Comparing CISNET Breast Cancer Incidence and Mortality Predictions to Observed Clinical Trial Results of Mammography Screening from Ages 40 to 49

Jeroen J. van den Broek, Nicolien T. van Ravesteyn, Jeanne S. Mandelblatt, Hui Huang, Mehmet Ali Ergun, Elizabeth S. Burnside, Cong Xu, Yisheng Li, Oguzhan Alagoz, Sandra J. Lee, Natasha K. Stout, Juhee Song, Amy Trentham-Dietz, Sylvia K. Plevritis, Sue M. Moss and Harry J. de Koning
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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
Hui Huang: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard Medical School Boston, Boston, MA, USA
Mehmet Ali Ergun: Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA
Elizabeth S. Burnside: Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
Cong Xu: Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA
Yisheng Li: Department of Biostatistics, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
Oguzhan Alagoz: Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA
Sandra J. Lee: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard Medical School Boston, Boston, MA, USA
Natasha K. Stout: Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
Juhee Song: Department of Biostatistics, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
Amy Trentham-Dietz: Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA
Sylvia K. Plevritis: Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA
Sue M. Moss: Department of cancer prevention, Wolfson Institute, Queen Mary University of London, London, UK
Harry J. de Koning: Department of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands

Medical Decision Making, 2018, vol. 38, issue 1_suppl, 140S-150S

Abstract: Background. The UK Age trial compared annual mammography screening of women ages 40 to 49 years with no screening and found a statistically significant breast cancer mortality reduction at the 10-year follow-up but not at the 17-year follow-up. The objective of this study was to compare the observed Age trial results with the Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer model predicted results. Methods. Five established CISNET breast cancer models used data on population demographics, screening attendance, and mammography performance from the Age trial together with extant natural history parameters to project breast cancer incidence and mortality in the control and intervention arm of the trial. Results. The models closely reproduced the effect of annual screening from ages 40 to 49 years on breast cancer incidence. Restricted to breast cancer deaths originating from cancers diagnosed during the intervention phase, the models estimated an average 15% (range across models, 13% to 17%) breast cancer mortality reduction at the 10-year follow-up compared with 25% (95% CI, 3% to 42%) observed in the trial. At the 17-year follow-up, the models predicted 13% (range, 10% to 17%) reduction in breast cancer mortality compared with the non-significant 12% (95% CI, -4% to 26%) in the trial. Conclusions. The models underestimated the effect of screening on breast cancer mortality at the 10-year follow-up. Overall, the models captured the observed long-term effect of screening from age 40 to 49 years on breast cancer incidence and mortality in the UK Age trial, suggesting that the model structures, input parameters, and assumptions about breast cancer natural history are reasonable for estimating the impact of screening on mortality in this age group.

Keywords: breast cancer models; CISNET; external validation; mammography trial simulation (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:140s-150s

DOI: 10.1177/0272989X17718168

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