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Jointly modeling time-to-event and longitudinal data: A Bayesian approach.

Yangxin Huang1, X Joan Hu2, Getachew A Dagne1

  • 1Department of Epidemiology & Biostatistics, College of Public Health, MDC 56, University of South Florida, Tampa, FL 33612, USA, Tel.: +1-813-9748209.

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Summary

This study introduces flexible Bayesian joint models for event times and longitudinal data, addressing non-normal responses and measurement errors. Findings reveal CD4 counts influence viral decay but not CD4/CD8 ratio decrease timing.

Keywords:
Accelerated failure time modelDirichlet processSemiparametric linear/nonlinear mixed-effects modelSkew-elliptical distributionTime-to-event

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Clinical Trials

Background:

  • Longitudinal data and event times are common in clinical trials.
  • Standard models often assume normality and parametric forms, which may not hold.
  • Measurement errors in longitudinal data can bias results.

Purpose of the Study:

  • To develop flexible Bayesian joint models for event times and longitudinal measures.
  • To accommodate non-normal longitudinal responses and measurement errors.
  • To provide a robust framework for analyzing complex clinical trial data.

Main Methods:

  • Bayesian joint modeling approach.
  • Incorporation of skew-distributed longitudinal responses with measurement errors.
  • Nonparametric prior distribution for time-to-event variable.
  • Simultaneous posterior distribution estimation for inference.

Main Results:

  • The time-varying CD4 count is significantly associated with the initial viral decay rate.
  • The time to CD4/CD8 ratio decrease showed no strong association with viral decay rates or CD4 changing rate.
  • The developed methodology was illustrated using an AIDS clinical trial dataset.

Conclusions:

  • The Bayesian joint models offer a flexible approach for analyzing longitudinal and event time data with non-normalities and measurement errors.
  • Findings provide insights into the complex interplay between virological and immunological responses to antiretroviral therapy.
  • The study highlights the importance of considering covariate effects on different phases of disease progression.