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