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Validation of a Cardiovascular Disease Policy Microsimulation Model Using Both Survival and Receiver Operating Characteristic Curves

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  • Ankur Pandya
  • Stephen Sy
  • Sylvia Cho
  • Sartaj Alam
  • Milton C. Weinstein
  • Thomas A. Gaziano

Abstract

Background. Despite some advances, cardiovascular disease (CVD) remains the leading cause of death and healthcare costs in the United States. We therefore developed a comprehensive CVD policy simulation model that identifies cost-effective approaches for reducing CVD burden. This paper aims to: 1) describe our model in detail; and 2) perform model validation analyses. Methods. The model simulates 1,000,000 adults (ages 35 to 80 years) using a variety of CVD-related epidemiological data, including previously calibrated Framingham-based risk scores for coronary heart disease and stroke. We validated our microsimulation model using recent National Health and Nutrition Examination Survey (NHANES) data, with baseline values collected in 1999-2000 and cause-specific mortality follow-up through 2011. Model-based (simulated) results were compared to observed all-cause and CVD-specific mortality data (from NHANES) for the same starting population using survival curves and, in a method not typically used for disease model validation, receiver operating characteristic (ROC) curves. Results. Observed 10-year all-cause mortality in NHANES v. the simulation model was 11.2% (95% CI, 10.3% to 12.2%) v. 10.9%; corresponding results for CVD mortality were 2.2% (1.8% to 2.7%) v. 2.6%. Areas under the ROC curves for model-predicted 10-year all-cause and CVD mortality risks were 0.83 (0.81 to 0.85) and 0.84 (0.81 to 0.88), respectively; corresponding results for 5-year risks were 0.80 (0.77 to 0.83) and 0.81 (0.75 to 0.87), respectively. Limitations. The model is limited by the uncertainties in the data used to estimate its input parameters. Additionally, our validation analyses did not include non-fatal CVD outcomes due to NHANES data limitations. Conclusions. The simulation model performed well in matching to observed nationally representative longitudinal mortality data. ROC curve analysis, which has been traditionally used for risk prediction models, can also be used to assess discrimination for disease simulation models.

Suggested Citation

  • Ankur Pandya & Stephen Sy & Sylvia Cho & Sartaj Alam & Milton C. Weinstein & Thomas A. Gaziano, 2017. "Validation of a Cardiovascular Disease Policy Microsimulation Model Using Both Survival and Receiver Operating Characteristic Curves," Medical Decision Making, , vol. 37(7), pages 802-814, October.
    • Handle: RePEc:sae:medema:v:37:y:2017:i:7:p:802-814
    DOI: 10.1177/0272989X17706081
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    References listed on IDEAS

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    1. Natasha Stout & Sue Goldie, 2008. "Keeping the noise down: common random numbers for disease simulation modeling," Health Care Management Science, Springer, vol. 11(4), pages 399-406, December.
    2. Weinstein, M.C. & Coxson, P.G. & Williams, L.W. & Pass, T.M. & Stason, W.B. & Goldman, L., 1987. "Forecasting coronary heart disease incidence, mortality, and cost: The coronary heart disease policy model," American Journal of Public Health, American Public Health Association, vol. 77(11), pages 1417-1426.
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    Cited by:

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    2. Duncan Ermini Leaf & Bryan Tysinger & Dana P. Goldman & Darius N. Lakdawalla, 2021. "Predicting quantity and quality of life with the Future Elderly Model," Health Economics, John Wiley & Sons, Ltd., vol. 30(S1), pages 52-79, November.

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