Markov Models in Medical Decision Making

TLDR

Markov models are useful for decision problems with continuous risk over time, important event timing, and repeated events, overcoming limitations of conventional decision trees that require unrealistic simplifications. Markov models can be evaluated using matrix algebra, cohort simulation, or Monte Carlo simulation, and the newer Markov‑cycle tree representation combines tree structure with simulation methods. Markov models’ ability to represent repetitive events and time‑dependent probabilities and utilities yields more accurate modeling of clinical settings involving these issues.

Abstract

Markov models are useful when a decision problem involves risk that is continuous over time, when the timing of events is important, and when important events may happen more than once. Representing such clinical settings with conventional decision trees is difficult and may require unrealistic simplifying assumptions. Markov models assume that a patient is always in one of a finite number of discrete health states, called Markov states. All events are represented as transitions from one state to another. A Markov model may be evaluated by matrix algebra, as a cohort simulation, or as a Monte Carlo simulation. A newer representation of Markov models, the Markov-cycle tree, uses a tree representation of clinical events and may be evaluated either as a cohort simulation or as a Monte Carlo simulation. The ability of the Markov model to represent repetitive events and the time dependence of both probabilities and utilities allows for more accurate representation of clinical settings that involve these issues.