State-transition modeling: A report of the ISPOR-SMDM modeling good research practices task force-3

Uwe Siebert, Oguzhan Alagoz, Ahmed M. Bayoumi, Beate Jahn, Douglas K. Owens, David J. Cohen, Karen M Kuntz

Research output: Contribution to journalArticlepeer-review

287 Scopus citations


State-transition modeling is an intuitive, flexible, and transparent approach of computer-based decision-analytic modeling including both Markov model cohort simulation and individual-based (first-order Monte Carlo) microsimulation. Conceptualizing a decision problem in terms of a set of (health) states and transitions among these states, state-transition modeling is one of the most widespread modeling techniques in clinical decision analysis, health technology assessment, and health-economic evaluation. State-transition models 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. The goal of this article was to provide consensus-based guidelines for the application of state-transition models in the context of health care. We structured the best practice recommendations in the following sections: choice of model type (cohort vs. individual-level model), model structure, model parameters, analysis, reporting, and communication. In each of these sections, we give a brief description, address the issues that are of particular relevance to the application of state-transition models, give specific examples from the literature, and provide best practice recommendations for state-transition modeling. These recommendations are directed both to modelers and to users of modeling results such as clinicians, clinical guideline developers, manufacturers, or policymakers.

Original languageEnglish (US)
Pages (from-to)812-820
Number of pages9
JournalValue in Health
Issue number6
StatePublished - Sep 2012

Bibliographical note

Funding 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
  • guidelines
  • state-transition modeling


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