Trial designs for evaluating combination HIV prevention approaches. Issue 2 (22nd July 2020)

• Record Type: Journal Article
• Authors: Chen, Ying Qing; Peng, Lili; Wang, Yixin; Appelmans, Eline; Dasgupta, Sayan; Fleming, Thomas R.
• Is Part Of: HIV research & clinical practice. Volume 21:Issue 2/3(2020)
• Page Start: 72
• Page End: 82
• Publication Date: 2020-07-22
• Subjects: Efficacy trials -- factorial design -- HIV prevention research -- multi-component interaction -- simulations; HIV Infections -- therapy; Acquired Immunodeficiency Syndrome -- therapy; Clinical Trials as Topic; Clinical Medicine; AIDS (Disease) -- Treatment -- Periodicals; AIDS (Disease) -- Treatment; Periodicals; Periodical; 616.9792
• DOI: 10.1080/25787489.2020.1798083
• Languages: English
• ISSNs: 2578-7489

Abstract

Abstract : Background: Combination HIV prevention approaches that include both biomedical and non-biomedical interventions often hold greater promise to improve health outcomes and reduce the risk of HIV transmission. Objectives: Evaluate the relative properties of four leading candidate trial designs – 'single-factor', 'multi-arm', 'all-in-one', and 'factorial' designs – for assessing individual and/or combination prevention intervention approaches. Methods: Monte-Carlo simulations are conducted, assuming a putative combination approach could choose its components from two candidate biomedical interventions, i.e. Treatment-as-Prevention (TasP) and Pre-exposure Prophylaxis (PrEP), and three candidate behavioral interventions, i.e. linkage-to-care, counseling, and use of condoms. Various scenarios for individual components' effect sizes, their possible interaction, and the sample size based on real clinical studies are considered. Results: The all-in-one and factorial designs used to assess a combination approach and the multi-arm design used to assess multiple individual components are consistently more powerful than single-factor designs. The all-in-one design is powerful when the individual components are effective without negative interaction, while the factorial design is more consistently powerful across a broad array of settings. Conclusions: The multi-arm design is useful for evaluating single factor regimens, while the all-in-one and factorial designs are sensitive in assessing the overall efficacy when there is interest in combining individual component regimens anticipated to have complementary mechanisms. The factorial design is a preferred approach when assessing combination regimens due to its favorable power properties and since it is the only design providing direct insights about the contribution of individual components to the combination approach's overall efficacy and about potential interactions.