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Published on: October 23, 2020
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Rotation survival forest for right censored data.
Lifeng Zhou1, Qingsong Xu1, Hong Wang1
1School of Mathematics and Statistics, Central South University , China.
Peerj
|June 18, 2015
Summary
Rotation survival forests extend classification methods for censored time-to-event data. This new ensemble outperforms existing methods and offers variable importance for survival analysis.
Area of Science:
- Biostatistics
- Machine Learning
- Survival Analysis
Background:
- Censored time-to-event data are common in biological and medical research.
- Existing survival ensembles have limitations in handling such data.
Purpose of the Study:
- To extend rotation forests, a classification algorithm, to survival analysis.
- To evaluate the performance of rotation survival forests on censored data.
Main Methods:
- Extending the rotation forest algorithm for survival data.
- Statistical analysis of right-censored time-to-event data.
- Development of a C-index based variable importance measure.
Main Results:
- Rotation survival forests demonstrate superior performance compared to state-of-the-art survival ensembles.
- The proposed method effectively handles right-censored data.
- A novel variable importance measure is introduced for censored survival data.
Conclusions:
- Rotation survival forests are a viable and effective extension for survival analysis.
- This approach offers improved performance and new analytical tools for censored time-to-event data.
- The variable importance measure aids in understanding covariate effects in survival models.
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