Skip to main content
SpringerLink
Log in

Micro-simulation Modeling

  • Reference work entry
  • First Online:
Health Services Evaluation

Part of the book series: Health Services Research ((HEALTHSR))

  • 1809 Accesses

Abstract

Microsimulation models are a tool for informing health policy decisions. Models provide a structure for combining a wide range of evidence that represents the current understanding of both disease and interventions to prevent or treat disease. In the health policy context, microsimulation refers to simulation of an entire population by simulating life histories for individuals within the population. The basic structure of a microsimulation model includes a description of heath states that describe key events in a disease process. Individuals occupy these health states, and the model includes rules describing how individuals transition between states. Models are developed by specifying states and transition rules that result in predictions that reproduce observed or expected results. Model parameters are selected to achieve good prediction through a process of model calibration. Once calibrated, models are used to predict population-level outcomes under different policy scenarios. Model predictions are increasingly being used to provide information to guide health policy decisions. This increased use brings with it the need both for better understanding of microsimulation models by policy researchers and continued improvement in methods for developing and applying microsimulation models. This chapter reviews the process of developing and applying a microsimulation model, drawing from guidelines for best practices for simulation outlined by the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and The Society for Medical Decision Making (SDM) (Caro et al. 2012).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 649.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 899.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Atkin WS, Edwards R, Kralj-Hans I, et al. Once-only flexible sigmoidoscopy screening in prevention of colorectal cancer: a multicentre randomised controlled trial. Lancet. 2010;375(9726):1624–33.

    Article  Google Scholar 

  • Beck J, Pauker S. The Markov process in medical prognosis. Med Decis Mak. 1983;3:419–58.

    Article  CAS  Google Scholar 

  • Berry DA, Inoue L, Shen Y, et al. Modeling the impact of treatment and screening on U.S. breast cancer mortality: a Bayesian approach. J Natl Cancer Inst Monogr. 2006; 36:30–6.

    Google Scholar 

  • Brauer F, Castillo-Chavez C. Mathematical models for communicable diseases. Philadelphia: Society for Industrial and Applied Mathematics; 2013.

    Google Scholar 

  • Briggs AH, Weinstein MC, Fenwick EA, et al. Model parameter estimation and uncertainty analysis: a report of the ISPOR-SMDM modeling good research practices Task Force Working Group-6. Med Decis Mak. 2012;32(5):722–32.

    Article  Google Scholar 

  • Cancer Incidence – Surveillance, Epidemiology, and End Results (SEER) Registries Research Data [database on the Internet]. National Cancer Institute, Surveillance Systems Branch. 2012. Available from: http://seer.cancer.gov/data/seerstat/nov2011/.

  • Caro JJ, Briggs AH, Siebert U, et al. Modeling good research practices – overview: a report of the ISPOR-SMDM modeling good research practices Task Force-1. Med Decis Mak. 2012;32(5):667–77.

    Article  Google Scholar 

  • Church JM. Clinical significance of small colorectal polyps. Dis Colon Rectum. 2004;47(4):481–5.

    Article  Google Scholar 

  • CISNET. 2014. Available at: http://cisnet.cancer.gov. Accessed 30 Apr 2014.

  • Cronin KA, Legler JM, Etzioni RD. Assessing uncertainty in microsimulation modelling with application to cancer screening interventions. Stat Med. 1998;17(21):2509–23.

    Article  CAS  Google Scholar 

  • Doubilet P, Begg CB, Weinstein MC, et al. Probabilistic sensitivity analysis using Monte Carlo simulation. A practical approach. Med Decis Mak. 1985;5(2):157–77.

    Article  CAS  Google Scholar 

  • Eddy D. Breast cancer screening for Medicare beneficiaries: effectiveness, costs to Medicare and medical resources required. Washington, DC: U.S. Congress, Health Program, Office of Technology Assessment; 1987.

    Google Scholar 

  • Eddy DM, Hollingworth W, Caro JJ, et al. Model transparency and validation: a report of the ISPOR-SMDM modeling good research practices Task Force-7. Med Decis Mak. 2012;32(5):733–43.

    Article  Google Scholar 

  • Etzioni R, Penson DF, Legler JM, et al. Overdiagnosis due to prostate-specific antigen screening: lessons from U.S. prostate cancer incidence trends. J Natl Cancer Inst. 2002;94(13):981–90.

    Article  Google Scholar 

  • Hardcastle JD, Chamberlain JO, Robinson MH, et al. Randomised controlled trial of faecal-occult-blood screening for colorectal cancer. Lancet. 1996;348(9040):1472–7.

    Article  CAS  Google Scholar 

  • Hixson LJ, Fennerty MB, Sampliner RE, et al. Prospective study of the frequency and size distribution of polyps missed by colonoscopy. J Natl Cancer Inst. 1990;82(22):1769–72.

    Article  CAS  Google Scholar 

  • Hunt CA, Kennedy RC, Kim SH, et al. Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity. Wiley Interdiscip Rev Syst Biol Med. 2013;5(4):461–80.

    Article  Google Scholar 

  • Imperiale TF. Sigmoidoscopy screening: understanding the trade-off between detection of advanced neoplasia and diagnostic efficiency. J Natl Cancer Inst. 2013;105(12):846–8.

    Article  Google Scholar 

  • Imperiale TF, Wagner DR, Lin CY, et al. Risk of advanced proximal neoplasms in asymptomatic adults according to the distal colorectal findings. N Engl J Med. 2000;343(3):169–74.

    Article  CAS  Google Scholar 

  • Johnson CD, Chen MH, Toledano AY, et al. Accuracy of CT colonography for detection of large adenomas and cancers. N Engl J Med. 2008;359(12):1207–17.

    Article  CAS  Google Scholar 

  • Karnon J, Stahl J, Brennan A, et al. Modeling using discrete event simulation: a report of the ISPOR-SMDM modeling good research practices Task Force-4. Value Health. 2012;15(6):821–7.

    Article  Google Scholar 

  • Knudsen AB, Lansdorp-Vogelaar I, Rutter CM, et al. Cost-effectiveness of computed tomographic colonography screening for colorectal cancer in the Medicare population. J Natl Cancer Inst. 2010;102(16):1238–52.

    Article  Google Scholar 

  • Kronborg O, Fenger C, Olsen J, et al. Randomised study of screening for colorectal cancer with faecal-occult-blood test. Lancet. 1996;348(9040):1467–71.

    Article  CAS  Google Scholar 

  • Lansdorp-Vogelaar I, Kuntz KM, Knudsen AB, et al. Stool DNA testing to screen for colorectal cancer in the Medicare population. A cost-effectiveness analysis. Ann Intern Med. 2010;153(6):368–77.

    Article  Google Scholar 

  • Levin B, Lieberman DA, McFarland BG, et al. Screening and surveillance for the early detection of colorectal cancer and adenomatous polyps, 2008: a joint guideline from the American Cancer Society, the US Multi-society task force on Colorectal Cancer, and the American College of Radiology. Gastroenterology. 2008;134(5):1570–95.

    Article  CAS  Google Scholar 

  • Lieberman DA, Weiss DG, Bond JH, et al. Use of colonoscopy to screen asymptomatic adults for colorectal cancer. Veterans affairs cooperative study group 380. N Engl J Med. 2000;343(3):162–8.

    Article  CAS  Google Scholar 

  • Loeve F, Boer R, Zauber AG, et al. National Polyp Study data: evidence for regression of adenomas. Int J Cancer. 2004;111(4):633–9.

    Article  CAS  Google Scholar 

  • Luke DA, Stamatakis KA. Systems science methods in public health: dynamics, networks, and agents. Annu Rev Public Health. 2012;33:357–76.

    Article  Google Scholar 

  • Mandelblatt J, Schechter C, Levy D, et al. Building better models: if we build them, will policy makers use them? Toward integrating modeling into health care decisions. Med Decis Mak. 2012;32(5):656–9.

    Article  Google Scholar 

  • Muller CM, Mandelblatt J, Schechter C. The cost and effectiveness of cervical cancer screening in elderly women. Washington, DC: Congress of the United States, Office of Technology Assessment; 1990.

    Google Scholar 

  • National Cancer Institute. Cancer Intervention and Surveillance Modeling Network (CISNET). n.d.. Available at: http://cisnet.cancer.gov/. Accessed 2008.

  • National Center for Health Statistics. US Life Tables. 2000.; Available at: www.cdc.gov/nchs/products/pubs/pubd/lftbls/life/1966.htm. Accessed 2013.

  • Odom SR, Duffy SD, Barone JE, et al. The rate of adenocarcinoma in endoscopically removed colorectal polyps. Am Surg. 2005;71(12):1024–6.

    PubMed  Google Scholar 

  • Parmigiani G. Measuring uncertainty in complex decision analysis models. Stat Methods Med Res. 2002;11(6):513–37.

    Article  CAS  Google Scholar 

  • Petitti DB. Meta-analysis, decision analysis, and cost-effectiveness analysis: methods for quantitative synthesis in medicine. 2nd ed. New York: Oxford University Press; 2000. 306 p.

    Google Scholar 

  • Pickhardt PJ, Choi JR, Hwang I, et al. Computed tomographic virtual colonoscopy to screen for colorectal neoplasia in asymptomatic adults. N Engl J Med. 2003;349(23):2191–200.

    Article  CAS  Google Scholar 

  • Pitman R, Fisman D, Zaric GS, et al. Dynamic transmission modeling: a report of the ISPOR-SMDM modeling good research practices Task Force Working Group-5. Med Decis Mak. 2012;32(5):712–21.

    Article  Google Scholar 

  • Rex DK, Cutler CS, Lemmel GT, et al. Colonoscopic miss rates of adenomas determined by back-to-back colonoscopies. Gastroenterology. 1997;112(1):24–8.

    Article  CAS  Google Scholar 

  • Rex DK, Johnson DA, Anderson JC, et al. American College of Gastroenterology guidelines for colorectal cancer screening 2009 [corrected]. Am J Gastroenterol. 2009;104(3):739–50.

    Article  Google Scholar 

  • Roberts M, Russell LB, Paltiel AD, et al. Conceptualizing a model: a report of the ISPOR-SMDM modeling good research practices Task Force-2. Med Decis Mak. 2012;32(5):678–89.

    Article  Google Scholar 

  • Rutter CM, Savarino JE. An evidence-based microsimulation model for colorectal cancer. Cancer Epidemiol Biomark Prev. 2010;19(8):1992–2002.

    Article  Google Scholar 

  • Rutter CM, Yu O, Miglioretti DL. A hierarchical non-homogenous Poisson model for meta-analysis of adenoma counts. Stat Med. 2007;26(1):98–109.

    Article  Google Scholar 

  • Rutter CM, Miglioretti DL, Savarino JE. Bayesian calibration of microsimulation models. J Am Stat Assoc. 2009;104(488):1338–50.

    Article  Google Scholar 

  • Rutter CM, Zaslavsky AM, Feuer EJ. Dynamic microsimulation models for health outcomes: a review. Med Decis Mak. 2010;31(1):10–8.

    Article  Google Scholar 

  • Siebert U, Alagoz O, Bayoumi AM, et al. State-transition modeling: a report of the ISPOR-SMDM modeling good research practices Task Force-3. Med Decis Mak. 2012;32(5):690–700.

    Article  Google Scholar 

  • Strul H, Kariv R, Leshno M, et al. The prevalence rate and anatomic location of colorectal adenoma and cancer detected by colonoscopy in average-risk individuals aged 40–80 years. Am J Gastroenterol. 2006;101(2):255–62.

    Article  Google Scholar 

  • The Mount Hood 4 Modeling Group. Computer modeling of diabetes and it's complication: a report on the 4th Mount Hood challenge meeting. Diabetes Care. 2007;30:1638–46.

    Article  Google Scholar 

  • Towler B, Irwig L, Glasziou P, et al. A systematic review of the effects of screening for colorectal cancer using the faecal occult blood test, hemoccult. BMJ. 1998;317(7158):559–65.

    Article  CAS  Google Scholar 

  • U. S. Preventive Services Task Force. Screening for colorectal cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2008;149(9):627–37.

    Article  Google Scholar 

  • van der Akker-van Marle ME, van Ballegooijen M, van Ootmarssen GJ, et al. Cost-effectivness of cervical cancer screening: comparison of screening policies. J Natl Cancer Inst. 2002;94:193–204.

    Article  Google Scholar 

  • Vanni T, Karnon J, Madan J, et al. Calibrating models in economic evaluation: a seven-step approach. PharmacoEconomics. 2011;29(1):35–49.

    Article  Google Scholar 

  • Vogelaar I, Van Ballegooijen M, Schrag D, et al. How much can current interventions reduce colorectal cancer mortality in the U.S.? Cancer. 2006;107:1623–33.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. RAND Corporation, Santa Monica, CA, USA

    Carolyn M. Rutter

Authors
  1. Carolyn M. Rutter
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Carolyn M. Rutter .

Editor information

Editors and Affiliations

  1. Community Health and Epidemiology, Dalhousie University, Halifax, NS, Canada

    Adrian Levy

  2. ICON plc, Vancouver, BC, Canada

    Sarah Goring

  3. Department of Biostatistics, Brown University, Providence, RI, USA

    Constantine Gatsonis

  4. University of British Columbia, Vancouver, BC, Canada

    Boris Sobolev

  5. European Observatory on Health Systems and Policies, Department of Health Care Management, Berlin University of Technology, Berlin, Germany

    Ewout van Ginneken

  6. Department Health Care Management Faculty of Economics and Management, Technische Universität Berlin, Berlin, Germany

    Reinhard Busse

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Science+Business Media, LLC, part of Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Rutter, C.M. (2019). Micro-simulation Modeling. In: Levy, A., Goring, S., Gatsonis, C., Sobolev, B., van Ginneken, E., Busse, R. (eds) Health Services Evaluation. Health Services Research. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-8715-3_35

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-8715-3_35

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4939-8714-6

  • Online ISBN: 978-1-4939-8715-3

  • eBook Packages: MedicineReference Module Medicine

Publish with us

Policies and ethics

Search

Navigation