Identifying Subgroups with Different Responses to HIV Risk Reduction Counseling

描述(由申请人提供)：AWARE是一项大型(n=5012)随机比较有效性试验，发现在艾滋病毒检测时为艾滋病毒阴性者提供艾滋病毒风险降低咨询对艾滋病毒的累积发病率没有影响。然而，问题仍然是是否有子组将从咨询中受益。此外，了解咨询如何矛盾地增加MSM中的STI，以及是否有其他亚群增加STI，对公共卫生具有重要意义。MSM是美国艾滋病毒风险最高的群体。在最近的创新中，机器学习技术被专门用来以一种可复制的方式发现具有不同治疗反应的亚组，并且不会遭受与多次测试相关的模型过度拟合。我们将基于其中两种方法--随机森林(RF)和虚拟双胞胎(VT)，扩展方法以探索治疗亚组和少数民族组之间的差异。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创建单独的基于森林的结果预测。然后，为每个个体创建特定于人的治疗效果，并进行基于树的预测。虽然这一过程已被证明是非常有希望的，但有一种趋势是该过程具有相对较低的敏感性和较低的阳性预测价值。这些问题可以通过用随机森林程序取代个体特定治疗效果的单一树预测因子和/或重新加权分类问题以使治疗成功的数量相等(STI发病率远远不会影响50%的样本)来缓解。我们将使用模拟来发现最优策略，然后使用最优策略创建一个模型，该模型基于行为和个体对咨询产生积极或负面影响的可能性来预测未来性传播感染的可能性。我们还将使用此模型的扩展，通过在Project Aware中观察到的咨询互动来找到与观察到的MSM相关的因素。这项研究的意义在于1)确定是否有STD门诊患者的亚群将从短期的艾滋病毒风险降低咨询中受益或受到伤害，2)提供一个针对这些人的模型或了解他们为什么可能表现出STI增加，以及3)为使用这些方法了解对其他艾滋病毒预防干预措施的不同反应提供基础。

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.