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Sensitivity Analysis for Survival Prognostic Prediction with Gene Selection: A Copula Method for Dependent Censoring.
Chih-Tung Yeh1, Gen-Yih Liao1, Takeshi Emura2,3
1Department of Information Management, Chang Gung University, Taoyuan 33302, Taiwan.
Biomedicines
|March 29, 2023
Summary
This study introduces a new method to assess how dependent censoring affects gene-based survival predictions. The developed web application helps analyze this impact on multi-gene predictors for cancer prognosis.
Area of Science:
- Bioinformatics
- Statistical genetics
- Computational biology
Background:
- Prognostic survival analysis commonly uses gene expression data from tumor tissues.
- Dependent censoring in survival data can lead to inaccurate gene effect identification with traditional Cox models.
- Copula-based models adjust for dependent censoring, creating multi-gene predictors for survival prognosis.
Purpose of the Study:
- To develop and implement a sensitivity analysis method for evaluating the impact of dependent censoring on multi-gene survival predictors.
- To create a practical web application for this sensitivity analysis.
- To provide a template for developers to build their own web applications.
Main Methods:
- Proposed a sensitivity analysis method using a copula-graphic estimator under dependent censoring.
- Implemented the method in the R package "compound.Cox".
- Developed a user-friendly web application for practical application of the method.
Main Results:
- The proposed method effectively investigates the sensitivity of multi-gene predictors to various dependent censoring mechanisms.
- The web application facilitates the practical application of this sensitivity analysis.
- Demonstrated the utility of the method and application using a lung cancer dataset.
Conclusions:
- The developed sensitivity analysis method and web application are valuable tools for assessing the robustness of multi-gene survival predictors under dependent censoring.
- This approach enhances the reliability of gene expression-based prognostic models.
- The provided template supports broader adoption and customization for different research needs.
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