers almost all aspects of a Markov disease model. The absorbing state dead helps in terminating a model. The temporary state sick acts as an event counter and the state healthy serves as a reservoir of modelling objects. The definition of the number and length of cycles completes the definition of a simple state transition model. Additional supplementary material with a functional sample model is provided.
Keywords: Computer simulation; chronic diseases; decision analysis; markov processes.