PharmacoEconomics

, Volume 25, Issue 1, pp 3–6

Rates and Probabilities in Economic Modelling

Transformation, Translation and Appropriate Application
  • Rachael L. Fleurence
  • Christopher S. Hollenbeak
Practical Application

Abstract

Economic modelling is increasingly being used to evaluate the cost effectiveness of health technologies. One of the requirements for good practice in modelling is appropriate application of rates and probabilities. In spite of previous descriptions of appropriate use of rates and probabilities, confusions persist beyond a simple understanding of their definitions. The objective of this article is to provide a concise guide to understanding the issues surrounding the use of rates and probabilities reported in the literature in economic models, and an understanding of when and how to transform them appropriately. The article begins by defining rates and probabilities and shows the essential difference between the two measures. Appropriate conversions between rates and probabilities are discussed, and simple examples are provided to illustrate the techniques and pitfalls. How the transformed rates and probabilities may be used in economic models is then described and some recommendations are suggested.

References

  1. 1.
    Neumann PJ. Evidence-based and value-based formulary guidelines. Health Aff (Millwood) 2004; 23 (1): 124–134CrossRefGoogle Scholar
  2. 2.
    Sculpher M, Fenwick E, Claxton K. Assessing quality in decision analytic cost-effectiveness models: a suggested framework and example of application. Pharmacoeconomics 2000; 17 (5): 461–477PubMedCrossRefGoogle Scholar
  3. 3.
    Buxton MJ, Drummond MF, Van Hout BA, et al. Modelling in economic evaluation: an unavoidable fact of life. Health Econ 1997; 6 (3): 217–227PubMedCrossRefGoogle Scholar
  4. 4.
    Briggs A, Sculpher M. An introduction to Markov modelling for economic evaluation. Pharmacoeconomics 1998; 13 (4): 397–409PubMedCrossRefGoogle Scholar
  5. 5.
    Philips Z, Ginnelly L, Sculpher M, et al. Review of guidelines for good practice in decision-analytic modelling in health technology assessment. Health Technol Assess 2004; 8 (36): 1–172PubMedGoogle Scholar
  6. 6.
    Weinstein MC, O’Brien B, Hornberger J, et al. Principles of good practice for decision analytic modeling in health-care evaluation: report of the ISPOR Task Force on Good Research Practices. Modeling studies. Value Health 2003; 6 (1): 9–17PubMedCrossRefGoogle Scholar
  7. 7.
    Miller DK, Homan SM. Determining transition probabilities: confusion and suggestions. Med Decis Making 1994; 14: 52–58PubMedCrossRefGoogle Scholar
  8. 8.
    Hennekens C, Buring J. Epidemiology in medicine. Toronto (ON): Little Brown and Co., 1987Google Scholar
  9. 9.
    Myers ER, McCrory DC, Nanda K, et al. Mathematical model for the natural history of human papillomavirus infection and cervical carcinogenesis. Am J Epidemiol 2000; 151 (12): 1158–1171PubMedCrossRefGoogle Scholar
  10. 10.
    Hunink M, Glasziou P, Siegel J, et al. Decision making in health and medicine: integrating evidence and values. New York: Cambridge University Press, 2001Google Scholar
  11. 11.
    Welton NJ, Ades AE. Estimation of Markov chain transition probabilities and rates from fully and partially observed data: uncertainty propagation, evidence synthesis, and model calibration. Med Decis Making 2005; 25 (6): 633–645PubMedCrossRefGoogle Scholar

Copyright information

© Adis Data Information BV 2007

Authors and Affiliations

  • Rachael L. Fleurence
    • 1
  • Christopher S. Hollenbeak
    • 2
  1. 1.United BioSource Corporation, Health Care Analytics GroupBethesdaUSA
  2. 2.Surgery and Health Evaluation SciencesPenn State College of MedicineHersheyUSA

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