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A Molecular Subtype–Specific Stochastic Simulation Model of US Breast Cancer Incidence, Survival, and Mortality Trends from 1975 to 2010

Author

Listed:
  • Diego F. Munoz

    (Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA)

  • Cong Xu

    (Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA)

  • Sylvia K. Plevritis

    (Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA)

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.

Suggested Citation

  • Diego F. Munoz & Cong Xu & Sylvia K. Plevritis, 2018. "A Molecular Subtype–Specific Stochastic Simulation Model of US Breast Cancer Incidence, Survival, and Mortality Trends from 1975 to 2010," Medical Decision Making, , vol. 38(1_suppl), pages 89-98, April.
    • Handle: RePEc:sae:medema:v:38:y:2018:i:1_suppl:p:89s-98s
    DOI: 10.1177/0272989X17737508
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