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An R-Based Landscape Validation of a Competing Risk Model
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Exploring Causal Effects of Hormone- and Radio-Treatments in an Observational Study of Breast Cancer Using
Tonghui Yu1, Mengjiao Peng2, Yifan Cui3
1School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore.
Statistics in Medicine
|June 4, 2025
Summary
This study introduces a new statistical framework to analyze breast cancer outcomes, addressing semi-competing risks for more accurate treatment effect evaluation. The method enhances causal inference and sensitivity analysis for patient survival data.
Area of Science:
- Biostatistics
- Medical Statistics
- Survival Analysis
Background:
- Breast cancer patients face relapse or death post-surgery, a phenomenon termed semi-competing risk.
- Analyzing treatment effects in semi-competing risk scenarios requires advanced statistical methods for unbiased results.
- Current causal inference applications for semi-competing risks regression are limited.
Purpose of the Study:
- To propose a novel frequentist and semi-parametric framework for causal inference in semi-competing risks data.
- To enable valid estimation and interpretation of net quantities and perform sensitivity analysis for unmeasured factors.
- To enhance parameter estimation and practical applicability in breast cancer research.
Main Methods:
- Development of a copula-based framework for right-censored semi-competing risks data.
- Introduction of novel procedures for parameter estimation and practical application.
- Application to a breast cancer dataset to analyze time-varying treatment effects.
Main Results:
- The proposed framework facilitates valid causal inference and sensitivity analysis.
- Novel procedures improve parameter estimation and practical applicability.
- Application revealed time-varying causal effects of hormone and radio-treatments on breast cancer relapse and survival.
Conclusions:
- The developed statistical framework offers a robust approach to causal inference in semi-competing risks.
- Extensive evaluations confirm the method's feasibility, minimal bias, and reliable inference.
- This research advances the analysis of treatment effects in breast cancer survival studies.
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