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Proportional Hazards Regression for Interval-Censored Outcomes With an Interval-Censored Covariate
Dongdong Li1, Yue Song2, Wenbin Lu3
1Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA.
Statistics in Medicine
|May 4, 2026
Summary
Achieving viral suppression faster during HIV treatment predicts a lower risk of viral rebound after stopping therapy. This finding is crucial for developing effective HIV cure strategies.
Area of Science:
- Infectious Diseases
- Biostatistics
- HIV Research
Background:
- Predicting viral rebound after antiretroviral therapy (ART) interruption is key for HIV cure research.
- Understanding factors influencing viral load dynamics post-ART is essential for patient management.
Purpose of the Study:
- To investigate if the time to achieve viral suppression after ART initiation predicts the time to viral rebound after ART interruption.
- To develop statistical methods for analyzing interval-censored outcomes and covariates in HIV studies.
Main Methods:
- Developed proportional hazards regression models for interval-censored outcomes and covariates.
- Extended methods to handle clustered repeated observations within individuals.
- Derived asymptotic properties and conducted simulation studies for performance evaluation.
Main Results:
- A longer time to achieve viral suppression during ART was associated with an increased hazard of viral rebound after ART interruption.
- The developed statistical methods were applied to real-world HIV patient data.
Conclusions:
- Time to viral suppression is a significant predictor of viral rebound dynamics post-ART interruption.
- The novel statistical approach provides a robust tool for analyzing complex HIV data with interval censoring.
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