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Updated: May 26, 2026
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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Modelling population-based cancer survival trends using join point models for grouped survival data.
Binbing Yu1, Lan Huang, Ram C Tiwari
1National Institutes of Health, Bethesda, USA.
Summary
Analyzing cancer survival trends using joinpoint models reveals significant changes in survival rates. These findings help understand the impact of medical progress on cancer patient outcomes.
Area of Science:
- Oncology
- Biostatistics
- Public Health
Background:
- Cancer is the second leading cause of death in the US, posing a significant public health challenge.
- Medical advancements and early diagnosis have improved cancer patient prognoses.
- Evaluating population-level cancer survival trends is crucial for understanding the impact of medical progress.
Purpose of the Study:
- To analyze population-based cancer survival data using joinpoint models.
- To identify significant change points in cancer survival trends.
- To understand the relationship between medical improvements and patient survival experiences.
Main Methods:
- Incorporation of joinpoints into survival models for analyzing grouped cancer survival data.
- Utilizing the Bayesian Information Criterion to determine the optimal number of joinpoints.
- Conducting intensive simulation studies to evaluate model performance regarding prognosis, joinpoint identification, and annual percent changes in death rates.
Main Results:
- Application of the joinpoint survival model to grouped relative survival data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) Program.
- Identification of specific change points in survival trends for several major cancer sites.
- Discussion of potential driving forces behind the observed changes in cancer survival.
Conclusions:
- Joinpoint survival models effectively identify significant shifts in population-level cancer survival trends.
- The identified change points offer insights into the impact of cancer treatments and interventions over time.
- Further investigation into the driving forces behind these trend changes is warranted to inform public health strategies.
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