Markov model

A Markov model is a type of analytical framework commonly used in health economic evaluations to simulate a patient cohort’s journey through a disease over time. The model represents all possible health outcomes as a set of mutually exclusive and exhaustive health states, meaning a patient can be in one and only one state at any given point. For instance, in a cancer model, these states might include “progression-free,” “post-progression,” and “dead.”
Individuals move (‘transition’) between disease states as their condition changes over time. Time itself is considered as discrete 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, e.g. one receiving a different (comparator) intervention for the same condition. Markov models are limited in their ability to ‘remember’ what occurred in previous model cycles (e.g. 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.