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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Bayesian analysis under accelerated failure time models with error-prone time-to-event outcomes.

Yanlin Tang1, Xinyuan Song2, Grace Yun Yi3

  • 1Key Laboratory of Advanced Theory and Application in Statistics and Data Science, MOE, School of Statistics, East China Normal University, Shanghai, China.

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|January 9, 2022
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Summary
This summary is machine-generated.

This study introduces new accelerated failure time (AFT) models to handle measurement errors in time-to-event data. The Bayesian approach with Markov chain Monte Carlo (MCMC) methods provides reliable statistical inference for these complex models.

Keywords:
AFT modelsBayesian inferenceError-prone outcomeMCMC methodsTime-to-event data

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Accelerated failure time (AFT) models are widely used for time-to-event data analysis.
  • Measurement errors in time-to-event outcomes can lead to biased statistical inference.
  • Existing AFT models often assume error-free time-to-event data.

Purpose of the Study:

  • To extend conventional AFT models to accommodate error-prone time-to-event outcomes.
  • To propose and evaluate two distinct measurement error models for time-to-event data.
  • To develop robust Bayesian statistical inference methods for these extended AFT models.

Main Methods:

  • Development of two measurement error models: a logarithm transformation regression model and an additive error model.
  • Application of Bayesian approaches for statistical inference.
  • Implementation of efficient Markov chain Monte Carlo (MCMC) algorithms for posterior inference.

Main Results:

  • The proposed Bayesian methods provide accurate and efficient statistical inference for AFT models with measurement error.
  • Simulation studies demonstrate the good performance of the developed methods under various scenarios.
  • The methodology is successfully applied to a real-world Alzheimer's disease study.

Conclusions:

  • The developed AFT models effectively address measurement errors in time-to-event outcomes.
  • Bayesian inference using MCMC algorithms is a viable approach for analyzing such models.
  • The proposed methods offer a valuable tool for researchers dealing with time-to-event data subject to measurement error, as exemplified by the Alzheimer's disease application.