Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Department of Biostatistics, University of Washington, Seattle, WA, USA.
HIV Res Clin Pract. 2020 Apr-Jun;21(2-3):72-82. doi: 10.1080/25787489.2020.1798083. Epub 2020 Jul 23.
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.
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.
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.
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.
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.
包含生物医学和非生物医学干预措施的组合 HIV 预防方法通常更有希望改善健康结果并降低 HIV 传播风险。
评估四种主要候选试验设计(“单因素”、“多臂”、“一体化”和“析因”设计)评估个体和/或组合预防干预措施的相对特性。
进行蒙特卡罗模拟,假设一种假定的组合方法可以从两种候选生物医学干预措施中选择其组成部分,即治疗即预防(TasP)和暴露前预防(PrEP),以及三种候选行为干预措施,即连接到护理、咨询和使用避孕套。考虑了个体成分效果大小的各种情况、它们可能的相互作用以及基于真实临床研究的样本量。
用于评估组合方法的一体化和析因设计以及用于评估多个个体成分的多臂设计始终比单因素设计更强大。当个体成分有效且没有负相互作用时,一体化设计具有强大的功能,而析因设计在广泛的环境中更一致地强大。
多臂设计可用于评估单因素方案,而一体化和析因设计在评估组合方案的整体疗效时很敏感,如果预期将具有互补机制的个体成分方案组合使用,则具有兴趣。由于析因设计具有有利的功效特性,并且是唯一提供关于个体成分对组合方案整体疗效的贡献以及关于潜在相互作用的直接见解的设计,因此它是评估组合方案的首选方法。