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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 case influence diagnostics for survival models.

Hyunsoon Cho1, Joseph G Ibrahim, Debajyoti Sinha

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

Biometrics
|April 22, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces Bayesian influence diagnostics for complex survival models, offering new methods to identify influential cases using Kullback-Leibler divergence. These diagnostics are computationally efficient with Markov chain Monte Carlo samples.

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Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • Complex survival models are widely used but sensitive to influential data points.
  • Existing diagnostic methods may not fully capture influence in Bayesian contexts.
  • Identifying influential cases is crucial for robust model interpretation.

Purpose of the Study:

  • To develop novel Bayesian case influence diagnostics for complex survival models.
  • To propose diagnostics based on Kullback-Leibler (K-L) divergence for joint and marginal posterior distributions.
  • To provide computationally efficient methods for assessing case influence.

Main Methods:

  • Development of case deletion influence diagnostics using K-L divergence.
  • Derivation of a simplified expression for K-L divergence computation.
  • Integration with Markov chain Monte Carlo (MCMC) sampling.
  • Application to the Cox model with a gamma process prior.

Main Results:

  • Proposed Bayesian diagnostics effectively identify influential cases.
  • A simplified computational approach using MCMC samples is presented.
  • Theoretical links between K-L divergence diagnostics and Cox's partial likelihood diagnostics are established.
  • Methodology demonstrated on simulated and real survival data.

Conclusions:

  • The proposed Bayesian diagnostics offer a robust approach for complex survival models.
  • The methods are computationally feasible and provide valuable insights into case influence.
  • These diagnostics enhance the reliability and interpretability of survival model analyses.