The University of Wisconsin Breast Cancer Epidemiology Simulation Model: An Update

Abstract

The University of Wisconsin Breast Cancer Epidemiology Simulation Model (UWBCS), also referred to as Model W, is a discrete-event microsimulation model that uses a systems engineering approach to replicate breast cancer epidemiology in the US over time. This population-based model simulates the lifetimes of individual women through 4 main model components: breast cancer natural history, detection, treatment, and mortality. A key feature of the UWBCS is that, in addition to specifying a population distribution in tumor growth rates, the model allows for heterogeneity in tumor behavior, with some tumors having limited malignant potential (i.e., would never become fatal in a woman's lifetime if left untreated) and some tumors being very aggressive based on metastatic spread early in their onset. The model is calibrated to Surveillance, Epidemiology, and End Results (SEER) breast cancer incidence and mortality data from 1975 to 2010, and cross-validated against data from the Wisconsin cancer reporting system. The UWBCS model generates detailed outputs including underlying disease states and observed clinical outcomes by age and calendar year, as well as costs, resource usage, and quality of life associated with screening and treatment. The UWBCS has been recently updated to account for differences in breast cancer detection, treatment, and survival by molecular subtypes (defined by ER/HER2 status), to reflect the recent advances in screening and treatment, and to consider a range of breast cancer risk factors, including breast density, race, body-mass-index, and the use of postmenopausal hormone therapy. Therefore, the model can evaluate novel screening strategies, such as risk-based screening, and can assess breast cancer outcomes by breast cancer molecular subtype. In this article, we describe the most up-to-date version of the UWBCS.

Association of Screening and Treatment With Breast Cancer Mortality by Molecular Subtype in US Women, 2000-2012

“…Each group used a common set of inputs 22 based on their specific model structure, prior research, 15 and assumptions to best reproduce US breast cancer incidence and mortality trends (eTable 1 in the Supplement). 5,6,[10][11][12][13][14][15][16][17][22][23][24][25][26][27] Five models used age-period-cohort (APC) analyses to estimate 1975-2012 breast cancer incidence rates in the absence of screening (baseline incidence rate) 17,25 ; model M applied a Bayesian approach to extend 1975-1979 Surveillance Epidemiology and End Results (SEER) rates forward in time with a 4% (SD, 0.2%) annual increase. Plain-film and digital mammography sensitivity data from the Breast Cancer Surveillance Consortium (BCSC) for 1994-2012 were used to estimate sensitivity for detection of invasive and ductal carcinoma in situ cancers by age group, first vs subsequent screening, and time since last mammogram.…”

Cost-effectiveness of mammography from a publicly funded health care system perspective

“…Tumours are assumed to initially be in situ, and all tumours grow until they reach a maximum size. 16,17 Size is used as a surrogate for stage, and cancers are classified into 4 groups: in situ, localized, regional metastasis or distant metastasis. Thresholds for detection are defined for clinical discovery of the cancers or for detection by screening.…”

“…The model conducts individual and separate simulation modelling based on all women born in 1960 (1960 birth cohort). It has been validated against US data 12,16 and, in its modified form, against Canadian data. 19…”

Analysis of Mammography Screening Policies under Resource Constraints

“…More information about UWBCS can be found elsewhere (Alagoz et al. ). Personal breast cancer risk . We use a modified version of the Gail model (Costantino et al.…”