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Competing risks analysis in mortality estimation for breast cancer patients from independent risk groups

Author

Listed:
  • Shengfan Zhang
  • Julie Ivy
  • James Wilson
  • Kathleen Diehl
  • Bonnie Yankaskas

Abstract

This study quantifies breast cancer mortality in the presence of competing risks for complex patients. Breast cancer behaves differently in different patient populations, which can have significant implications for patient survival; hence these differences must be considered when making screening and treatment decisions. Mortality estimation for breast cancer patients has been a significant research question. Accurate estimation is critical for clinical decision making, including recommendations. In this study, a competing risks framework is built to analyze the effect of patient risk factors and cancer characteristics on breast cancer and other cause mortality. To estimate mortality probabilities from breast cancer and other causes as a function of not only the patient’s age or race but also biomarkers for estrogen and progesterone receptor status, a nonparametric cumulative incidence function is formulated using data from the community-based Carolina Mammography Registry. Based on the log(−log) transformation, confidence intervals are constructed for mortality estimates over time. To compare mortality probabilities in two independent risk groups at a given time, a method with improved power is formulated using the log(−log) transformation. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Shengfan Zhang & Julie Ivy & James Wilson & Kathleen Diehl & Bonnie Yankaskas, 2014. "Competing risks analysis in mortality estimation for breast cancer patients from independent risk groups," Health Care Management Science, Springer, vol. 17(3), pages 259-269, September.
    • Handle: RePEc:kap:hcarem:v:17:y:2014:i:3:p:259-269
    DOI: 10.1007/s10729-013-9255-x
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    References listed on IDEAS

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    1. Julie Simmons Ivy, 2009. "Can we Do Better? Optimization Models for Breast Cancer Screening," Springer Optimization and Its Applications, in: H. Edwin Romeijn & Panos M. Pardalos (ed.), Handbook of Optimization in Medicine, chapter 2, pages 25-52, Springer.
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