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Joint two-part Tobit models for longitudinal and time-to-event data.

Getachew A Dagne1

  • 1Department of Epidemiology and Biostatistics, College of Public Health, MDC 56, University of South Florida, Tampa, FL 33612, USA.

Statistics in Medicine
|August 11, 2017
PubMed
Summary

This study introduces Tobit models for analyzing left-censored data in joint time-to-event and longitudinal models. The method helps identify patient characteristics and assess AIDS progression risk using viral load and CD4/CD8 ratio data.

Keywords:
Bayesian inferenceaccelerated failure time modelsemiparametric modelskew distributionsurvival analysis

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Medical Data Analysis

Background:

  • Analyzing time-to-event and longitudinal data with left-censored outcomes presents challenges.
  • Existing separate analysis methods may be inadequate when outcomes are dependent and values are below detection limits.

Purpose of the Study:

  • To develop a robust method for jointly analyzing time-to-event and longitudinal data with left-censored outcomes.
  • To assess the association between CD4/CD8 ratio decline and viral load changes in AIDS progression.
  • To differentiate between patients progressing to AIDS and those who are not.

Main Methods:

  • Development of a joint model for time-to-event and a two-part longitudinal outcome, linked via random effects.
  • Implementation of a fully Bayesian approach for fitting joint two-part Tobit models.
  • Application of the proposed methods to simulated and real data from an AIDS clinical study.

Main Results:

  • Demonstrated the utility of Tobit models in handling left-censored outcomes in joint analysis.
  • Successfully assessed the association between CD4/CD8 ratio decline and viral load dynamics.
  • Showcased the model's ability to discriminate between AIDS progressors and non-progressors.

Conclusions:

  • The proposed joint two-part Tobit model provides an effective framework for analyzing complex biomedical data with censored outcomes.
  • This method enhances understanding of disease progression markers, such as viral load and CD4/CD8 ratio, in HIV/AIDS research.