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Assessing the Sensitivity of Decision-Analytic Results to Unobserved Markers of Risk: Defining the Effects of Heterogeneity Bias

Author

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  • Karen M. Kuntz

    (Center for Risk Analysis, Department of Health Policy and Management, Harvard School of Public Health, Boston, Massachusetts)

  • Sue J. Goldie

    (Center for Risk Analysis, Department of Health Policy and Management, Harvard School of Public Health, Boston, Massachusetts)

Abstract

An important assumption made when constructing a Markov model is that all persons residing in a health state are identical. Failure to adjust for population heterogeneity caused by unobserved variables can therefore cause bias in model results. The authors used a simple model to evaluate the potential impact of heterogeneity bias, defined as the percentage change in the life expectancy gain with an intervention predicted by a model that does not adjust for heterogeneity (unadjusted model) compared to one that does (adjusted model). The life expectancy gains were consistently greater in the unadjusted model compared to the adjusted model (positive bias). For an annual probability of developing disease of 1%, the heterogeneity bias exceeded 50% when the relative risk of disease with the heterogeneity factor versus without the factor was greater than 15 and the prevalence of the heterogeneity factor was between 5% and 25%. When constructing decision-analytic models, analysts need to be cognizant of unobserved factors that introduce heterogeneity into the cohort. This analysis provides a general framework to determine when issues of heterogeneity may be important.

Suggested Citation

  • Karen M. Kuntz & Sue J. Goldie, 2002. "Assessing the Sensitivity of Decision-Analytic Results to Unobserved Markers of Risk: Defining the Effects of Heterogeneity Bias," Medical Decision Making, , vol. 22(3), pages 218-227, June.
    • Handle: RePEc:sae:medema:v:22:y:2002:i:3:p:218-227
    DOI: 10.1177/0272989X0202200310
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    Citations

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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. Giovanni S. P. Malloy & Jeremy D. Goldhaber-Fiebert & Eva A. Enns & Margaret L. Brandeau, 2021. "Predicting the Effectiveness of Endemic Infectious Disease Control Interventions: The Impact of Mass Action versus Network Model Structure," Medical Decision Making, , vol. 41(6), pages 623-640, August.
    4. 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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