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Updated: May 15, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data

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Published on: October 23, 2020

On a Bayesian multivariate survival tree approach based on three frailty models.

Patcharaporn Porndumnernsawat¹, Till D Frank², Lily Ingsrisawang³

¹Department of Mathematics and Computer Science, Faculty of Science and Technology, Rajamangala University of Technology Krungthep, Bangkok, Thailand.

Scientific Reports

|April 8, 2025

Summary

The Bayesian multivariate survival tree with a Weibull distribution demonstrated superior accuracy in classifying clustered survival data. Model performance improved with larger cluster numbers and sizes, but decreased with higher censoring rates.

Keywords:

Bayesian survival trees Classification accuracy Frailty model Tooth loss

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Area of Science:

Biostatistics
Survival Analysis
Machine Learning

Background:

Clustered survival data presents unique analytical challenges due to correlated failure times.
Accurate classification is crucial for understanding disease progression and treatment efficacy in such data.

Purpose of the Study:

To compare the classification performance of a Bayesian multivariate survival tree with shared gamma frailty models.
To evaluate the impact of baseline hazard functions and data characteristics on model accuracy.

Main Methods:

A simulation study generated 90 clustered survival datasets with correlated failure times and covariates.
Compared Bayesian multivariate survival trees (extended Cox w/ gamma frailty) against shared gamma frailty models (exponential & Weibull baseline hazards).
Evaluated performance across varying cluster numbers, sizes, and censoring rates using 70/30 train/test splits.

Main Results:

The Bayesian multivariate survival tree with a Weibull baseline hazard achieved the highest classification accuracy.
Accuracy increased with greater cluster size and number of clusters for all models.
Accuracy decreased as the right censoring rate increased.

Conclusions:

The Bayesian multivariate survival tree approach, utilizing a Weibull baseline hazard function, is recommended for clustered survival data classification.
Model performance is sensitive to data structure, particularly cluster size, number, and censoring proportion.