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Related Experiment Video

Updated: Jun 25, 2026

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

Semi-parametric additive risk models: application to injury duration study.

Hyun J Lim¹, Xu Zhang

¹Department of Community Health and Epidemiology, College of Medicine, University of Saskatchewan, 107 Wiggins Road, Saskatoon, SK S7N 5E5, Canada. hyun.lim@usask.ca

Accident; Analysis and Prevention

|February 28, 2009

Summary

This summary is machine-generated.

The additive hazards model offers an alternative to the Cox model when proportional hazards are questionable. Both models identified similar risk factors but yielded different estimates, suggesting combined use for comprehensive survival analysis.

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Area of Science:

Statistics
Biostatistics
Epidemiology

Background:

The Cox proportional hazards model is standard in survival analysis but relies on the proportional hazards assumption.
Violations of this assumption can impact the validity of Cox model results.
Additive hazards models provide an alternative when proportionality is uncertain.

Purpose of the Study:

To compare estimates from additive hazards regression models and the Cox model using emergency department visit data.
To assess the impact of the proportional hazards assumption on survival analysis outcomes.
To evaluate the utility of employing both additive and Cox models for a comprehensive understanding of risk factors.

Main Methods:

Application of additive hazards regression models to emergency department visit data.
Comparison of parameter estimates and covariate selection between additive and Cox models.
Evaluation of survival function estimates derived from both modeling approaches.

Main Results:

The Cox model produced higher estimates compared to the additive hazards model.
Despite differing estimates, both models identified similar significant covariates.
Survival functions estimated by both models were nearly identical.

Conclusions:

Additive hazards models are a valuable alternative when the proportional hazards assumption is violated.
The Cox and additive hazards models offer complementary insights into risk factor associations.
Utilizing both models concurrently enhances the comprehensive understanding of survival data and risk factor impact.