Markov Model

The Markov model is a widely used analytical framework in decision analysis, particularly common in the economic evaluation of healthcare interventions. Markov models utilize disease states to represent all possible outcomes of an intervention. These states are mutually exclusive and exhaustive, meaning each individual in the model can only occupy one disease state at any given time. For instance, a simple Markov model for a cancer intervention might include health states such as progression-free, post-progression, and dead.

Key features of Markov models include:

– Disease States: Represent all possible health outcomes, ensuring each individual is in only one state at a time.

– Transitions: Individuals move between states (transition) as their health condition changes over time.

– Cycles: Time is divided into discrete periods, or cycles, typically lasting a certain number of weeks or months.

– Transition Probabilities: Movements from one state to another in subsequent cycles are represented as probabilities.

Each cycle in the model accounts for the time spent in each disease state, with associated costs and health outcomes. These are aggregated over successive cycles for a cohort of patients to summarize the overall experience. This aggregate experience can then be compared with a similar cohort receiving a different intervention for the same condition.

Markov models are advantageous for their structured approach but have limitations, particularly in their ability to ‘remember’ past events. For example, the probability of events after disease progression may depend on the time to progression, which Markov models handle less effectively. While health states can be ingeniously defined to address some of these complexities, other modeling approaches may be necessary for more complex diseases.