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Estimation of Mortality Rates for Disease Simulation Models Using Bayesian Evidence Synthesis

Author

Listed:
  • Pamela M. McMahon

    (Institute for Technology Assessment, Massachusetts General Hospital, Boston, pamela@mgh-ita.org)

  • Alan M. Zaslavsky

    (Department of Health Care Policy, Harvard Medical School, Boston)

  • Milton C. Weinstein

    (Department of Health Policy and Management, Harvard School of Public Health, Boston)

  • Karen M. Kuntz

    (Department of Health Policy and Management, Harvard School of Public Health, Boston)

  • Jane C. Weeks

    (Department of Medical Oncology, Dana-Farber Cancer Institute, Boston)

  • G. Scott Gazelle

    (Institute for Technology Assessment, Massachusetts General Hospital, Boston, Department of Health Policy and Management, Harvard School of Public Health, Boston)

Abstract

Purpose . The authors propose a Bayesian approach for estimating competing risks for inputs to disease simulation models. This approach is suggested when modeling a disease that causes a large proportion of all-cause mortality, particularly when mortality from the disease of interest and other-cause mortality are both affected by the same risk factor. Methods . The authors demonstrate a Bayesian evidence synthesis by estimating other-cause mortality, stratified by smoking status, for use in a simulation model of lung cancer. National (US) survey data linked to death registries (National Health Interview Survey [NHIS]—Multiple Cause of Death files) were used to fit cause-specific hazard models for 3 causes of death (lung cancer, heart disease, and all other causes), controlling for age, sex, race, and smoking status. Synthesis of NHIS data with national vital statistics data on numbers and causes of deaths was performed in WinBUGS (version 1.4.1, MRC Biostatistics Unit, UK). Correction for inconsistencies between the NHIS and vital statistics data is described. A published cohort study was a source of prior information for smoking-related mortality. Results . Marginal posterior densities of annual mortality rates for lung cancer and other-cause death (further divided into heart disease and all other causes), stratified by 5-year age interval, race (white and black), gender, and smoking status (current, former, never), were estimated, specific to a time period (1987-1995). Overall, black current smokers experienced the highest mortality rates. Conclusions . Bayesian evidence synthesis is an effective method for estimation of cause-specific mortality rates, stratified by demographic factors.

Suggested Citation

  • Pamela M. McMahon & Alan M. Zaslavsky & Milton C. Weinstein & Karen M. Kuntz & Jane C. Weeks & G. Scott Gazelle, 2006. "Estimation of Mortality Rates for Disease Simulation Models Using Bayesian Evidence Synthesis," Medical Decision Making, , vol. 26(5), pages 497-511, September.
    • Handle: RePEc:sae:medema:v:26:y:2006:i:5:p:497-511
    DOI: 10.1177/0272989X06291326
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    References listed on IDEAS

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    1. A. E. Ades & S. Cliffe, 2002. "Markov Chain Monte Carlo Estimation of a Multiparameter Decision Model: Consistency of Evidence and the Accurate Assessment of Uncertainty," Medical Decision Making, , vol. 22(4), pages 359-371, August.
    2. Russell, L.B. & Carson, J.L. & Taylor, W.C. & Milan, E. & Dey, A. & Jagannathan, R., 1998. "Modeling all-cause mortality: Projections of the impact of smoking cessation based on the NHEFS," American Journal of Public Health, American Public Health Association, vol. 88(4), pages 630-636.
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    Cited by:

    1. Marjorie A. Rosenberg & Eric J. Feuer & Binbing Yu & Jiafeng Sun & S. Jane Henley & Thomas G Shanks & Christy M. Anderson & Pamela M. McMahon & Michael J. Thun & David M. Burns, 2012. "Chapter 3: Cohort Life Tables by Smoking Status, Removing Lung Cancer as a Cause of Death," Risk Analysis, John Wiley & Sons, vol. 32(s1), pages 25-38, August.

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