Related Experiment Video
Updated: May 29, 2025
05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
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Multiple third-variable analysis for competing risk data-With an application to explore racial disparity in breast
Qingzhao Yu1, Lin Zhu2, Lu Zhang3
1School of Public Health, LSU Health, New Orleans, LA, 70112, USA.
Stat
|February 3, 2025
Summary
African-American women face higher breast cancer recurrence rates than Caucasian women. Tumor characteristics, subtypes, treatment, and environment explain 50% of this disparity.
Area of Science:
- Oncology
- Biostatistics
- Health Disparities Research
Background:
- Racial and ethnic disparities in cancer outcomes are a significant public health concern.
- African-American women exhibit higher breast cancer recurrence rates compared to Caucasian women.
Purpose of the Study:
- To identify and quantify risk factors contributing to racial disparities in breast cancer recurrence.
- To explore the impact of tumor characteristics, cancer subtypes, treatment, and environmental factors on these disparities.
Main Methods:
- Utilized the Fine-Gray model for competing risks analysis in a multiple third-variable framework.
- Addressed challenges of left-truncated and right-censored data with specific weighting strategies for different racial populations.
- Developed and applied a novel algorithm for analyzing breast cancer recurrence data from Louisiana patients diagnosed in 2011.
Main Results:
- Racial disparities in breast cancer recurrence were partially explained by tumor characteristics at diagnosis, cancer subtypes, treatment modalities, and residential environmental conditions.
- The identified factors accounted for 50% of the observed racial disparity in cancer recurrence.
- The proposed methodology was successfully implemented in the R package 'mma'.
Conclusions:
- Tumor characteristics, cancer subtypes, treatment, and environmental factors play a significant role in explaining racial disparities in breast cancer recurrence.
- The developed statistical approach effectively quantifies the contribution of various factors to health disparities.
- Further research into these contributing factors may inform targeted interventions to reduce breast cancer outcome disparities.
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