Literature DB >> 29554466

Common Model Inputs Used in CISNET Collaborative Breast Cancer Modeling.

Jeanne S Mandelblatt1, Aimee M Near1, Diana L Miglioretti2, Diego Munoz3, Brian L Sprague4, Amy Trentham-Dietz5, Ronald Gangnon5,6, Allison W Kurian7, Harald Weedon-Fekjaer8, Kathleen A Cronin9, Sylvia K Plevritis10.   

Abstract

BACKGROUND: Since their inception in 2000, the Cancer Intervention and Surveillance Network (CISNET) breast cancer models have collaborated to use a nationally representative core of common input parameters to represent key components of breast cancer control in each model. Employment of common inputs permits greater ability to compare model output than when each model begins with different input parameters. The use of common inputs also enhances inferences about the results, and provides a range of reasonable results based on variations in model structure, assumptions, and methods of use of the input values. The common input data are updated for each analysis to ensure that they reflect the most current practice and knowledge about breast cancer. The common core of parameters includes population rates of births and deaths; age- and cohort-specific temporal rates of breast cancer incidence in the absence of screening and treatment; effects of risk factors on incidence trends; dissemination of plain film and digital mammography; screening test performance characteristics; stage or size distribution of screen-, interval-, and clinically- detected tumors by age; the joint distribution of ER/HER2 by age and stage; survival in the absence of screening and treatment by stage and molecular subtype; age-, stage-, and molecular subtype-specific therapy; dissemination and effectiveness of therapies over time; and competing non-breast cancer mortality. METHOD AND
RESULTS: In this paper, we summarize the methods and results for the common input values presently used in the CISNET breast cancer models, note assumptions made because of unobservable phenomena and/or unavailable data, and highlight plans for the development of future parameters.
CONCLUSION: These data are intended to enhance the transparency of the breast CISNET models.

Entities:  

Keywords:  breast cancer epidemiology; cancer simulation; simulation models

Mesh:

Substances:

Year:  2018        PMID: 29554466      PMCID: PMC5862072          DOI: 10.1177/0272989X17700624

Source DB:  PubMed          Journal:  Med Decis Making        ISSN: 0272-989X            Impact factor:   2.583


  34 in total

1.  Collaborative Modeling of the Benefits and Harms Associated With Different U.S. Breast Cancer Screening Strategies.

Authors:  Jeanne S Mandelblatt; Natasha K Stout; Clyde B Schechter; Jeroen J van den Broek; Diana L Miglioretti; Martin Krapcho; Amy Trentham-Dietz; Diego Munoz; Sandra J Lee; Donald A Berry; Nicolien T van Ravesteyn; Oguzhan Alagoz; Karla Kerlikowske; Anna N A Tosteson; Aimee M Near; Amanda Hoeffken; Yaojen Chang; Eveline A Heijnsdijk; Gary Chisholm; Xuelin Huang; Hui Huang; Mehmet Ali Ergun; Ronald Gangnon; Brian L Sprague; Sylvia Plevritis; Eric Feuer; Harry J de Koning; Kathleen A Cronin
Journal:  Ann Intern Med       Date:  2016-01-12       Impact factor: 25.391

2.  Carcinoma of the breast; results from statistical research.

Authors:  J CLEMMESEN
Journal:  Br J Radiol       Date:  1948-12       Impact factor: 3.039

3.  Trends in use of adjuvant multi-agent chemotherapy and tamoxifen for breast cancer in the United States: 1975-1999.

Authors:  Angela Mariotto; Eric J Feuer; Linda C Harlan; Lap-Ming Wun; Karen A Johnson; Jeffrey Abrams
Journal:  J Natl Cancer Inst       Date:  2002-11-06       Impact factor: 13.506

4.  Performance of first mammography examination in women younger than 40 years.

Authors:  Bonnie C Yankaskas; Sebastien Haneuse; Julie M Kapp; Karla Kerlikowske; Berta Geller; Diana S M Buist
Journal:  J Natl Cancer Inst       Date:  2010-05-03       Impact factor: 13.506

Review 5.  A comparative review of CISNET breast models used to analyze U.S. breast cancer incidence and mortality trends.

Authors:  Lauren D Clarke; Sylvia K Plevritis; Rob Boer; Kathleen A Cronin; Eric J Feuer
Journal:  J Natl Cancer Inst Monogr       Date:  2006

6.  Race-specific impact of natural history, mammography screening, and adjuvant treatment on breast cancer mortality rates in the United States.

Authors:  Nicolien T van Ravesteyn; Clyde B Schechter; Aimee M Near; Eveline A M Heijnsdijk; Michael A Stoto; Gerrit Draisma; Harry J de Koning; Jeanne S Mandelblatt
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2010-11-30       Impact factor: 4.254

7.  A sustained decline in postmenopausal hormone use: results from the National Health and Nutrition Examination Survey, 1999-2010.

Authors:  Brian L Sprague; Amy Trentham-Dietz; Kathleen A Cronin
Journal:  Obstet Gynecol       Date:  2012-09       Impact factor: 7.661

8.  Prevalence of mammographically dense breasts in the United States.

Authors:  Brian L Sprague; Ronald E Gangnon; Veronica Burt; Amy Trentham-Dietz; John M Hampton; Robert D Wellman; Karla Kerlikowske; Diana L Miglioretti
Journal:  J Natl Cancer Inst       Date:  2014-09-12       Impact factor: 13.506

9.  Trends of postmenopausal estrogen plus progestin prevalence in the United States between 1970 and 2010.

Authors:  Patricia I Jewett; Ronald E Gangnon; Amy Trentham-Dietz; Brian L Sprague
Journal:  Obstet Gynecol       Date:  2014-10       Impact factor: 7.661

10.  Comparisons between different polychemotherapy regimens for early breast cancer: meta-analyses of long-term outcome among 100,000 women in 123 randomised trials.

Authors:  R Peto; C Davies; J Godwin; R Gray; H C Pan; M Clarke; D Cutter; S Darby; P McGale; C Taylor; Y C Wang; J Bergh; A Di Leo; K Albain; S Swain; M Piccart; K Pritchard
Journal:  Lancet       Date:  2011-12-05       Impact factor: 79.321

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  25 in total

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

Authors:  Sylvia K Plevritis; Diego Munoz; Allison W Kurian; Natasha K Stout; Oguzhan Alagoz; Aimee M Near; Sandra J Lee; Jeroen J van den Broek; Xuelin Huang; Clyde B Schechter; Brian L Sprague; Juhee Song; Harry J de Koning; Amy Trentham-Dietz; Nicolien T van Ravesteyn; Ronald Gangnon; Young Chandler; Yisheng Li; Cong Xu; Mehmet Ali Ergun; Hui Huang; Donald A Berry; Jeanne S Mandelblatt
Journal:  JAMA       Date:  2018-01-09       Impact factor: 56.272

2.  Comparative effectiveness of incorporating a hypothetical DCIS prognostic marker into breast cancer screening.

Authors:  Amy Trentham-Dietz; Mehmet Ali Ergun; Oguzhan Alagoz; Natasha K Stout; Ronald E Gangnon; John M Hampton; Kim Dittus; Ted A James; Pamela M Vacek; Sally D Herschorn; Elizabeth S Burnside; Anna N A Tosteson; Donald L Weaver; Brian L Sprague
Journal:  Breast Cancer Res Treat       Date:  2017-11-28       Impact factor: 4.872

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

Authors:  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; Harry J de Koning
Journal:  Med Decis Making       Date:  2018-04       Impact factor: 2.583

4.  Introduction to the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Models.

Authors:  Oguzhan Alagoz; Donald A Berry; Harry J de Koning; Eric J Feuer; Sandra J Lee; Sylvia K Plevritis; Clyde B Schechter; Natasha K Stout; Amy Trentham-Dietz; Jeanne S Mandelblatt
Journal:  Med Decis Making       Date:  2018-04       Impact factor: 2.583

5.  Structure, Function, and Applications of the Georgetown-Einstein (GE) Breast Cancer Simulation Model.

Authors:  Clyde B Schechter; Aimee M Near; Jinani Jayasekera; Young Chandler; Jeanne S Mandelblatt
Journal:  Med Decis Making       Date:  2018-04       Impact factor: 2.583

6.  The Dana-Farber CISNET Model for Breast Cancer Screening Strategies: An Update.

Authors:  Sandra J Lee; Xiaoxue Li; Hui Huang; Marvin Zelen
Journal:  Med Decis Making       Date:  2018-04       Impact factor: 2.583

7.  Modeling Ductal Carcinoma In Situ (DCIS): An Overview of CISNET Model Approaches.

Authors:  Nicolien T van Ravesteyn; Jeroen J van den Broek; Xiaoxue Li; Harald Weedon-Fekjær; Clyde B Schechter; Oguzhan Alagoz; Xuelin Huang; Donald L Weaver; Elizabeth S Burnside; Rinaa S Punglia; Harry J de Koning; Sandra J Lee
Journal:  Med Decis Making       Date:  2018-04       Impact factor: 2.583

8.  Simulating the Impact of Risk-Based Screening and Treatment on Breast Cancer Outcomes with MISCAN-Fadia.

Authors:  Jeroen J van den Broek; Nicolien T van Ravesteyn; Eveline A Heijnsdijk; Harry J de Koning
Journal:  Med Decis Making       Date:  2018-04       Impact factor: 2.583

9.  Comparing CISNET Breast Cancer Models Using the Maximum Clinical Incidence Reduction Methodology.

Authors:  Jeroen J van den Broek; Nicolien T van Ravesteyn; Jeanne S Mandelblatt; Mucahit Cevik; Clyde B Schechter; Sandra J Lee; Hui Huang; Yisheng Li; Diego F Munoz; Sylvia K Plevritis; Harry J de Koning; Natasha K Stout; Marjolein van Ballegooijen
Journal:  Med Decis Making       Date:  2018-04       Impact factor: 2.583

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

Authors:  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
Journal:  Med Decis Making       Date:  2018-04       Impact factor: 2.583

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