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Updated: Jun 2, 2026
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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Semiparametric Accelerated Failure Time Partial Linear Model and Its Application to Breast Cancer
Yubo Zou1, Jiajia Zhang, Guoyou Qin
1Department of Epidemiology and Biostatistics, University of South Carolina Columbia, SC 29208, USA.
Computational Statistics & Data Analysis
|April 19, 2011
Summary
Breast cancer survival rates vary by age at diagnosis, with a unique pattern observed. Women diagnosed around age 38 show higher survival than younger or older women, highlighting age as a key factor in breast cancer outcomes.
Area of Science:
- Oncology
- Biostatistics
Background:
- Breast cancer is a leading cause of cancer death in U.S. women.
- Survival rates for most cancers decrease with age, but breast cancer exhibits an unusual age-related pattern.
Purpose of the Study:
- To investigate the stage risk and age effects on breast cancer survival.
- To develop a statistical model for analyzing these effects.
Main Methods:
- Proposed a semiparametric accelerated failure time partial linear model.
- Utilized P-spline and rank estimation for method development.
- Conducted simulation studies to compare the proposed method with parametric approaches.
Main Results:
- The proposed method is robust to data contamination compared to parametric methods.
- Significant effects of cancer stage on survival were identified.
- Women diagnosed with breast cancer around age 38 demonstrated consistently higher survival rates.
Conclusions:
- The semiparametric accelerated failure time partial linear model effectively reveals stage and age effects in breast cancer.
- Age at diagnosis, particularly around 38 years, is a significant prognostic factor for breast cancer survival.
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