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05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
High-dimensional, massive sample-size Cox proportional hazards regression for survival analysis
Sushil Mittal1, David Madigan, Randall S Burd
1Department of Statistics, Columbia University, New York, NY 10027, USA.
Biostatistics (Oxford, England)
|October 8, 2013
Summary
This study introduces new tools for survival analysis on large, complex datasets. These methods improve prediction accuracy and calibration for high-dimensional, massive sample-size data.
Area of Science:
- Statistics
- Biostatistics
- Machine Learning
Background:
- Survival analysis is a critical statistical field with broad applications.
- Modern data acquisition generates high-dimensional, massive sample-size (HDMSS) datasets, challenging traditional methods.
- Existing survival analysis techniques require adaptation for HDMSS data.
Purpose of the Study:
- To present novel computational tools for fitting regularized Cox survival analysis models.
- To address the challenges posed by high-dimensional, massive sample-size (HDMSS) data.
- To enhance the efficiency and performance of survival analysis on large datasets.
Main Methods:
- Development of a variant of the cyclic coordinate descent optimization technique.
- Tailoring the optimization method for sparsity inherent in HDMSS data.
- Implementation of tools for fitting regularized Cox survival models.
Main Results:
- Demonstrated efficiency in analyzing HDMSS data.
- Achieved improved predictive performance compared to existing methods.
- Showcased enhanced calibration of survival models on real-world data.
Conclusions:
- The presented tools offer an efficient approach for survival analysis on HDMSS data.
- The methods lead to superior predictive accuracy and model calibration.
- These advancements facilitate the application of survival analysis to complex, large-scale datasets.
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