The Impact of Model Assumptions on Personalized Lung Cancer Screening Recommendations

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. Plevritis, Rafael Meza and Harry J. de Koning
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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
Harry J. de Koning: Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands

Medical Decision Making, 2024, vol. 44, issue 5, 497-511

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

Keywords: lung cancer; personalized screening; natural-history modelling; maximum clinical incidence reduction (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:44:y:2024:i:5:p:497-511

DOI: 10.1177/0272989X241249182

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