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Related Experiment Video

Updated: Sep 12, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Bayesian Expectile Joint Model With Varying Coefficient for Longitudinal and Semi-Competing Risks Data.

Feng Gu1, Jiaqing Chen1,2, Jinjing Wang1

  • 1College of Mathematics and Statistics, Wuhan University of Technology, Wuhan, China.

Statistics in Medicine
|August 7, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel joint model for analyzing longitudinal and semi-competing risks data in clinical research. The proposed expectile regression-based method enhances parameter estimation accuracy and reduces computational load for complex survival data.

Keywords:
Bayesian inferenceexpectile regressionlongitudinal datasemi‐competing riskstime‐varying coefficient

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Area of Science:

  • Biostatistics
  • Clinical Medical Research
  • Survival Analysis

Background:

  • Semi-competing risks data are common in clinical research but understudied in joint modeling.
  • Existing methods often lack flexibility for time-varying relationships in longitudinal and survival data.

Purpose of the Study:

  • To propose a flexible joint model for longitudinal and semi-competing risks data.
  • To incorporate time-varying coefficients using nonparametric functions for enhanced modeling.
  • To develop a robust Bayesian inference method for parameter estimation.

Main Methods:

  • Formulation of a linear mixed-effects longitudinal sub-model using expectile regression.
  • Development of a Cox proportional hazards survival sub-model within the semi-competing risks framework.
  • Integration of time-varying coefficients and nonparametric functions to link sub-models.
  • Application of simultaneous Bayesian inference for parameter estimation.

Main Results:

  • The proposed joint model effectively handles longitudinal and semi-competing risks data.
  • Bayesian inference method overcomes convergence issues and improves estimation accuracy.
  • Simulation studies confirm the model's robust performance.
  • Real-world data analysis demonstrates practical applicability.

Conclusions:

  • The novel joint model provides a flexible and accurate approach for analyzing complex clinical data.
  • The simultaneous Bayesian inference method offers computational advantages and reliable parameter estimation.
  • This methodology advances the analysis of semi-competing risks in longitudinal studies.