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Updated: May 24, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Bayesian influence measures for joint models for longitudinal and survival data.

Hongtu Zhu1, Joseph G Ibrahim, Yueh-Yun Chi

  • 1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7420, USA. hzhu@bios.unc.edu

Biometrics
|March 6, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces new Bayesian influence measures for joint models of longitudinal and survival data (JMLS). These methods help detect influential data points and assess model sensitivity in Bayesian JMLS analysis.

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Last Updated: May 24, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Bayesian Inference

Background:

  • Joint models for longitudinal and survival data (JMLS) are crucial for analyzing complex health outcomes.
  • Bayesian analysis offers a flexible framework for JMLS but requires careful assessment of model assumptions and data influence.
  • Identifying influential observations and assessing sensitivity are vital for robust JMLS inference.

Purpose of the Study:

  • To develop novel influence measures for perturbation analysis in Bayesian JMLS.
  • To quantify the impact of perturbations on data, priors, and sampling distributions within JMLS.
  • To enable the detection of outliers and evaluate the sensitivity of JMLS inferences to unverifiable assumptions.

Main Methods:

  • Introduction of a perturbation model to characterize individual and global perturbations.
  • Proposal of local influence measures to quantify perturbations in Bayesian JMLS.
  • Application of simulation studies and a real-world dataset to demonstrate the methods.

Main Results:

  • The developed influence measures effectively detect outliers and influential observations in JMLS.
  • The methods provide a quantitative assessment of sensitivity to various assumptions in Bayesian JMLS.
  • Demonstrated broad applicability of the proposed Bayesian influence methods through simulations and real data.

Conclusions:

  • The proposed Bayesian influence measures enhance the robustness and reliability of JMLS.
  • These methods are valuable tools for practitioners conducting sensitivity and outlier analysis in JMLS.
  • The study contributes to the rigorous application of Bayesian statistics in analyzing correlated longitudinal and survival data.