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Determining Transition Probabilities

Author

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  • Douglas K. Miller
  • Sharon M. Homan

Abstract

Confusion regarding proper use of the terms rate and risk persists in the literature. This has implications for the proper modeling of prognosis and transition between health states in decision analysis and related techniques. The issue is complicated by the plethora of terms related to rate and risk. Although the suggestion to use the terms force and probability as substitutes for rate and risk has some appeal, the change in terminology by itself is unlikely to solve all the confusion or misuse of terms. This paper clarifies the proper definitions and estimations of rates and risks and suggests critical factors for the decision analyst to re member when using, modeling, or interpreting transition rates and risks. Key words: decision models; rate; risk; force; probability; transitions. (Med Decis Making 1994;14:52-58)

Suggested Citation

  • Douglas K. Miller & Sharon M. Homan, 1994. "Determining Transition Probabilities," Medical Decision Making, , vol. 14(1), pages 52-58, February.
    • Handle: RePEc:sae:medema:v:14:y:1994:i:1:p:52-58
    DOI: 10.1177/0272989X9401400107
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    Cited by:

    1. K. Le Lay & E. Myon & S. Hill & L. Riou-Franca & D. Scott & M. Sidhu & D. Dunlop & R. Launois, 2007. "Comparative cost-minimisation of oral and intravenous chemotherapy for first-line treatment of non-small cell lung cancer in the UK NHS system," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 8(2), pages 145-151, June.
    2. Bruce A. Craig & Peter P. Sendi, 2002. "Estimation of the transition matrix of a discrete‐time Markov chain," Health Economics, John Wiley & Sons, Ltd., vol. 11(1), pages 33-42, January.
    3. Jan Jürgensen & Robert Ikenberg & Roger-Axel Greiner & Volker Hösel, 2015. "Cost-effectiveness of modern mTOR inhibitor based immunosuppression compared to the standard of care after renal transplantation in Germany," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 16(4), pages 377-390, May.
    4. Rachael Fleurence & Christopher Hollenbeak, 2007. "Rates and Probabilities in Economic Modelling," PharmacoEconomics, Springer, vol. 25(1), pages 3-6, January.
    5. Francesco Saverio Mennini & Simone Russo & Andrea Marcellusi & Giuseppe Quintaliani & Denis Fouque, 2013. "Economic effects of treatment of chronic kidney disease with low-protein diet," CEIS Research Paper 292, Tor Vergata University, CEIS, revised 10 Oct 2013.
    6. Lartey, Stella T. & Si, Lei & Otahal, Petr & de Graaff, Barbara & Boateng, Godfred O. & Biritwum, Richard Berko & Minicuci, Nadia & Kowal, Paul & Magnussen, Costan G. & Palmer, Andrew J., 2020. "Annual transition probabilities of overweight and obesity in older adults: Evidence from World Health Organization Study on global AGEing and adult health," Social Science & Medicine, Elsevier, vol. 247(C).
    7. Thomas Hoffmann & Helmut Brunner, 2004. "Model for simulation of HIV/AIDS and cost-effectiveness of preventing non-tuberculous mycobacterial (MAC)-disease," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 5(2), pages 129-135, May.
    8. Nicola J. Cooper & Alex J. Sutton & Keith R. Abrams & David Turner & Allan Wailoo, 2004. "Comprehensive decision analytical modelling in economic evaluation: a Bayesian approach," Health Economics, John Wiley & Sons, Ltd., vol. 13(3), pages 203-226, March.

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