title: The SPECTRUM population model of the impact of screening and treatment on U.S. breast cancer trends from 1975 to 2000: principles and practice of the model methods.
abstract: OBJECTIVE
  This stochastic simulation model was developed to estimate the impact of screening and treatment diffusion on U.S. breast cancer mortality between 1975 and 2000.
  We use an event-driven continuous-time state transition model. Women who are destined to develop breast cancer may be screen detected, present with symptoms, or die of other causes before cancer is diagnosed. At presentation, the cancer has a stage assigned on the basis of mode of detection. Cancers are assumed to be estrogen receptor (ER) positive or negative. Data on screening and treatment diffusion are based on national datasets; other parameters are based on a synthesis of the evidence available in the literature.
  The model is calibrated to predict incidence and stage distribution (in situ, local, regional, and distant). Other than screening or treatment, background events that affect mortality are not explicitly modeled but are captured in the deviation between model projections of mortality trends and actual trends. We assume that: 1) tumors progress more slowly in older age groups, 2) screen- and clinically detected disease have the same survival conditional on age and stage, 3) women do not die of breast cancer within the "lead time" period, 4) screening benefits are captured by shifts in stage at diagnosis, 4) tamoxifen benefits only ER-positive women, and 5) preclinical sojourn time and dwell times in each of the clinical stages are stochastically independent.
  Dissemination of screening and therapeutic advances had a substantial impact on mortality trends. We estimate that, by the year 2000, diffusion of screening lowered mortality by 12.4% and treatment improvements and dissemination lowered mortality by 14.6%.
  
  
  CONCLUSIONS
  Models such as this one can be useful to translate clinical trial findings to general populations. This model can also be used inform policy debates about how to best achieve targeted reductions in breast cancer morbidity and mortality.
authors.affiliation: Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20007, USA. mandelbj@georgetown.edu
authors.forename: Jeanne
authors.initials: J
authors.lastname: Mandelblatt
authors.forename: Clyde B
authors.initials: CB
authors.lastname: Schechter
authors.forename: William
authors.initials: W
authors.lastname: Lawrence
authors.forename: Bin
authors.initials: B
authors.lastname: Yi
authors.forename: Jennifer
authors.initials: J
authors.lastname: Cullen
mesh_terms: D000328:Adult; D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D000971:Antineoplastic Combined Chemotherapy Protocols / Q000627:therapeutic use*; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000188:drug therapy* / Q000401:mortality; D017024:Chemotherapy, Adjuvant; D018450:Disease Progression; D005260:Female; D006801:Humans; D015994:Incidence; D008327:Mammography / Q000706:statistics & numerical data; D008403:Mass Screening / Q000706:statistics & numerical data*; D008875:Middle Aged; D015233:Models, Statistical; D009367:Neoplasm Staging; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity; D013269:Stochastic Processes; D015996:Survival Rate; D014481:United States / Q000453:epidemiology