HIV treatment as prevention: considerations in the design, conduct, and analysis of cluster randomized controlled trials of combination HIV prevention.
HIV treatment as prevention: considerations in the design, conduct, and analysis of cluster randomized controlled trials of combination HIV prevention.
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DOI：
10.1371/journal.pmed.1001250
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发表时间：
2012
期刊：
影响因子：
15.8
通讯作者：
Hallett TB
中科院分区：
文献类型：
--
作者：
Boily MC;Mâsse B;Alsallaq R;Padian NS;Eaton JW;Vesga JF;Hallett TB
严格评价艾滋病毒综合预防措施在人口一级的影响，对于艾滋病毒预防工作的未来至关重要。在这篇综述中，我们讨论了重要的考虑因素的设计和解释的群集随机对照试验（C-RCT）的组合预防干预措施。我们重点关注三个大型的C-RCT，将很快开始，旨在测试这一假设，即组合预防包，包括扩大获得抗逆转录病毒治疗，可以大大降低艾滋病毒的发病率。使用通用框架将数学建模分析整合到C-RCT的设计、实施和分析中，将补充传统的统计分析，并加强干预措施的评价。重要的是，即使采用联合干预措施，在C-RCT的2至3年期间大幅降低艾滋病毒发病率也可能具有挑战性，除非迅速扩大干预措施并覆盖关键人群。因此，我们建议创新地使用数学建模进行中期分析，当中期艾滋病毒发病率数据不可用时，允许正在进行的试验进行修改或调整，以减少不确定结果的可能性。在C-RCT期间预先计划的数学模型的交互式使用也将为验证和完善模型预测提供宝贵的机会。
The rigorous evaluation of the impact of combination HIV prevention packages at the population level will be critical for the future of HIV prevention. In this review, we discuss important considerations for the design and interpretation of cluster randomized controlled trials (C-RCTs) of combination prevention interventions. We focus on three large C-RCTs that will start soon and are designed to test the hypothesis that combination prevention packages, including expanded access to antiretroviral therapy, can substantially reduce HIV incidence. Using a general framework to integrate mathematical modelling analysis into the design, conduct, and analysis of C-RCTs will complement traditional statistical analyses and strengthen the evaluation of the interventions. Importantly, even with combination interventions, it may be challenging to substantially reduce HIV incidence over the 2- to 3-y duration of a C-RCT, unless interventions are scaled up rapidly and key populations are reached. Thus, we propose the innovative use of mathematical modelling to conduct interim analyses, when interim HIV incidence data are not available, to allow the ongoing trials to be modified or adapted to reduce the likelihood of inconclusive outcomes. The preplanned, interactive use of mathematical models during C-RCTs will also provide a valuable opportunity to validate and refine model projections.
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