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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

Partial logistic artificial neural network for competing risks regularized with automatic relevance determination.

Paulo J G Lisboa¹, Terence A Etchells, Ian H Jarman

¹School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool L33AF, UK. p.j.lisboa@ljmu.ac.uk

IEEE Transactions on Neural Networks

|July 25, 2009

Summary

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Relative Risk

Relative Risk

Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...

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This study introduces a new neural network model for competing risks analysis, enhancing risk prediction accuracy. The PLANNCR-ARD model improves hazard assessment for multiple, mutually exclusive factors in areas like cancer recurrence.

Area of Science:

Biostatistics
Machine Learning
Medical Informatics

Background:

Time-to-event analysis is crucial for clinical prognosis and risk modeling in finance and insurance.
Simultaneous assessment of hazards from multiple, mutually exclusive factors is often required in risk modeling.
Existing models may not fully capture the complexities of competing risks in predictive modeling.

Purpose of the Study:

To adapt and apply an existing neural network model for competing risks (PLANNCR) with Bayesian regularization.
To implement automatic relevance determination (ARD) within the PLANNCR framework (PLANNCR-ARD).
To illustrate the model's application in predicting local and distal breast cancer recurrence.

Main Methods:

Utilized a neural network model for competing risks (PLANNCR).

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Establishing a Competing Risk Regression Nomogram Model for Survival Data

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An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Incorporated Bayesian regularization with standard approximation of the evidence.

Implemented automatic relevance determination (ARD) for enhanced model feature assessment.

Main Results:

The PLANNCR-ARD model provides a robust framework for competing risks analysis.
Demonstrated successful application in predicting breast cancer recurrence using real-world data.
The integration of ARD enhances the model's ability to handle complex risk factors.

Conclusions:

The PLANNCR-ARD model offers a powerful tool for time-to-event analysis with competing risks.
This approach is valuable for improving prognostic accuracy in clinical settings, such as breast cancer.
The methodology can be extended to other fields requiring simultaneous hazard assessment of multiple factors.