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Correlation-based joint feature screening for semi-competing risks outcomes with application to breast cancer data
1Academy of Statistics and Interdisciplinary Sciences, 12655East China Normal University, China.
Statistical Methods in Medical Research
|September 14, 2021
Summary
This study introduces a novel method to identify key genes impacting both distant metastasis-free survival and overall survival in cancer. The approach effectively handles ultrahigh-dimensional gene expression data under semi-competing risks, aiding precision medicine.
Area of Science:
- Genomics
- Biostatistics
- Cancer Research
Background:
- Cancer studies generate ultrahigh-dimensional gene expression data, posing challenges for survival prediction and genetic understanding.
- Predicting patient survival using genetic predictors is complex, especially with multiple survival endpoints and semi-competing risks.
Purpose of the Study:
- To develop a method for extracting impactful gene features from massive gene expression data in a semi-competing risks setting.
- To jointly identify genes affecting both distant metastasis-free survival and overall survival.
Main Methods:
- A model-free screening method is proposed, ranking gene features by their correlation with the joint survival function.
- The method incorporates a utility measure to account for the relationship between the two survival endpoints.
Main Results:
- The proposed method demonstrates sure screening and ranking consistency properties.
- Extensive simulations confirm favorable finite sample performance.
- Application to breast cancer data showcases practical utility in classifying disease outcomes.
Conclusions:
- The developed method effectively identifies critical genes influencing multiple survival outcomes in ultrahigh-dimensional data.
- This approach supports precision medicine by enhancing genetic understanding and survival prediction in complex cancer studies.
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