Identifying Subgroups with Different Responses to HIV Risk Reduction Counseling

确定对艾滋病毒风险降低咨询有不同反应的亚组

• 批准号: 8798287
• 负责人: Daniel J Feaster
• 金额: $ 23.03万
• 依托单位: UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8798287
• 关键词: AIDS prevention; Accounting; Affect; Behavior; Behavioral; Biological; Characteristics; Classification; Clinic; Clinical Trials; Complex; Complication; Counseling; Data; Databases; Ensure; Funding; Future; HIV; HIV Infections; HIV risk; Heterogeneity; Human immunodeficiency virus test; Incidence; Individual; Intervention; Lead; Machine Learning; Methods; Minority Groups; Modeling; Modification; Outcome; Participant; Patients; Persons; Predictive Factor; Predictive Value; Prevention strategy; Preventive Intervention; Procedures; Property; Public Health; Randomized; Reaction; Relative (related person); Research; Risk; Risk Reduction; Sampling; Sexually Transmitted Diseases; Site; Stimulus; Subgroup; Techniques; Testing; Time; Treatment outcome; Trees; Twin Multiple Birth; Visit; Work; base; comparative effectiveness; cost; design; effectiveness trial; forest; group counseling; high risk; innovation; men who have sex with men; person centered; predictive modeling; public health relevance; response; simulation; success; treatment effect; treatment response; virtual

DESCRIPTION (provided by applicant): Aware, a large (n=5012) randomized comparative effectiveness trial, found that HIV risk reduction counseling for HIV negative individuals at the time of an HIV test did not have an impact on cumulative incidence of However, the question remains as to whether there are subgroups that would benefit from counseling. Further, understanding how counseling paradoxically increased STIs in MSM, the group most at risk for HIV in the US, and whether there are other subgroups who increased STIs is of public health importance. In recent innovations machine learning techniques have been used specifically to uncover subgroups with differential treatment responses in a fashion that is replicable and does not suffer model over-fitting associated with multiple testing. We will extend methods to explore treatment subgroups and differences across minority groups based on two of these approaches-Random Forests (RF), and Virtual Twins (VT). The VT approach uses random forests as a first step to create separate forest-based predictions of outcomes under both treatment and control conditions for each trial participant. Then a person-specific treatment effect is created for each individual and a tree-based prediction is made. Whereas this procedure has been shown to be very promising, there is a tendency for the procedure to have relatively low sensitivity, and low positive predictive value. These problems may be alleviated by replacing the single tree predictor of the individual-specific treatment effect by the random forest procedure and/or reweighting of the classification problem to equalize the number of treatment successes (STI incidence is far from affecting 50% of the sample). We will use simulations to uncover the optimal strategy and then use the optimal strategy to create a model that predicts likelihood of future STIs based on behaviors and likelihood of the individual having a positive or negative impact of counseling. We will also use an extension of this model to find the factors associated with the observed MSM by counseling interaction observed in Project Aware. The significance of this research lies in 1) determining if there are subgroups of STD clinic patients who would benefit from or be harmed by short HIV risk reduction counseling 2) providing a model to target these individuals or understanding why they may show increased STIs, and 3) providing the groundwork for use of these approaches to understanding the heterogeneous response to other HIV prevention interventions.

描述（由申请人提供）：意识到，一个大的（n = 5012）随机比较有效性试验发现，在艾滋病毒测试时，艾滋病毒负面个体的艾滋病毒风险降低咨询并没有影响累积发生率的累积发生率，但是问题仍然存在辅导中是否受益的亚组。此外，了解咨询方式如何在MSM中自相矛盾地增加了性传播感染，该组织在美国处于艾滋病毒的风险最高，以及其他亚组是否增加了性传播感染是公共卫生的重要性。在最近的创新中，机器学习技术已专门用于以可复制且不遭受与多次测试相关的模型过度拟合的方式发现具有差异治疗反应的亚组。我们将扩展基于这些方法随机森林（RF）和虚拟双胞胎（VT）的两种方法来探索少数群体的治疗亚组和差异的方法。 VT方法使用随机森林作为第一步，为每个试验参与者在治疗和控制条件下对结局的单独预测产生单独的预测。然后为每个人创建特定于人的治疗效果，并做出基于树的预测。尽管该过程已被证明是非常有希望的，但该过程的灵敏度相对较低，而阳性预测值较低。通过随机森林程序和/或分类问题的重新加权以均衡治疗成功的数量，可以通过更换单个特异性治疗效果的单个树预测来缓解这些问题（STI发病率远非影响样本的50％）。我们将使用仿真来揭示最佳策略，然后使用最佳策略来创建一个模型，该模型根据行为和个人对咨询的正面或负面影响的可能性预测未来性传播感染的可能性。我们还将使用该模型的扩展，通过在项目意识中观察到的咨询相互作用来找到与观察到的MSM相关的因素。这项研究的重要性在于1）确定是否有STD诊所患者的亚组会受益或受到短暂的HIV风险降低风险咨询的损害2）提供模型来针对这些人或理解为什么他们可能显示出增加的性传播感染，而3）为了解这些方法的基础，以了解这些方法对其他艾滋病毒的反应。