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

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  • 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
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    File URL: http://www.microsimulation.org/IJM/V10_3/IJM_2017_10_3_1.pdf
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    References listed on IDEAS

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    1. 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.
    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. 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.
    5. 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.
    6. 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.
    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. Michael Wolfson, 2011. "Linking Policies to Well-Being Outcomes Through Micro-Simulation," OECD Statistics Working Papers 2011/8, OECD Publishing.
    9. Frank A. Sonnenberg & J. Robert Beck, 1993. "Markov Models in Medical Decision Making," Medical Decision Making, , vol. 13(4), pages 322-338, December.
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    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

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