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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
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Accounting for individualized competing mortality risks in estimating postmenopausal breast cancer risk
Mara A Schonberg1,2, Vicky W Li3, A Heather Eliassen4,5
1Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA. mschonbe@bidmc.harvard.edu.
Breast Cancer Research and Treatment
|November 3, 2016
Summary
A new breast cancer prediction model for postmenopausal women accounts for non-breast cancer death risks. This tool aids in personalized breast cancer prevention decisions.
Area of Science:
- Oncology
- Epidemiology
- Biostatistics
Background:
- Accurate breast cancer risk assessment is crucial for effective prevention strategies.
- Existing models may not adequately address competing risks of non-breast cancer mortality in postmenopausal women.
Purpose of the Study:
- To develop and validate a breast cancer prediction model for postmenopausal women.
- The model incorporates individualized competing risks of non-breast cancer death.
Main Methods:
- Utilized data from 73,066 women in the Nurses' Health Study (NHS) and 74,887 in the Women's Health Initiative Extension Study (WHI-ES).
- Considered 17 breast cancer risk factors and 7 non-breast cancer death risk factors, including comorbidities and functional dependency.
- Employed competing risk regression and validated the model using calibration (E/O) and discrimination (c-statistic).
Main Results:
- The final model included 9 breast cancer risk factors, 5 comorbidities, functional dependency, and mammography use.
- The model's discrimination (c-statistic) was 0.61 in NHS and 0.57 in WHI-ES.
- The model showed good calibration, with an expected-to-observed ratio of 0.92 in WHI-ES.
Conclusions:
- A novel prediction model was developed for postmenopausal women's breast cancer risk.
- This model uniquely integrates competing risks of non-breast cancer death.
- The model can enhance personalized breast cancer prevention decision-making.
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