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The Effect of Treatment Advances on the Mortality Results of Breast Cancer Screening Trials: A Microsimulation ModelBreast Cancer Screening and Treatment Advances

Jeanette Birnbaum, PhD, MPH; Vijayakrishna K. Gadi, MD, PhD; Elan Markowitz; and Ruth Etzioni, PhD
[+] Article, Author, and Disclosure Information

This article was published at www.annals.org on 12 January 2016.

From University of Washington and Fred Hutchinson Cancer Research Center, Seattle, Washington.

Disclaimer: The results and conclusions are those of the authors only and do not reflect the opinions of the University of Washington or the National Cancer Institute.

Grant Support: In part by a Comparative Effectiveness Research dissertation award from the University of Washington Pharmaceutical Outcomes Research and Policy Program and by the National Cancer Institute (grant 1RC4CA155806).

Disclosures: Authors have disclosed no conflicts of interest. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M15-0754.

Editors' Disclosures: Christine Laine, MD, MPH, Editor in Chief, reports that she has no financial relationships or interests to disclose. Darren B. Taichman, MD, PhD, Executive Deputy Editor, reports that he has no financial relationships or interests to disclose. Cynthia D. Mulrow, MD, MSc, Senior Deputy Editor, reports that she has no relationships or interests to disclose. Deborah Cotton, MD, MPH, Deputy Editor, reports that she has no financial relationships or interest to disclose. Jaya K. Rao, MD, MHS, Deputy Editor, reports that she has stock holdings/options in Eli Lilly and Pfizer. Sankey V. Williams, MD, Deputy Editor, reports that he has no financial relationships or interests to disclose. Catharine B. Stack, PhD, MS, Deputy Editor for Statistics, reports that she has stock holdings in Pfizer.

Reproducible Research Statement:Study protocol: Available from Dr. Etzioni (e-mail, retzioni@fredhutch.org). Statistical code: Available at https://github.com/netterie/screentreat. Data set: All data are from publicly available sources (see Appendix Table 1). The user interface for the model is available at https://emarkowitz.shinyapps.io/screening_trial_updater.

Requests for Single Reprints: Ruth Etzioni, PhD, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, M2-B230, PO Box 19024, Seattle, WA 98109; e-mail, retzioni@fredhutch.org.

Current Author Addresses: Dr. Birnbaum: Department of Health Services, University of Washington, 1959 Northeast Pacific Street, Seattle, WA 98195.

Dr. Gadi: Division of Medical Oncology, Department of Medicine, University of Washington, 825 Eastlake Avenue East, Seattle, WA 98109.

Mr. Markowitz and Dr. Etzioni: Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, M2-B230, PO Box 19024, Seattle, WA 98109.

Author Contributions: Conception and design: J. Birnbaum, V.K. Gadi, E. Markowitz, R. Etzioni.

Analysis and interpretation of the data: J. Birnbaum, V.K. Gadi, E. Markowitz, R. Etzioni.

Drafting of the article: J. Birnbaum, V.K. Gadi, R. Etzioni.

Critical revision of the article for important intellectual content: V.K. Gadi, R. Etzioni.

Final approval of the article: J. Birnbaum, V.K. Gadi, R. Etzioni.

Statistical expertise: J. Birnbaum, R. Etzioni.

Obtaining of funding: J. Birnbaum, R. Etzioni.

Collection and assembly of data: J. Birnbaum, E. Markowitz, R. Etzioni.

Ann Intern Med. 2016;164(4):236-243. doi:10.7326/M15-0754
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Background: Mammography trials, which are the primary sources of evidence for screening benefit, were conducted decades ago. Whether advances in systemic therapies have rendered previously observed benefits of screening less significant is unknown.

Objective: To compare the outcomes of breast cancer screening trials had they been conducted using contemporary systemic treatments with outcomes of trials conducted with previously used treatments.

Design: Computer simulation model of 3 virtual screening trials with similar reductions in advanced-stage cancer cases but reflecting treatment patterns in 1975 (prechemotherapy era), 1999, or 2015 (treatment according to receptor status).

Data Sources: Meta-analyses of screening and treatment trials; study of dissemination of primary systemic treatments; SEER (Surveillance, Epidemiology, and End Results) registry.

Target Population: U.S. women aged 50 to 74 years.

Time Horizon: 10 and 25 years.

Perspective: Population.

Intervention: Mammography, chemotherapy, tamoxifen, aromatase inhibitors, and trastuzumab.

Outcome Measures: Breast cancer mortality rate ratio (MRR) and absolute risk reduction (ARR) obtained by the difference in cumulative breast cancer mortality between control and screening groups.

Results of Base-Case Analysis: At 10 years, screening in a 1975 trial yielded an MRR of 90% and an ARR of 5 deaths per 10 000 women. A 2015 screening trial yielded a 10-year MRR of 90% and an ARR of 3 deaths per 10 000 women.

Results of Sensitivity Analysis: Greater reductions in advanced-stage disease yielded a greater screening effect, but MRRs remained similar across trials. However, ARRs were consistently lower under contemporary treatments. When contemporary treatments were available only for early-stage cases, the MRR was 88%.

Limitation: Disease models simplify reality and cannot capture all breast cancer subtypes.

Conclusion: Advances in systemic therapies for breast cancer have not substantively reduced the relative benefits of screening but have likely reduced the absolute benefits because of their positive effect on breast cancer survival.

Primary Funding Source: University of Washington and National Cancer Institute.


Grahic Jump Location

Breast cancer states in the state-transition model.

Healthy women are at risk for clinical breast cancer diagnosis in the absence of screening. Patients clinically diagnosed with breast cancer may have either early- or advanced-stage disease at diagnosis, and their survival in the absence of screening was assigned accordingly. The effect of screening is to reduce the chance of diagnosis in the advanced stage; for a fraction of advanced-stage cases, the stage at diagnosis was reclassified as early with a commensurate change in disease-specific survival.

Grahic Jump Location
Grahic Jump Location
Appendix Figure.

10-year MRRs for trials in which treatment efficacies vary by stage and screening reduces advanced-stage incidence by 15%.

Each cell contains the within-trial MRR of screening for a trial in which a single treatment is given to all early-stage cases and another treatment is given to all advanced cases. The columns indicate the early-stage HRs, or benefit of the early-stage treatment in the trial, and the rows indicate the HRs for the advanced-stage treatment in the trial. The top left cell indicates that with no effective early- or advanced-stage treatments, the stage-shift model projects an MRR of 0.91 as the effect of screening on mortality. Trials in which advanced-stage treatment is better than early-stage treatment find a smaller effect of screening (dark green cells) than trials in which early-stage treatment is better than advanced-stage treatment (white cells). HR = hazard ratio; MRR = mortality rate ratio.

Grahic Jump Location




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