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Related Experiment Video

Updated: Aug 31, 2025

An R-Based Landscape Validation of a Competing Risk Model
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Scalable Algorithms for Large Competing Risks Data.

Eric S Kawaguchi1, Jenny I Shen2, Marc A Suchard3,4,5

  • 1Department of Preventive Medicine, University of Southern California.

Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
|August 19, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces faster algorithms for sparse regression in competing risks time-to-event data. New methods significantly accelerate the broken adaptive ridge (BAR) method and PSH model fitting, enabling analysis of large datasets.

Keywords:
Broken Adaptive RidgeFine-Gray modelMassive Sample SizeModel Selection/Variable selectionOracle propertySubdistribution hazardℓ0-regularization

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Area of Science:

  • Biostatistics
  • Statistical Computing
  • Survival Analysis

Background:

  • Competing risks time-to-event data analysis is crucial in many fields.
  • Existing methods for sparse regression in proportional subdistributional hazards (PSH) models, like the broken adaptive ridge (BAR) method, face scalability challenges.
  • Computational costs for fitting PSH models increase quadratically with sample size.

Purpose of the Study:

  • To develop scalable sparse regression methods for competing risks data.
  • To accelerate the broken adaptive ridge (BAR) algorithm for PSH models.
  • To reduce the computational complexity of fitting PSH models.

Main Methods:

  • Developed a cyclic update algorithm (cycBAR) for BAR regression, improving computational efficiency.
  • Proposed a novel forward-backward scan algorithm to reduce PSH model fitting costs from O(n^2) to O(n).
  • Combined cycBAR and the forward-backward scan for substantial speedups.

Main Results:

  • The cycBAR algorithm provides significant speedups over the original BAR method.
  • The forward-backward scan algorithm drastically reduces computation time for PSH model fitting.
  • The combined approach achieves over 1,000-fold speedups compared to the original BAR algorithm.
  • Demonstrated impressive scalability on simulated and real-world (US Renal Data System) competing risks data.

Conclusions:

  • The proposed algorithms offer significant computational advantages for sparse regression in competing risks.
  • These advancements enable the analysis of large-scale competing risks datasets, previously computationally prohibitive.
  • The methods are applicable to both penalized and unpenalized PSH model estimation.