Comparing CISNET Breast Cancer Models Using the Maximum Clinical Incidence Reduction Methodology

Jeroen J. van den Broek, Nicolien T. van Ravesteyn, Jeanne S. Mandelblatt, Mucahit Cevik, Clyde B. Schechter, Sandra J. Lee, Hui Huang, Yisheng Li, Diego F. Munoz, Sylvia K. Plevritis, Harry J. de Koning, Natasha K. Stout and Marjolein van Ballegooijen
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Jeroen J. van den Broek: Department of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands
Nicolien T. van Ravesteyn: Department of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands
Jeanne S. Mandelblatt: Department of Oncology, Georgetown-Lombardi Comprehensive Cancer Center, Georgetown University School of Medicine, Washington, DC, USA
Mucahit Cevik: Department of Industrial and Systems Engineering, University of Wisconsin-Madison, WI, USA
Clyde B. Schechter: Departments of Family and Social Medicine and Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
Sandra J. Lee: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard Medical School Boston, Boston, MA, USA
Hui Huang: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard Medical School Boston, Boston, MA, USA
Yisheng Li: Department of Biostatistics, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
Diego F. Munoz: Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA
Sylvia K. Plevritis: Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA
Harry J. de Koning: Department of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands
Natasha K. Stout: Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
Marjolein van Ballegooijen: Department of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands

Medical Decision Making, 2018, vol. 38, issue 1_suppl, 112S-125S

Abstract: Background. Collaborative modeling has been used to estimate the impact of potential cancer screening strategies worldwide. A necessary step in the interpretation of collaborative cancer screening model results is to understand how model structure and model assumptions influence cancer incidence and mortality predictions. In this study, we examined the relative contributions of the pre-clinical duration of breast cancer, the sensitivity of screening, and the improvement in prognosis associated with treatment of screen-detected cases to the breast cancer incidence and mortality predictions of 5 Cancer Intervention and Surveillance Modeling Network (CISNET) models. Methods. To tease out the impact of model structure and assumptions on model predictions, the Maximum Clinical Incidence Reduction (MCLIR) method compares changes in the number of breast cancers diagnosed due to clinical symptoms and cancer mortality between 4 simplified scenarios: 1) no-screening; 2) one-time perfect screening exam, which detects all existing cancers and perfect treatment (i.e., cure) of all screen-detected cancers; 3) one-time digital mammogram and perfect treatment of all screen-detected cancers; and 4) one-time digital mammogram and current guideline-concordant treatment of all screen-detected cancers. Results. The 5 models predicted a large range in maximum clinical incidence (19% to 71%) and in breast cancer mortality reduction (33% to 67%) from a one-time perfect screening test and perfect treatment. In this perfect scenario, the models with assumptions of tumor inception before it is first detectable by mammography predicted substantially higher incidence and mortality reductions than models with assumptions of tumor onset at the start of a cancer’s screen-detectable phase. The range across models in breast cancer clinical incidence (11% to 24%) and mortality reduction (8% to 18%) from a one-time digital mammogram at age 62 y with observed sensitivity and current guideline-concordant treatment was considerably smaller than achievable under perfect conditions. Conclusions. The timing of tumor inception and its effect on the length of the pre-clinical phase of breast cancer had a substantial impact on the grouping of models based on their predictions for clinical incidence and breast cancer mortality reduction. This key finding about the timing of tumor inception will be included in future CISNET breast analyses to enhance model transparency. The MCLIR approach should aid in the interpretation of variations in model results and could be adopted in other disease screening settings to enhance model transparency.

Keywords: breast cancer natural history assumptions; maximum clinical incidence reduction; screening effectiveness (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:38:y:2018:i:1_suppl:p:112s-125s

DOI: 10.1177/0272989X17743244

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