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A Critique and Impact Analysis of Decision Modeling Assumptions

Author

Listed:
  • Jonathan Karnon

    (School of Health and Related Research, University of Sheffield, Sheffield, UK, j.karnon@sheffield.ac.uk)

  • Alan Brennan

    (School of Health and Related Research, University of Sheffield, Sheffield, UK)

  • Ron Akehurst

    (School of Health and Related Research, University of Sheffield, Sheffield, UK)

Abstract

Background . Numerous guidelines have been published defining good practice for the conduct of economic evaluations in general and model-based evaluations in particular. The extent to which guidelines are accepted is unknown, and the impact of deviations from good practice is not generally recorded. The authors identified 4 specific issues in applied studies that may affect the accuracy and comparability of different evaluations. Methods . A descriptive analysis of 4 modeling issues (inclusion of incident cases over a model time horizon, appropriate time horizon, parsimonious model structure, and the handling of age-specific subgroups) is presented. A case study model is analyzed to illustrate the quantitative impact of 3 of the issues. Results . In the case study model, alternative specifications of the modeling framework are shown to alter the estimated cost-effectiveness by large percentages. The combined effect of including incident cases and reduced follow-up yielded the highest divergence from the reference case results, by between 20% and 40%, depending on the age group. Reference case results of an age-weighted population were almost 14% different from the middle single age cohort. Discussion . The identified issues are all generalizable to a wide range of treatment areas and are, or should be, addressed by evaluative guidelines. The authors call for the continued development, dissemination, and application of guidelines for the conduct of economic evaluation in general and model-based economic evaluations in particular.

Suggested Citation

  • Jonathan Karnon & Alan Brennan & Ron Akehurst, 2007. "A Critique and Impact Analysis of Decision Modeling Assumptions," Medical Decision Making, , vol. 27(4), pages 491-499, July.
    • Handle: RePEc:sae:medema:v:27:y:2007:i:4:p:491-499
    DOI: 10.1177/0272989X07300606
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    References listed on IDEAS

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    1. Michael Drummond, 1992. "Cost‐effectiveness guidelines for reimbursement of pharmaceuticals: Is economic evaluation ready for its enhanced status?," Health Economics, John Wiley & Sons, Ltd., vol. 1(2), pages 85-92, July.
    2. Salkeld, Glenn & Phongsavan, Philayrath & Oldenburg, Brian & Johannesson, Magnus & Convery, Paula & Graham-Clarke, Peita & Walker, Sheila & Shaw, John, 1997. "The cost-effectiveness of a cardiovascular risk reduction program in general practice," Health Policy, Elsevier, vol. 41(2), pages 105-119, August.
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    Citations

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    Cited by:

    1. Karen M. Kuntz & Elisabeth Fenwick & Andrew Briggs, 2010. "Appropriate Cohorts for Cost-Effectiveness Analysis: To Mix or Not to Mix?," Medical Decision Making, , vol. 30(4), pages 424-425, July.
    2. Christopher McCabe, 2007. "Guidance on Good Practice in Cost-Effectiveness Modeling: Is More Needed?," Medical Decision Making, , vol. 27(4), pages 350-351, July.
    3. Lee Goldman & Jean-Michel Gaspoz, 2008. "Cost-Effectiveness of Clopidogrel: Seeing through the Smoke," Medical Decision Making, , vol. 28(6), pages 803-809, November.
    4. Jonathan Karnon & Alan Brennan & Ron Akehurst, 2010. "Decision Modeling to Inform Decision Making: Seeing the Wood for the Trees," Medical Decision Making, , vol. 30(3), pages 20-22, May.

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