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Establishing a Competing Risk Regression Nomogram Model for Survival Data

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Published on: October 23, 2020

Statistical approaches to identifying significant differences in predictive performance between machine learning and

Justine B Nasejje¹, Albert Whata², Charles Chimedza¹

¹School of Statistics and Actuarial Science, University of the Witwatersrand, Johannesburg, Gauteng, South Africa.

|December 28, 2022

Summary

Comparing predictive models like Fine-Gray (FG) and random survival forest (RSF) requires robust statistics. Our study shows RSF excels with complex data, while FG performs better with linear relationships, with significant differences found.

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Area of Science:

Statistics
Machine Learning
Biostatistics

Background:

Comparing predictive model performance necessitates rigorous statistical validation.
Estimates of model performance often lack evidence of true differences.

Purpose of the Study:

To statistically compare the predictive performance of Fine-Gray (FG) and random survival forest (RSF) models for competing risks.
To evaluate model performance across simulated low-dimensional survival data with varying predictor relationships.

Main Methods:

Application of two statistical tests: 5 × 2-fold cross-validation paired t-test and combined 5 × 2-fold cross-validation F-test.
Training and testing FG and RSF models on simulated survival data under linear, quadratic, and interaction scenarios.
Repeating simulations 100 times across 10 different seeds for reliability.

Main Results:

The random survival forest (RSF) model demonstrated superior predictive performance with complex relationships (quadratic and interactions).
The Fine-Gray (FG) model showed superior predictive performance in linear simulations, with significant differences compared to RSF.
Statistical tests confirmed significant performance differences in quadratic simulations but not in interaction simulations.

Conclusions:

RSF is superior for complex predictor-outcome relationships, while FG is better for linear relationships in competing risks prediction.
The combined 5 × 2-fold cv F-test exhibited lower Type I error rates than the 5 × 2-fold cv paired t-test.
Rigorous statistical testing is crucial for validating differences in predictive model performance.