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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| Published in: | The Journal of infectious diseases Vol. 226; no. 2; pp. 236 - 245 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
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Oxford University Press
24.08.2022
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| ISSN: | 0022-1899, 1537-6613, 1537-6613 |
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| Abstract | 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. |
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| AbstractList | 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. 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.BACKGROUNDAnalytical 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.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.METHODSUsing 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.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.RESULTSIn 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.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.CONCLUSIONSCurrent 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. 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. 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. 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. 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. |
| Author | Pinkevych, Mykola Davenport, Miles P Cromer, Deborah McMahon, James H Rasmussen, Thomas A Lewin, Sharon R Lau, Jillian S Y |
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| Copyright | The Author(s) 2022. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com. 2022 The Author(s) 2022. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com. |
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| Keywords | analytical treatment interruption HIV cure HIV infection modelling posttreatment controllers viral rebound |
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Background
Analytical treatment interruptions (ATI) are pauses of antiretroviral therapy (ART) in the context of human immunodeficiency virus (HIV)... Analytical treatment interruptions (ATI) are pauses of antiretroviral therapy (ART) in the context of human immunodeficiency virus (HIV) cure trials. They are... |
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| SubjectTerms | Anti-Retroviral Agents - therapeutic use Clinical Trials as Topic - methods HIV Infections - drug therapy Humans Research Design Risk Assessment Viral Load - statistics & numerical data Withholding Treatment |
| Title | Balancing Statistical Power and Risk in HIV Cure Clinical Trial Design |
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