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Dynamic Microsimulation Models for Health Outcomes

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  • Carolyn M. Rutter
  • Alan M. Zaslavsky
  • Eric J. Feuer

Abstract

Background . Microsimulation models (MSMs) for health outcomes simulate individual event histories associated with key components of a disease process; these simulated life histories can be aggregated to estimate population-level effects of treatment on disease outcomes and the comparative effectiveness of treatments. Although MSMs are used to address a wide range of research questions, methodological improvements in MSM approaches have been slowed by the lack of communication among modelers. In addition, there are few resources to guide individuals who may wish to use MSM projections to inform decisions. Methods . This article presents an overview of microsimulation modeling, focusing on the development and application of MSMs for health policy questions. The authors discuss MSM goals, overall components of MSMs, methods for selecting MSM parameters to reproduce observed or expected results (calibration), methods for MSM checking (validation), and issues related to reporting and interpreting MSM findings(sensitivity analyses, reporting of variability, and model transparency). Conclusions . MSMs are increasingly being used to provide information to guide health policy decisions. This increased use brings with it the need for both better understanding of MSMs by policy researchers, and continued improvement in methods for developing and applying MSMs.

Suggested Citation

  • Carolyn M. Rutter & Alan M. Zaslavsky & Eric J. Feuer, 2011. "Dynamic Microsimulation Models for Health Outcomes," Medical Decision Making, , vol. 31(1), pages 10-18, January.
    • Handle: RePEc:sae:medema:v:31:y:2011:i:1:p:10-18
    DOI: 10.1177/0272989X10369005
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    References listed on IDEAS

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    Cited by:

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    3. Deborah J Schofield & Melanie J B Zeppel & Owen Tan & Sharyn Lymer & Michelle M Cunich & Rupendra N Shrestha, 2018. "A Brief, Global History of Microsimulation Models in Health: Past Applications, Lessons Learned and Future Directions," International Journal of Microsimulation, International Microsimulation Association, vol. 11(1), pages 97-142.
    4. Cathal O'Donoghue & Gijs Dekkers, 2018. "Increasing the Impact of Dynamic Microsimulation Modelling," International Journal of Microsimulation, International Microsimulation Association, vol. 11(1), pages 61-96.
    5. Alejandra Macías Sánchez & Héctor Juan Villarreal Páez, 2018. "Sostenibilidad del gasto público: Cobertura y financiamiento de enfermedades crónicas en México. (Public Spending Sustainability: Coverage and Financing of Chronic Diseases in Mexico)," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(1), pages 99-134, May.
    6. Piccarreta, Raffaella & Bonetti, Marco, 2019. "Assessing and comparing models for sequence data by microsimulation (with Supplementary Material)," SocArXiv 3mcfp, Center for Open Science.
    7. Matteo Richiardi, 2018. "Editorial," International Journal of Microsimulation, International Microsimulation Association, vol. 11(1), pages 1-3.
    8. Lay-Yee, Roy & Milne, Barry & Davis, Peter & Pearson, Janet & McLay, Jessica, 2015. "Determinants and disparities: A simulation approach to the case of child health care," Social Science & Medicine, Elsevier, vol. 128(C), pages 202-211.
    9. Jan Pablo Burgard & Simon Schmaus, 2019. "Sensitivity Analysis for Dynamic Microsimulation Models," Research Papers in Economics 2019-15, University of Trier, Department of Economics.
    10. Stavroula A. Chrysanthopoulou & Carolyn M. Rutter & Constantine A. Gatsonis, 2021. "Bayesian versus Empirical Calibration of Microsimulation Models: A Comparative Analysis," Medical Decision Making, , vol. 41(6), pages 714-726, August.
    11. Stavroula A Chrysanthopoulou, 2017. "MILC: A Microsimulation Model of the Natural History of Lung Cancer," International Journal of Microsimulation, International Microsimulation Association, vol. 10(3), pages 5-26.
    12. Abbygail Jaccard & Lise Retat & Martin Brown & Laura Webber & Zaid Chalabi, 2018. "Global Sensitivity Analysis of a Model Simulating an Individual’s Health State through Their Lifetime," International Journal of Microsimulation, International Microsimulation Association, vol. 11(3), pages 100-121.
    13. Alison Ritter & Nagesh Shukla & Marian Shanahan & Phuong Van Hoang & Vu Lam Cao & Pascal Perez & Michael Farrell, 2016. "Building a Microsimulation Model of Heroin Use Careers in Australia," International Journal of Microsimulation, International Microsimulation Association, vol. 9(3), pages 140-176.
    14. William, Jananie & Loong, Bronwyn & Hanna, Dana & Parkinson, Bonny & Loxton, Deborah, 2022. "Lifetime health costs of intimate partner violence: A prospective longitudinal cohort study with linked data for out-of-hospital and pharmaceutical costs," Economic Modelling, Elsevier, vol. 116(C).
    15. Wizdom Powell & Leah Frerichs & Rachel Townsley & Maria Mayorga & Jennifer Richmond & Giselle Corbie-Smith & Stephanie Wheeler & Kristen Hassmiller Lich, 2020. "The potential impact of the Affordable Care Act and Medicaid expansion on reducing colorectal cancer screening disparities in African American males," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-13, January.
    16. Mehdi Javanbakht & Atefeh Mashayekhi & Hamid R Baradaran & AliAkbar Haghdoost & Ashkan Afshin, 2015. "Projection of Diabetes Population Size and Associated Economic Burden through 2030 in Iran: Evidence from Micro-Simulation Markov Model and Bayesian Meta-Analysis," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-17, July.
    17. Zaid Chalabi & Sari Kovats, 2014. "Tools for developing adaptation policy to protect human health," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 19(3), pages 309-330, March.
    18. Maximilian Tremmel & Ulf-G. Gerdtham & Peter M. Nilsson & Sanjib Saha, 2017. "Economic Burden of Obesity: A Systematic Literature Review," IJERPH, MDPI, vol. 14(4), pages 1-18, April.

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