Evidence map›Paper›PMID 29554470›Full record

ArticleMedical decision making : an international journal of the Society for Medical Decision Making2018

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

Oguzhan Alagoz, Mehmet Ali Ergun, Mucahit Cevik, Brian L Sprague, Dennis G Fryback, Ronald E Gangnon, John M Hampton, Natasha K Stout, Amy Trentham-Dietz

Abstract read
In one paragraph

Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

39 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Mindfulness-based interventions for breast cancer patients in China across outcome domains: a systematic review and meta-analysis of the Chinese literature.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2021
    Pooled it
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  7. A Scoping Review on Calibration Methods for Cancer Simulation Models.Medical decision making : an international journal of the Society for Medical Decision Making · 2025
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  12. Modeling Thyroid Cancer Epidemiology in the United States Using Papillary Thyroid Carcinoma Microsimulation Model.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2024
    Article
  13. Article
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  15. Population simulation modeling of disparities in US breast cancer mortality.Journal of the National Cancer Institute. Monographs · 2023
    Article
  16. Article
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  18. Article
  19. Article
  20. The OncoSim-Breast Cancer Microsimulation Model.Current oncology (Toronto, Ont.) · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Oguzhan AlagozDepartment of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI.
Mehmet Ali ErgunDepartment of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI.
Mucahit CevikUniversity of Toronto, Toronto, ON, Canada.
Brian L SpragueDepartment of Surgery and University of Vermont Cancer Center, University of Vermont, Burlington, VT.
Dennis G FrybackDepartment of Population Health Sciences, University of Wisconsin-Madison, Madison, WI.
Ronald E GangnonDepartment of Population Health Sciences and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI.
John M HamptonDepartment of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI.
Natasha K StoutDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA.
Amy Trentham-DietzDepartment of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI.

Funding

UW COMPREHENSIVE CANCER CENTER SUPPORTP30CA014520 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI AMY TRENTHAM-DIETZ · 1985 to 2026
$142.6M
Risk-based Imaging Strategies to Improve Breast Cancer Surveillance OutcomesP01CA154292 · NCI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Diana L Miglioretti · 2011 to 2026
$52.9M
Comparative Modeling: Informing Breast Cancer Control Practice and PolicyU01CA199218 · NCI · GEORGETOWN UNIVERSITY · PI BERRY, DONALD A, DE KONING, HARRY J · 2015 to 2019
$9.0M
Comparative Modeling: Informing Breast Cancer Control Practice & PolicyU01CA152958 · NCI · GEORGETOWN UNIVERSITY · PI BERRY, DONALD A, DE KONING, HARRY J · 2010 to 2015
$7.6M
Vermont Screening Process Documentation UnitU54CA163303 · NCI · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI SPRAGUE, BRIAN L · 2011 to 2017
$6.4M
NCI NIH HHS HHSN261201100031CNCI NIH HHS P01 CA154292NCI NIH HHS P30 CA014520NCI NIH HHS U01 CA152958NCI NIH HHS U01 CA199218NCI NIH HHS U54 CA163303
6 · The paper itself

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.

Indexed as

Models, BiologicalAdultAgedAged, 80 and overBreast NeoplasmsComputer SimulationFemaleHumansMammographyMiddle AgedModels, StatisticalNeoplasm StagingReproducibility of ResultsRisk AssessmentRisk FactorsSEER Programbreast cancerincidencescreeningsimulation

Identifiers

PMID29554470
PMCPMC5862066

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.