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...

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Detalles Bibliográficos
Publicado en:Journal of Consulting & Clinical Psychology Vol. 89; no. 10; pp. 869 - 885
Autores principales: López-Castro, Teresa, Zhao, Yihong, Fitzpatrick, Skye, Ruglass, Lesia M., Hien, Denise A.
Formato: Artículo
Publicado: American Psychological Association Oct2021
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Acceso en línea:Ver este registro en EBSCOhost
Descripción
Sumario: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.