Estimating Mediation and Moderation Effects in HIV Incidence Prevention Trials

项目摘要/摘要 该项目的总体目标是分析旨在降低艾滋病毒发病率的现有行为干预措施 为了检查预测疗效的机制(即，介体)，以及对哪些子组(即， 主持人)干预是有效的。我们建议使用高级方法来分析调解人 和版主。具体地说，我们将使用允许直接测试间接(中介)影响的方法 并量化，即使在没有发现显著总X？Y效应(即有效性)的干预中也是如此。我们建议 分析来自四个大规模随机对照试验(RCT)的数据，以测试行为疗法的有效性 对艾滋病毒发病率结果的干预；两项试验显示对艾滋病毒有显著影响，两项试验无效 关于艾滋病毒的结果。我们将使用一种名为“条件过程建模”的新方法，它同时 建立调解人和主持人的模型，并测试干预如何以及对谁有效，帮助确定 不同的过程导致不同亚组的疗效(目标1)。我们将比较有意义的和 在其重要的调解人和主持人和模型特征中进行无效试验以检查可能的 无效试验中的掩蔽疗效情况(AIM 2)。我们将在因果关系方面利用尖端技术 调解分析。具体地说，我们将进行敏感性分析，以估计调解人在多大程度上 主要负责对艾滋病毒产生影响，加强对介体的因果推断，从而导致 在干预措施中减少艾滋病毒传播(目标3)。据我们所知，这些新的统计方法 从未被用来分析艾滋病毒干预随机对照试验中的调解人和主持人。我们还将探索 使用生物医学策略作为中介途径的调节器来探索那些报告了 生物医学工具的使用显示了干预效果的不同因果过程(目标4)。理解 现有行为艾滋病毒预防干预措施的调解人和主持人将有助于了解如何削减 减少或增加干预措施以达到其最有效的元素，以及哪些子组和背景必须 旨在产生更简短、更便宜、更有影响力的干预措施。这些干预措施可以用于 与生物医学预防策略相结合。还通过分析生物医学工具(即nPEP的使用和 男性包皮环切术状态)作为中介效应的潜在调节因素，我们获得了有价值的洞察 这些特定的生物医学策略在干预中必须针对哪些行为机制 都被使用过。为了达到我们的目标，我们将使用“黄金标准”评估方法分析研究数据。 艾滋病毒预防干预措施。具体地说，我们将使用来自四个不同的双臂RCT的数据 有能力测试艾滋病毒发病率作为主要结果。这些随机对照试验测试了行为HIV的有效性。 美国男男性行为者、南非和南非男性和女性的预防干预 乌干达，以及乌克兰注射毒品的人。因此，目前的提案将分析四个独特的 来自全球一些受艾滋病毒影响最严重的人群的干预数据。

Project Summary/Abstract The overall goal of this project is to analyze existing behavioral interventions designed to reduce HIV incidence in order to examine the mechanisms (i.e., mediators) that predict efficacy, and the subgroups for whom (i.e., moderators) interventions are efficacious. We propose to use advanced approaches to analyzing mediators and moderators. Specifically, we will utilize methods that allow indirect (mediated) effects to be directly tested and quantified, even in interventions that do not find a significant total X ÞY effect (i.e., efficacy). We propose to analyze data from four large-scale randomized controlled trials (RCTs) testing the efficacy of behavioral interventions on an HIV incidence outcome; two trials were showed a significant effect on HIV, two had null results on HIV. We will use the novel approach called “conditional process modeling” which simultaneously models mediators and moderators and tests both how and for whom an intervention works, helping to identify different processes leading to efficacy for different subgroups (AIM 1). We will compare the significant and the null trials in their significant mediators and moderators and model characteristics to examine possible situations of masked efficacy in the null trials (AIM 2). We will utilize cutting-edge advancements in causal mediation analysis. Specifically, we will conduct sensitivity analysis to estimate the extent to which a mediator is primarily responsible for producing effects on HIV, strengthening causal inference about mediators leading to reduced HIV transmission in the interventions (AIM 3). To our knowledge these novel statistical approaches have never been used to analyze mediators and moderators in HIV intervention RCTs. We will also explore the use of biomedical strategies as moderators of mediated pathways to explore whether participants who reported use of a biomedical tool show a different causal process in intervention efficacy (AIM 4). Understanding mediators and moderators of existing behavioral HIV prevention interventions will help to inform how to pare down or ramp up interventions to their most effective elements and which subgroups and contexts must be targeted to produce briefer, cheaper, and more impactful interventions. These interventions could be used in combination with biomedical prevention strategies.!By also analyzing biomedical tools (i.e., nPEP use and male circumcision status) as potential moderators of mediated effects, we gain valuable insight into whether and which behavioral mechanisms must be targeted in interventions when these specific biomedical strategies are used. To meet our aims we will analyze data from studies using the “gold-standard” method for evaluating HIV prevention interventions. Specifically, we will use data from four different two-arm RCTs that were powered to test HIV incidence as the primary outcome. These RCTs tested the efficacy of behavioral HIV prevention interventions among men who have sex with men in the U.S., men and women in South Africa and Uganda, and people who inject drugs in Ukraine. Thus, the current proposal will analyze four unique intervention datasets from some of the most HIV-affected populations globally.