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Joint bent-cable Tobit models for longitudinal and time-to-event data.

Getachew A Dagne¹

¹a Department of Epidemiology & Biostatistics , College of Public Health, University of South Florida , Tampa , Florida , USA.

Journal of Biopharmaceutical Statistics

|April 20, 2017

Summary

This study introduces a Bayesian method to estimate the transition period for antiretroviral (ARV) drug resistance in HIV/AIDS patients. The method analyzes viral load changes and time-to-event data, offering insights into treatment effectiveness.

Keywords:

Accelerated failure time model Bayesian inference piecewise model skew distribution survival analysis

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Area of Science:

Biostatistics
Epidemiology
Computational Biology

Background:

Antiretroviral (ARV) drug resistance is a significant challenge in HIV/AIDS management.
Longitudinal viral load data in HIV/AIDS patients often shows gradual shifts from treatment-induced decline to eventual increase.
Understanding the transition period of drug resistance is crucial for optimizing treatment strategies.

Purpose of the Study:

To develop a Bayesian statistical framework for estimating the transition period of antiretroviral drug resistance.
To jointly analyze time-to-event and longitudinal viral load data in HIV/AIDS patients.
To model the gradual changes in viral load trajectories associated with treatment response and resistance.

Main Methods:

Development of a joint bent-cable Tobit model for time-to-event and left-censored longitudinal data.
Incorporation of skewness and phasic developments in the model.
Utilization of random effects to capture stochastic dependence between viral load and time-to-event processes.

Main Results:

The proposed Bayesian method effectively estimates the transition period for antiretroviral drug resistance.
The joint model captures the gradual, non-abrupt changes in viral load trajectories observed in HIV/AIDS patients.
The method demonstrates its utility through application to real-world AIDS clinical study data.

Conclusions:

The developed Bayesian joint bent-cable Tobit model provides a robust approach for analyzing complex HIV/AIDS data.
This method enhances the understanding of antiretroviral drug resistance evolution and its impact on patient outcomes.
The findings support improved clinical decision-making in managing HIV/AIDS treatment and resistance.