IDEAS home Printed from https://ideas.repec.org/a/ijm/journl/v10y2017i3p5-26.html
   My bibliography  Save this article

MILC: A Microsimulation Model of the Natural History of Lung Cancer

Author

Listed:
  • Stavroula A Chrysanthopoulou

    (Brown University School of Public Health, Providence, RI, USA)

Abstract

The Microsimulation Lung Cancer (MILC) model was developed to simulate individual trajectories and predict outcomes of lung cancer for populations. The model describes the natural history of lung cancer from a disease-free state to death. Predictions of individual trajectories depend on a set of covariates including age, sex, and smoking behaviors. The module presented here is designed as part of a comprehensive decision-making toolkit for evaluating lung cancer prevention, screening and treatment policies. The MILC package implements the model in the open-source statistical software R. This paper introduces the main components, simulation algorithm, and specifics of the MILC model, validates it by reproducing observed lung cancer incidence trends in the US population, and uses it to make plausible predictions for 50-year-old men and women with a range of smoking histories.

Suggested Citation

  • 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.
  • Handle: RePEc:ijm:journl:v10:y:2017:i:3:p:5-26
    as

    Download full text from publisher

    File URL: http://www.microsimulation.org/IJM/V10_3/IJM_2017_10_3_1.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Michael Wolfson, 2011. "Linking Policies to Well-Being Outcomes Through Micro-Simulation," OECD Statistics Working Papers 2011/8, OECD Publishing.
    2. Eugenio Zucchelli & Andrew M Jones & Nigel Rice, 2012. "The evaluation of health policies through dynamic microsimulation methods," International Journal of Microsimulation, International Microsimulation Association, vol. 5(1), pages 2-20.
    3. 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.
    4. Jinjing Li & Cathal O'Donoghue, 2013. "A survey of dynamic microsimulation models: uses, model structure and methodology," International Journal of Microsimulation, International Microsimulation Association, vol. 6(2), pages 3-55.
    5. Rutter, Carolyn M. & Miglioretti, Diana L. & Savarino, James E., 2009. "Bayesian Calibration of Microsimulation Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1338-1350.
    6. Frank A. Sonnenberg & J. Robert Beck, 1993. "Markov Models in Medical Decision Making," Medical Decision Making, , vol. 13(4), pages 322-338, December.
    7. Levy, D.T. & Bauer, J.E. & Lee, H.-R., 2006. "Simulation modeling and tobacco control: Creating more robust public health policies," American Journal of Public Health, American Public Health Association, vol. 96(3), pages 494-498.
    8. Mathilda L. Bongers & Dirk de Ruysscher & Cary Oberije & Philippe Lambin & Carin A. Uyl–de Groot & V. M. H. Coupé, 2016. "Multistate Statistical Modeling," Medical Decision Making, , vol. 36(1), pages 86-100, January.
    9. David T. Levy & Karen Friend, 2002. "A Simulation Model of Policies Directed at Treating Tobacco Use and Dependence," Medical Decision Making, , vol. 22(1), pages 6-17, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. GENEVOIS Anne-Sophie & LIEGEOIS Philippe & PI ALPERIN Maria Noel, 2019. "DyMH_LU: a simple tool for modelling and simulating the health status of the Luxembourgish elderly in the longer run," LISER Working Paper Series 2019-06, Luxembourg Institute of Socio-Economic Research (LISER).
    3. 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.
    4. Matteo Richiardi, 2018. "Editorial," International Journal of Microsimulation, International Microsimulation Association, vol. 11(1), pages 1-3.
    5. Levy, David T. & Hyland, Andrew & Higbee, Cheryl & Remer, Lillian & Compton, Christine, 2007. "The role of public policies in reducing smoking prevalence in California: Results from the California Tobacco Policy Simulation Model," Health Policy, Elsevier, vol. 82(2), pages 167-185, July.
    6. Johannes Geyer & Salmai Qari & Hermann Buslei & Peter Haan, 2021. "DySiMo Dokumentation: Version 1.0," Data Documentation 101, DIW Berlin, German Institute for Economic Research.
    7. 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.
    8. Holger Bonin & Karsten Reuss & Holger Stichnoth, 2015. "Life-Cycle Incidence of Family Policy Measures in Germany: Evidence from a Dynamic Microsimulation Model," SOEPpapers on Multidisciplinary Panel Data Research 770, DIW Berlin, The German Socio-Economic Panel (SOEP).
    9. Sean A. Simpson & James M. Nonnemaker, 2013. "New York Tobacco Control Program Cessation Assistance: Costs, Benefits, and Effectiveness," IJERPH, MDPI, vol. 10(3), pages 1-11, March.
    10. Malek B Hannouf & Chander Sehgal & Jeffrey Q Cao & Joseph D Mocanu & Eric Winquist & Gregory S Zaric, 2012. "Cost-Effectiveness of Adding Cetuximab to Platinum-Based Chemotherapy for First-Line Treatment of Recurrent or Metastatic Head and Neck Cancer," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-9, June.
    11. Matteo Richiardi & Ross E. Richardson, 2017. "JAS-mine: A new platform for microsimulation and agent-based modelling," International Journal of Microsimulation, International Microsimulation Association, vol. 10(1), pages 106-134.
    12. Bärnighausen, Till & Bloom, David E., 2009. ""Conditional scholarships" for HIV/AIDS health workers: Educating and retaining the workforce to provide antiretroviral treatment in sub-Saharan Africa," Social Science & Medicine, Elsevier, vol. 68(3), pages 544-551, February.
    13. Devolder, Daniel & Spijker, Jeroen & Zueras, Pilar, 2021. "DEMOCARE: A mixed kinship microsimulation and Agent-Based models for studying family supply of time for care of elderly people with disabilities," SocArXiv 3e7sg_v1, Center for Open Science.
    14. 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.
    15. van de Ven, Justin, 2017. "SIDD: An adaptable framework for analysing the distributional implications of policy alternatives where savings and employment decisions matter," Economic Modelling, Elsevier, vol. 63(C), pages 161-174.
    16. de Wit, G.Ardine & Ramsteijn, Paul G & de Charro, Frank Th, 1998. "Economic evaluation of end stage renal disease treatment," Health Policy, Elsevier, vol. 44(3), pages 215-232, June.
    17. Afschin Gandjour & Eva-Julia Weyler, 2006. "Cost-effectiveness of referrals to high-volume hospitals: An analysis based on a probabilistic Markov model for hip fracture surgeries," Health Care Management Science, Springer, vol. 9(4), pages 359-369, November.
    18. Vincenzo Atella & Federico Belotti & Ludovico Carrino & Andrea Piano Mortari, 2017. "The future of Long Term Care in Europe. An investigation using a dynamic microsimulation model," CEIS Research Paper 405, Tor Vergata University, CEIS, revised 08 May 2017.
    19. Malek Ebadi & Raha Akhavan-Tabatabaei, 2021. "Personalized Cotesting Policies for Cervical Cancer Screening: A POMDP Approach," Mathematics, MDPI, vol. 9(6), pages 1-20, March.
    20. C. GEAY & M. KOUBI & G. de LAGASNERIE, 2015. "Evolution of outpatient healthcare expenditure, a dynamic micro-simulation using the Destinie model," Documents de Travail de l'Insee - INSEE Working Papers g2015-15, Institut National de la Statistique et des Etudes Economiques.

    More about this item

    Keywords

    MICROSIMULATION; COMPARATIVE EFFECTIVENESS RESEARCH; NATURAL HISTORY MODEL; LUNG CANCER; SOFTWARE;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ijm:journl:v10:y:2017:i:3:p:5-26. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Jinjing Li (email available below). General contact details of provider: http://www.microsimulation.pub .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.