The Markov model is an analytical framework that is frequently used in decision analysis, and is probably the most common type of model used in economic evaluation of healthcare interventions. Markov models use disease states to represent all possible consequences of an intervention of interest. These are mutually exclusive and exhaustive and so each individual represented in the model can be in one and only one of these disease states at any given time. Examples of health states that might be included in a simple Markov model for a cancer intervention are: progression-free, post-progression and dead. Individuals move (‘transition’) between disease states as their condition changes over time. Time itself is considered as discrete time periods called ‘cycles’ (typically a certain number of weeks or months), and movements from one disease state to another (in the subsequent time period) are represented as ‘transition probabilities’. Time spent in each disease state for a single model cycle (and transitions between states) is associated with a cost and a health outcome. Costs and health outcomes are aggregated for a modelled cohort of patients over successive cycles to provide a summary of the cohort experience, which can be compared with the aggregate experience of a similar cohort, for example one receiving a different (comparator) intervention for the same condition. Markov models are limited in their limited ability to ‘remember’ what occurred in previous model cycles. For example the probability of what occurs after disease progression may be related to the time to progression. Although to some extent health states can be defined ingeniously to address this complexity, other modelling approaches may be required for more complex diseases.
How to cite: Markov Model [online]. (2016). York; York Health Economics Consortium; 2016. https://yhec.co.uk/glossary/markov-model/