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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

Competing risks model for clustered data based on the subdistribution hazards with spatial random effects.

Somayeh Momenyan¹, Farzane Ahmadi², Jalal Poorolajal³

¹Department of Biostatistics, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Journal of Applied Statistics

|June 16, 2022

Summary

This study introduces Bayesian spatial survival models for clustered HIV/AIDS data with competing risks. The models improve parameter estimation and identify high-risk areas by incorporating spatial patterns.

Keywords:

Competing risks Markov chain Monte Carlo cumulative incidence function spatial random effect subdistribution hazard

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Area of Science:

Biostatistics
Epidemiology
Spatial Analysis

Background:

Clustered survival data often exhibit spatial patterns, such as in clinical centers or geographical regions.
Ignoring spatial variation can reduce parameter estimation accuracy and efficiency.
Competing risks are common in survival analysis, where multiple failure types exist but only the first is observed.

Purpose of the Study:

To develop and apply Bayesian subdistribution hazard regression models incorporating spatial random effects for clustered HIV/AIDS data.
To investigate spatial patterns of survivorship for identifying high-risk areas.
To improve the accuracy and efficiency of parameter estimation in survival analysis.

Main Methods:

Bayesian subdistribution hazard regression models were utilized.
An intrinsic conditional autoregressive (ICAR) distribution was employed to model areal spatial random effects.
Model comparison was performed using the deviance information criterion (DIC).

Main Results:

The proposed Bayesian spatial models demonstrated improved parameter estimation for clustered HIV/AIDS data.
The models effectively identified spatial patterns and high-risk areas.
Simulation studies and application to HIV/AIDS data confirmed the gains of the spatial approach.

Conclusions:

Bayesian subdistribution hazard models with spatial random effects are valuable for analyzing clustered survival data with competing risks.
Incorporating spatial variation enhances the understanding of disease patterns and risk factors.
The developed methodology provides a robust framework for epidemiological studies, particularly for HIV/AIDS surveillance.