Balancing Statistical Power and Risk in HIV Cure Clinical Trial Design

Abstract Background Analytical treatment interruptions (ATI) are pauses of antiretroviral therapy (ART) in the context of human immunodeficiency virus (HIV) cure trials. They are the gold standard in determining if interventions being tested can achieve sustained virological control in the absence o...

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Bibliographic Details
Published in:The Journal of infectious diseases 2022-08, Vol.226 (2), p.236-245
Main Authors: Lau, Jillian S Y, Cromer, Deborah, Pinkevych, Mykola, Lewin, Sharon R, Rasmussen, Thomas A, McMahon, James H, Davenport, Miles P
Format: Article
Language:English
Subjects:
Antiretroviral drugs
Antiretroviral therapy
Clinical trials
HIV
Human immunodeficiency virus
Major
Mathematical models
Medical research
Patient safety
Statistical analysis
Statistical power
Statistics
Stochasticity
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Summary:Abstract Background Analytical treatment interruptions (ATI) are pauses of antiretroviral therapy (ART) in the context of human immunodeficiency virus (HIV) cure trials. They are the gold standard in determining if interventions being tested can achieve sustained virological control in the absence of ART. However, withholding ART comes with risks and discomforts to trial participant. We used mathematical models to explore how ATI study design can be improved to maximize statistical power, while minimizing risks to participants. Methods Using previously observed dynamics of time to viral rebound (TVR) post-ATI, we modelled estimates for optimal sample size, frequency, and ATI duration required to detect a significant difference in the TVR between control and intervention groups. Groups were compared using a log-rank test, and analytical and stochastic techniques. Results In placebo-controlled TVR studies, 120 participants are required in each arm to detect 30% difference in frequency of viral reactivation at 80% power. There was little statistical advantage to measuring viral load more frequently than weekly, or interrupting ART beyond 5 weeks in a TVR study. Conclusions Current TVR HIV cure studies are underpowered to detect statistically significant changes in frequency of viral reactivation. Alternate study designs can improve the statistical power of ATI trials. Mathematical modelling was used to show how HIV cure study design can be improved to maximize statistical power, while minimizing risks to participants. Alternate study designs are also explored and discussed in the context of real-world HIV cure study conduct.
ISSN:0022-1899
1537-6613
DOI:10.1093/infdis/jiac032