Seeing the Forest for the Trees: Predicting Attendance in Trials for Co-Occurring PTSD and Substance Use Disorders With a Machine Learning Approach.
Objective: High dropout rates are common in randomized clinical trials (RCTs) for comorbid posttraumatic stress disorder and substance use disorders (PTSD + SUD). Optimizing attendance is a priority for PTSD + SUD treatment development, yet research has found few consistent associations to guide res...
| Publicado en: | Journal of Consulting & Clinical Psychology Vol. 89; no. 10; pp. 869 - 885 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
| Publicado: |
American Psychological Association
Oct2021
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=161848885&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 161848885 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0022006X JCC jtl: Journal of Consulting & Clinical Psychology issn: 0022006X maglogo: N pubinfo: dt: Oct2021 vid: 89 iid: 10 pid: 34 pub: American Psychological Association artinfo: ui: 161848885 10.1037/ccp0000688 ppf: 869 ppct: 16 formats: tig: atl: Seeing the Forest for the Trees: Predicting Attendance in Trials for Co-Occurring PTSD and Substance Use Disorders With a Machine Learning Approach. aug: au: López-Castro, Teresa Zhao, Yihong Fitzpatrick, Skye Ruglass, Lesia M. Hien, Denise A. affil: Psychology Department, The City College of New York The Center of Alcohol and Substance Use Studies, Rutgers University – New Brunswick Department of Psychology, York University su: Substance abuse Post-traumatic stress disorder Machine learning Random forest algorithms Poisson regression sug: subj: Substance abuse Post-traumatic stress disorder Machine learning Random forest algorithms Poisson regression keyword: attendance machine learning PTSD randomized clinical trial substance use disorder attendance machine learning PTSD randomized clinical trial substance use disorder ab: Objective: High dropout rates are common in randomized clinical trials (RCTs) for comorbid posttraumatic stress disorder and substance use disorders (PTSD + SUD). Optimizing attendance is a priority for PTSD + SUD treatment development, yet research has found few consistent associations to guide responsive strategies. In this study, we employed a data-driven pipeline for identifying salient and reliable predictors of attendance. Method: In a novel application of the iterative Random Forest algorithm (iRF), we investigated the association of individual level characteristics and session attendance in a completed RCT for PTSD + SUD (n = 70; women = 22 [31.4%]). iRF identified a group of potential predictor candidates for the total trial sessions attended; then, a Poisson regression model assessed the association between the iRF-identified factors and attendance. As a validation set, a parallel regression of significant predictors was conducted on a second, independent RCT for PTSD + SUD (n = 60; women = 48 [80%]). Results: Two testable hypotheses were derived from iRF's variable importance measures. Faster within-treatment improvement of PTSD symptoms was associated with greater session attendance with age moderating this relationship (p =.01): faster PTSD symptom improvement predicted fewer sessions attended among younger patients and more sessions among older patients. Full-time employment was also associated with fewer sessions attended (p =.02). In the validation set, the interaction between age and speed of PTSD improvement was significant (p =.05) and the employment association was not. Conclusions: Results demonstrate the potential of data-driven methods to identifying meaningful predictors as well as the dynamic contribution of symptom change during treatment to understanding RCT attendance. What is the public health significance of this article?: This study suggests that attendance in treatments for co-occurring PTSD and substance use disorders may be affected by multiple factors, including how PTSD symptoms change during an intervention. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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