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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Bibliographic Details

Published in:	Journal of Consulting & Clinical Psychology Vol. 89; no. 10; pp. 869 - 885
Main Authors:	López-Castro, Teresa, Zhao, Yihong, Fitzpatrick, Skye, Ruglass, Lesia M., Hien, Denise A.
Format:	Article
Published:	American Psychological Association Oct2021
Subjects:	Substance abuse Post-traumatic stress disorder Machine learning Random forest algorithms Poisson regression
Online Access:	View this record in EBSCOhost