ArticleMedical decision making : an international journal of the Society for Medical Decision Making2018
A Molecular Subtype-Specific Stochastic Simulation Model of US Breast Cancer Incidence, Survival, and Mortality Trends from 1975 to 2010.
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 10 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- The predictive value of multiple artificial intelligence models in axillary lymph node metastasis of breast cancer detected by ultrasound - a network meta-analysis.Frontiers in oncology · 2026Pooled it
- Mathematical Modeling to Address Questions in Breast Cancer Screening: An Overview of the Breast Cancer Models of the Cancer Intervention and Surveillance Modeling Network.Journal of breast imaging · 2025Review
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- Reflecting on 20 years of breast cancer modeling in CISNET: Recommendations for future cancer systems modeling efforts.PLoS computational biology · 2021Review
- Introduction to the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Models.Medical decision making : an international journal of the Society for Medical Decision Making · 2018Article
- Comparing CISNET Breast Cancer Models Using the Maximum Clinical Incidence Reduction Methodology.Medical decision making : an international journal of the Society for Medical Decision Making · 2018Article
- Comparing CISNET Breast Cancer Incidence and Mortality Predictions to Observed Clinical Trial Results of Mammography Screening from Ages 40 to 49.Medical decision making : an international journal of the Society for Medical Decision Making · 2018Article
- Estimating Breast Cancer Survival by Molecular Subtype in the Absence of Screening and Adjuvant Treatment.Medical decision making : an international journal of the Society for Medical Decision Making · 2018Article
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Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
We present a Monte Carlo simulation model that reproduces US invasive breast cancer incidence and mortality trends from 1975 to 2010 as a function of screening and adjuvant treatment. This model was developed for multiple purposes, including to quantify the impact of screening and adjuvant therapy on past and current trends, predict future trends, and evaluate potential outcomes under hypothetical screening and treatment interventions. The model first generates the life histories of individual breast cancer patients by determining the patient's age, tumor size, estrogen receptor (ER) status, human epidermal growth factor 2 (HER2) status, SEER (Surveillance, Epidemiology, and End Results) historic stage, detection mode at time of detection, preclinical tumor course, and death age and cause of death (breast cancer v. other causes). The model incorporates common inputs used by the Cancer Intervention and Surveillance Modeling Network (CISNET), including the dissemination patterns for screening mammography, breast cancer survival in the absence of adjuvant therapy, dissemination and efficacy of treatment by ER and HER2 status, and death from causes other than breast cancer. In this article, predicted mortality outcomes are compared assuming proportional v. nonproportional hazards effects of treatment on breast cancer survival. We found that the proportional hazards treatment effects are sufficient for ER-negative disease. However, for ER-positive disease, the treatment effects appear to be higher during the early years following diagnosis and then diminish over time. Using nonproportional hazards effects for ER-positive cases, the predicted breast cancer mortality rates closely match the SEER mortality trends from 1975 to 2010, particularly after 1995. Our work indicates that population-level simulation modeling may have a broader role in assessing the time dependence of treatment effects.
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Registered trials
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.