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.
A recent large (n=5012) national study, Project Aware1, showed that on average there was no impact of brief risk reduction counseling on the likelihood of getting a new sexually transmitted infection (STI). This project will determine if there are subgroups of individuals who actually did benefit from brief risk reduction counseling and decreased their likelihood of a future STI as a result or subgroups that showed increases in STIs when counseled. If there are any subgroups with benefit of counseling evident in the data, this project will create a predictive model which could be used to identify individuals prospectively who would benefit from brief sexual risk reduction counseling when visiting an STD clinic; if there are subgroups with negative impact the project will help to understand why.
