title: Cost-Effectiveness Models in Breast Cancer Screening in the General Population: A Systematic Review.
abstract: BACKGROUND
  Many Western countries have long-established population-based mammography screening programs. Prior to implementing these programs, decision-analytic modeling was widely used to inform decisions.
  
  
  OBJECTIVE
  The aim of this study was to perform a systematic review of cost-effectiveness models in breast cancer screening in the general population to analyze their structural and methodological approaches.
  
  
  METHODS
  A systematic literature search for health economic models was performed in the electronic databases MEDLINE (Ovid), EMBASE, CRD Databases, Cochrane Library, and EconLit in August 2011 with updates in June 2013, April 2015, and November 2016. To assess studies systematically, a standardized form was applied to extract relevant information that was then summarized in evidence tables.
  
  
  RESULTS
  Thirty-five studies were included; 27 state-transition models were analyzed using cohort (n = 12) and individual-level simulation (n = 15). Twenty-one studies modeled the natural history of breast cancer and predicted mortality as a function of the early detection modality. The models employed different assumptions regarding ductal carcinoma in situ. Thirteen studies performed cost-utility analyses with different sources for utility values, but assumptions were often made about utility weights. Twenty-two models did not report any validation.
  
  
  CONCLUSIONS
  State-transition modeling was the most frequently applied analytic approach. Different methods in modeling the progression of ductal carcinoma in situ to invasive cancer were identified because there is currently no agreement on the biological behavior of noninvasive breast cancer. Main weaknesses were the lack of precise utility estimates and insufficient reporting of validation. Sensitivity analyses of assumptions regarding ductal carcinoma in situ and in particular adequate validation are critical to minimize the risk of biased model outcomes.
authors.affiliation: Department of Evidence-Based Economic Health Care, Main Association of Austrian Social Security Institutions, Kundmanngasse 21, 1030, Vienna, Austria. irmgard.schiller-fruehwirth@sozialversicherung.at.|Department of Public Health, Health Services Research and Health Technology Assessment, University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Austria. irmgard.schiller-fruehwirth@sozialversicherung.at.
authors.forename: Irmgard C
authors.initials: IC
authors.lastname: Schiller-Frühwirth
authors.affiliation: Department of Public Health, Health Services Research and Health Technology Assessment, University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Austria.|Division of Health Technology Assessment and Bioinformatics, ONCOTYROL-Center for Personalized Cancer Medicine, Innsbruck, Austria.
authors.forename: Beate
authors.initials: B
authors.lastname: Jahn
authors.affiliation: Department of Public Health, Health Services Research and Health Technology Assessment, University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Austria.
authors.forename: Marjan
authors.initials: M
authors.lastname: Arvandi
authors.affiliation: Department of Public Health, Health Services Research and Health Technology Assessment, University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Austria.|Division of Health Technology Assessment and Bioinformatics, ONCOTYROL-Center for Personalized Cancer Medicine, Innsbruck, Austria.|Department of Radiology, Institute for Technology Assessment, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.|Department of Health Policy and Management, Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
authors.forename: Uwe
authors.initials: U
authors.lastname: Siebert
mesh_terms: D000328:Adult; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000191:economics*; D003362:Cost-Benefit Analysis / Q000706:statistics & numerical data*; D055088:Early Detection of Cancer / Q000191:economics*; D005260:Female; D006801:Humans; D008875:Middle Aged; D018803:Models, Economic