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An R-Based Landscape Validation of a Competing Risk Model

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Published on: September 16, 2022

Support Vector Hazards Machine: A Counting Process Framework for Learning Risk Scores for Censored Outcomes.

Yuanjia Wang¹, Tianle Chen², Donglin Zeng³

¹Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.

Journal of Machine Learning Research : JMLR

|January 10, 2017

Summary

This study introduces a novel Support Vector Hazards Machine (SVHM) for predicting time-to-event outcomes with censoring. SVHM improves prediction accuracy for event times compared to existing machine learning and conventional methods.

Keywords:

early disease detection neuroimaging biomarkers risk bound risk prediction support vector machine survival analysis

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Establishing a Competing Risk Regression Nomogram Model for Survival Data

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Published on: September 16, 2022

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

Machine Learning
Survival Analysis
Biostatistics

Background:

Machine learning for risk scores is established for dichotomous/continuous outcomes.
Predicting time-to-event outcomes with right censoring using machine learning is less explored.
Existing methods like inverse probability weighting and rank-based regression may lack efficiency.

Purpose of the Study:

To develop a novel machine learning approach, Support Vector Hazards Machine (SVHM), for predicting time-to-event outcomes with right censoring.
To connect Support Vector Machines (SVM) with hazards regression through counting processes.
To establish theoretical properties and demonstrate practical superiority of SVHM.

Main Methods:

Developed Support Vector Hazards Machine (SVHM) using a series of support vector machines to predict counting processes for time-to-event data.
Incorporated a time-varying offset to account for varying at-risk populations.
Utilized convex quadratic programming and the kernel trick for non-linearity.
Linked SVHM's empirical risk function to Cox partial likelihood.

Main Results:

Formally demonstrated SVHM's optimality in discriminating covariate-specific hazard functions from population average hazard functions.
Established consistency and learning rates for predicted risks.
Simulation studies showed improved prediction accuracy of event times compared to existing methods.
Real-world biomedical data analysis demonstrated SVHM's superiority in distinguishing high-risk from low-risk subjects.

Conclusions:

SVHM offers a powerful and theoretically sound approach for learning risk scores in time-to-event prediction with censoring.
The method demonstrates improved predictive performance over existing machine learning and conventional survival analysis techniques.
SVHM has practical utility in biomedical research for risk stratification using clinical and neuroimaging biomarkers.