State-transition modeling (STM) is an intuitive, flexible, and transparent approach of computer-based decision-analytic modeling, including both Markov model cohort simulation as well as individual-based (first-order Monte Carlo) microsimulation. Conceptualizing a decision problem in terms of a set of (health) states and transitions among these states, STM is one of the most widespread modeling techniques in clinical decision analysis, health technology assessment, and health-economic evaluation. STMs have been used in many different populations and diseases, and their applications range from personalized health care strategies to public health programs. Most frequently, state-transition models are used in the evaluation of risk factor interventions, screening, diagnostic procedures, treatment strategies, and disease management programs.
Bibliographical noteFunding Information:
Source of financial support: This Task Force was supported by ISPOR. U.S. and B.J were supported in part by the Oncotyrol Center for Personalized Cancer Medicine (COMET center).
- Markov models
- decision-analytic modeling
- state-transition modeling