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The Impact of Model Assumptions on Personalized Lung Cancer Screening Recommendations

Author

Listed:
  • Kevin ten Haaf

    (Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands)

  • Koen de Nijs

    (Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands)

  • Giulia Simoni

    (Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA)

  • Andres Alban

    (MGH Institute for Technology Assessment, Harvard Medical School, Boston, MA, USA)

  • Pianpian Cao

    (Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA)

  • Zhuolu Sun

    (Canadian Partnership Against Cancer, Toronto, ON, Canada)

  • Jean Yong

    (Canadian Partnership Against Cancer, Toronto, ON, Canada)

  • Jihyoun Jeon

    (Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA)

  • Iakovos Toumazis

    (Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, TX, USA)

  • Summer S. Han

    (Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA, USA)

  • G. Scott Gazelle

    (Department of Radiology, Massachusetts General Hospital, Boston, MA, USA)

  • Chung Ying Kong

    (Division of General Internal Medicine, Department of Medicine, Mount Sinai Hospital, New York, NY, USA)

  • Sylvia K. Plevritis

    (Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA)

  • Rafael Meza

    (Department of Integrative Oncology, BC Cancer Research Institute, BC, Canada
    School of Population and Public Health, University of British Columbia, BC, Canada)

  • Harry J. de Koning

    (Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands)

Abstract

Background Recommendations regarding personalized lung cancer screening are being informed by natural-history modeling. Therefore, understanding how differences in model assumptions affect model-based personalized screening recommendations is essential. Design Five Cancer Intervention and Surveillance Modeling Network (CISNET) models were evaluated. Lung cancer incidence, mortality, and stage distributions were compared across 4 theoretical scenarios to assess model assumptions regarding 1) sojourn times, 2) stage-specific sensitivities, and 3) screening-induced lung cancer mortality reductions. Analyses were stratified by sex and smoking behavior. Results Most cancers had sojourn times

Suggested Citation

  • Kevin ten Haaf & Koen de Nijs & Giulia Simoni & Andres Alban & Pianpian Cao & Zhuolu Sun & Jean Yong & Jihyoun Jeon & Iakovos Toumazis & Summer S. Han & G. Scott Gazelle & Chung Ying Kong & Sylvia K. , 2024. "The Impact of Model Assumptions on Personalized Lung Cancer Screening Recommendations," Medical Decision Making, , vol. 44(5), pages 497-511, July.
    • Handle: RePEc:sae:medema:v:44:y:2024:i:5:p:497-511
    DOI: 10.1177/0272989X241249182
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