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The Impact of Ignoring Population Heterogeneity when Markov Models are Used in Cost-Effectiveness Analysis

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  • Gregory S. Zaric

Abstract

Many factors related to the spread and progression of diseases vary throughout a population. This heterogeneity is frequently ignored in cost-effectiveness analyses by using aver-age or representative values or by considering multiple risk groups. The author explores the impact that such simplifying assumptions may have on the results and interpretation of cost-effectiveness analyses when Markov models are used to calculate the costs and health impact of interventions. A discrete-time Markov model for a disease is defined, and 5 potential interventions are considered. Health benefits, costs, and incremental cost-effectiveness ratios are calculated for each intervention. It is assumed that the population is heterogeneous with respect to the probability of becoming sick. Ignoring this heterogeneity may lead to optimistic or pessimistic estimates of cost-effectiveness ratios, depending on the intervention and, in some cases, the parameter values. Implications are discussed of this finding on the use of league tables and on comparisons of cost-effectiveness ratios versus commonly accepted threshold values.

Suggested Citation

  • Gregory S. Zaric, 2003. "The Impact of Ignoring Population Heterogeneity when Markov Models are Used in Cost-Effectiveness Analysis," Medical Decision Making, , vol. 23(5), pages 379-386, September.
    • Handle: RePEc:sae:medema:v:23:y:2003:i:5:p:379-386
    DOI: 10.1177/0272989X03256883
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    References listed on IDEAS

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    1. Kahn, J.G., 1996. "The cost-effectiveness of HIV prevention targeting: How much more bang for the buck?," American Journal of Public Health, American Public Health Association, vol. 86(12), pages 1709-1712.
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    Cited by:

    1. Olivier Ethgen & Baudouin Standaert, 2012. "Population–versus Cohort–Based Modelling Approaches," PharmacoEconomics, Springer, vol. 30(3), pages 171-181, March.
    2. Gregory S. Zaric, 2008. "Optimal drug pricing, limited use conditions and stratified net benefits for Markov models of disease progression," Health Economics, John Wiley & Sons, Ltd., vol. 17(11), pages 1277-1294, November.
    3. Oren Shavit & Moshe Leshno & Assaf Goldberger & Amir Shmueli & Amnon Hoffman, 2007. "It’s Time to Choose the Study Design!," PharmacoEconomics, Springer, vol. 25(11), pages 903-911, November.
    4. Grace Hui-Min Wu & Shu-Hui Chang & Tony Hsiu-Hsi Chen, 2008. "A Bayesian Random-Effects Markov Model for Tumor Progression in Women with a Family History of Breast Cancer," Biometrics, The International Biometric Society, vol. 64(4), pages 1231-1237, December.
    5. Gregory S. Zaric, 2008. "Optimal drug pricing, limited use conditions and stratified net benefits for Markov models of disease progression," Health Economics, John Wiley & Sons, Ltd., vol. 17(11), pages 1277-1294.

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