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Variable Selection in Threshold Regression Model with Applications to HIV Drug Adherence Data.

Takumi Saegusa¹, Tianzhou Ma², Gang Li³

¹Department of Biostatistics, University of Maryland, College Park MD 20742.

Statistics in Biosciences

|April 2, 2021

Summary

This summary is machine-generated.

This study introduces a new variable selection method for threshold regression models, improving risk factor identification in complex health data. The enhanced method accurately selects variables for initial health and degradation speed, outperforming existing techniques.

Keywords:

HIV Survival Analysis Threshold regression Variable selection

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Area of Science:

Biostatistics
Survival Analysis
Statistical Modeling

Background:

The Cox proportional hazards model has limitations when its proportional hazards assumption is violated.
Threshold regression offers a flexible alternative, modeling distinct aspects of time-to-event data like initial status and degradation rate.
Identifying relevant risk factors for these distinct components is crucial for complex health models.

Purpose of the Study:

To extend the broken adaptive ridge (BAR) method for simultaneous variable selection in both regression functions of the threshold regression model.
To evaluate the performance of the proposed method against existing approaches.

Main Methods:

Extension of the broken adaptive ridge (BAR) method for simultaneous variable selection.
Theoretical establishment of variable selection consistency and asymptotic normality.
Simulation studies to compare performance.

Main Results:

The proposed BAR method achieves variable selection consistency and asymptotic normality for threshold regression.
Simulation results demonstrate superior performance compared to standard threshold regression and Akaike information criterion-based selection.
The method was successfully applied to identify risk factors for non-adherence in an HIV drug study.

Conclusions:

The extended BAR method provides a robust approach for variable selection in threshold regression.
This method enhances the identification of risk factors in complex time-to-event data, particularly when proportional hazards assumptions are not met.
The approach has practical utility, as shown in the HIV drug adherence study.