PROJECT SUMMARY/ABSTRACT Anxiety disorders in youth are highly prevalent [1] and impairing [2-4]. Left untreated, these disorders confer substantial additional risk for the development of a wide range of negative sequelae, including substance use [5], suicidal ideation and attempts [6], and additional mental health comorbidities [7]. Although several treatments have demonstrated efficacy for anxiety in youth, including individual cognitive behavioral therapy (ICBT), family CBT (FCBT), medication (MED), and combination of CBT and medication (COMB) [8], a meaningful portion of youth are classified as non-responders after a full course of treatment [9]. The identification of baseline predictors and moderators of response is critical to improve treatment efficacy and reduce burden on families. Increased anxiety severity, comorbidity (behavioral problems, depression), and family psychopathology, along with older age, female gender and anxiety diagnosis, have been highlighted as potential predictors and moderators of outcome. However, studies have been underpowered and findings are inconsistent [10]. To date, all studies have taken a traditional analytic approach, which typically provides conservative estimates as a result of imposed explanatory constraints [11]. Machine Learning (ML) represents a promising complementary statistical technique to traditional analyses, given its focus on predictive fit rather than explanatory inference [12] and will facilitate identification of non-linear, complex patterns of predictors and moderators at the individual level [13, 14]. These methods have shown promise in identification of treatment outcome predictors in other medical [e.g., 15-18] and psychiatric samples [e.g., 19-22], but to date have not been implemented in a sample of anxious youth. The proposed project will aggregate datasets from at minimum ten peer-reviewed and published randomized controlled trials (N=1444) and train and validate two models along overlapping features, including (1) demographics, (2) diagnosis, (3) anxiety severity (4) behavioral problems, and (5) family psychopathology. Models will also be used to examine differential response to ICBT, FCBT, MED and COMB. Aggregated data will be uploaded into a centralized dataset, in line with the NIMH RDoC db and NDAR [23] datasets, and then used to predict outcome for individual anxious youth (N=80) completing ICBT and COMB at the Child and Adolescent Anxiety Disorders Clinic at Temple. The aims of this study are consistent with calls issued in the NIMH strategic plan (Objective 3) and will help facilitate the development of person-centered interventions for anxious youth [24]. An individualized approach to treatment is important to further increase treatment efficacy and reduce the financial and emotional burden associated with non-response [25, 26]. A training plan has been designed that consists of mentorship, formal classwork and experiential learning to develop the applicant's expertise in ...