Balancing Statistical Power and Risk in HIV Cure Clinical Trial Design

Jillian S. Y. Lau, Deborah Cromer, Mykola Pinkevych, Sharon R. Lewin, Thomas A. Rasmussen, James H. McMahon, Miles P. Davenport

Research output: Contribution to journalArticleResearchpeer-review

Abstract

BACKGROUND: Analytical treatment interruptions (ATI) are pauses of antiretroviral therapy (ART) in the context of HIV cure trials. They are the gold standard in determining if interventions being tested can achieve sustained virological control in the absence ART. However, withholding ART comes with risks and discomforts to the trial participant. We used mathematical models to explore how ATI study design can be improved to maximise statistical power, while minimising 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 the 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.
Original languageEnglish
Number of pages10
JournalThe Journal of Infectious Diseases
DOIs
Publication statusAccepted/In press - Feb 2022

Keywords

  • HIV cure HIV infection analytical treatment interruption modelling post treatment controllers viral rebound

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