State-transition modeling: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--3

Value Health. Sep-Oct 2012;15(6):812-20. doi: 10.1016/j.jval.2012.06.014.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Advisory Committees*
  • Comparative Effectiveness Research
  • Consensus
  • Decision Making, Computer-Assisted*
  • Evidence-Based Practice*
  • Guidelines as Topic
  • Markov Chains
  • Models, Theoretical*