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Output correlations in probabilistic models with multiple alternatives

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  • Klemen Naveršnik

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Abstract

A comprehensive cost-effectiveness decision model will often go beyond a one-to-one comparison and will include a number of competing alternatives. Only a simultaneous assessment of all relevant treatment alternatives avoids comparing average cost-effectiveness ratios and allows a truly incremental analysis. Two issues arise if the analysis is probabilistic, namely, the occurrence of output correlations and difficulty in presenting the results. I have examined the role of output correlations using a screening model with eight alternatives and have shown that specifically cost–cost and quality-adjusted life years (QALY)–QALY correlations between alternatives have a major impact on decision uncertainty, as measured by the probability of the cost-effectiveness and expected value of perfect information. In particular, the latter strongly depends on between-alternative output correlations. This analysis shows that both the expected value of perfect information plots and acceptability curves/frontiers are sensitive to output correlations and thus appropriate for presentation of multiple alternatives. To avoid confusing statistical significance and economic importance I propose that acceptability curves be augmented by incremental net-benefit density plots at a given willingness to pay threshold. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Klemen Naveršnik, 2015. "Output correlations in probabilistic models with multiple alternatives," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 16(2), pages 133-139, March.
  • Handle: RePEc:spr:eujhec:v:16:y:2015:i:2:p:133-139
    DOI: 10.1007/s10198-013-0558-0
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    References listed on IDEAS

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    1. Elisabeth Fenwick & Karl Claxton & Mark Sculpher, 2001. "Representing uncertainty: the role of cost‐effectiveness acceptability curves," Health Economics, John Wiley & Sons, Ltd., vol. 10(8), pages 779-787, December.
    2. Michał Jakubczyk & Bogumił Kamiński, 2010. "Cost‐effectiveness acceptability curves – caveats quantified," Health Economics, John Wiley & Sons, Ltd., vol. 19(8), pages 955-963, August.
    3. Claxton, Karl, 1999. "The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies," Journal of Health Economics, Elsevier, vol. 18(3), pages 341-364, June.
    4. Joshua Graff Zivin, 2001. "Cost‐effectiveness analysis with risk aversion," Health Economics, John Wiley & Sons, Ltd., vol. 10(6), pages 499-508, September.
    5. Aaron A. Stinnett & John Mullahy, 1998. "Net Health Benefits: A New Framework for the Analysis of Uncertainty in Cost-Effectiveness Analysis," NBER Technical Working Papers 0227, National Bureau of Economic Research, Inc.
    6. Mohan V. Bala & Gary A. Zarkin & Josephine Mauskopf, 2008. "Presenting results of probabilistic sensitivity analysis: the incremental benefit curve," Health Economics, John Wiley & Sons, Ltd., vol. 17(3), pages 435-440, March.
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    More about this item

    Keywords

    Output correlations; Multiple alternatives; Uncertainty analysis; Probabilistic model; C5; I1;

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • I1 - Health, Education, and Welfare - - Health

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