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dc.contributor.advisorKendall, Philip C.
dc.contributor.advisorOlino, Thomas
dc.creatorNorris, Lesley
dc.date.accessioned2023-09-03T14:40:08Z
dc.date.available2023-09-03T14:40:08Z
dc.date.issued2023
dc.identifier.urihttp://hdl.handle.net/20.500.12613/8880
dc.description.abstractBackground: Efficacious treatments for youth anxiety disorders exist, but there is considerable heterogeneity in outcome. Predictors and moderators of differential treatment response have been difficult to identify. Machine Learning (ML) is a promising approach. Methods: Data from nine randomized controlled trials (RCTs) of youth anxiety treatments were harmonized into a dataset (N = 1362; Mage = 10.59, SDage = 2.47; 48.9% female; 71.9% White, 5.9% Black, 1.0% Asian; 10.8% Hispanic) and supervised ML algorithms predicted treatment outcomes. ML models were also built separately for youth who completed individual cognitive behavioral therapy (CBT), family CBT, combination of sertraline (SRT) and CBT, and SRT alone to examine predictive features. Models were then externally validated in a research clinic providing CBT for youth anxiety (N = 50; Mage = 12.04, SDage = 3.22; 56% female; 76% Caucasian, 10% Black, 6 % Asian, 2% Other; 6% Hispanic). Results: Lasso Regression emerged as the best performing model (RMSE = 1.40), with comparable RMSEs when the same approach was applied within an external dataset (RMSE = 1.40). Predictive features of poorer outcomes were primarily indicators of increased symptom severity, particularly youth depressive symptom severity, although predictors varied within subsamples (e.g., caregiver psychopathology was an important predictor for FCBT; increased somatic symptoms were important predictors for better response to SRT). Discussion: ML helped identify features of anxious youth who will respond to treatments.
dc.format.extent75 pages
dc.language.isoeng
dc.publisherTemple University. Libraries
dc.relation.ispartofTheses and Dissertations
dc.rightsIN COPYRIGHT- This Rights Statement can be used for an Item that is in copyright. Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available.
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectClinical psychology
dc.titleUsing Machine Learning Methods to Predict Treatment Outcome for Anxious Youth
dc.typeText
dc.type.genreThesis/Dissertation
dc.contributor.committeememberHeimberg, Richard G.
dc.contributor.committeememberAlloy, Lauren B.
dc.contributor.committeememberJarcho, Johanna
dc.contributor.committeememberGosch, Elizabeth A.
dc.description.departmentPsychology
dc.relation.doihttp://dx.doi.org/10.34944/dspace/8844
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.description.degreePh.D.
dc.identifier.proqst14913
dc.date.updated2023-08-24T16:08:00Z
refterms.dateFOA2023-09-03T14:40:09Z
dc.identifier.filenameNorris_temple_0225E_14913.pdf


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