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Updated: Aug 8, 2026
05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Cross-validated bagged prediction of survival
Sandra E Sinisi1, Romain Neugebauer, Mark J van der Laan
1University of California, Berkeley, USA. ssinisi@stat.berkeley.edu
Summary
This study applies a novel Deletion/Substitution/Addition algorithm for survival prediction with right-censored data. The enhanced method improves survival time predictions for diffuse large B-cell lymphoma patients using gene expression.
Area of Science:
- Biostatistics
- Bioinformatics
- Genomics
Background:
- Survival data analysis is crucial in clinical research.
- Right-censoring is a common challenge in survival data.
- Gene expression data holds potential for predicting patient outcomes.
Purpose of the Study:
- To adapt the Deletion/Substitution/Addition algorithm for right-censored survival data.
- To integrate bagging for robust, cross-validated survival prediction.
- To predict survival time in diffuse large B-cell lymphoma (DLBCL) patients.
Main Methods:
- Application of the Deletion/Substitution/Addition algorithm to right-censored data.
- Incorporation of bagging to create a cross-validated bagged estimator.
- Utilizing gene expression variables for survival time prediction in DLBCL.
Main Results:
- Demonstration of the Deletion/Substitution/Addition algorithm's applicability to right-censored survival data.
- Successful implementation of a cross-validated bagged estimator through bagging.
- Prediction of survival times for DLBCL patients using gene expression data.
Conclusions:
- The Deletion/Substitution/Addition algorithm is effective for survival prediction with right-censoring.
- Bagging enhances the algorithm, providing a reliable cross-validated estimator.
- Gene expression data can be leveraged for accurate survival time prediction in DLBCL.
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