Clusters Based on Within-Treatment Symptom Trajectories as Predictors of Dropout in Treatment for Posttraumatic Stress Disorder and Substance Use Disorder

Abstract

Objective: Dropout rates are high in treatments for co-occurring posttraumatic stress disorder (PTSD) and substance use disorders (SUDs). We examined dropout predictors in PTSD-SUD treatment. Methods: Participants were 183 veterans receiving integrated or phased motivational enhancement therapy and prolonged exposure. Using survival models, we examined demographics and symptom trajectories as dropout predictors. Using latent trajectory analysis, we incorporated clusters based on symptom trajectories to improve dropout prediction. Results: Hispanic ethnicity (integrated arm), Black or African American race (phased arm), and younger age (phased arm) predicted dropout. Clusters based on PTSD and substance use trajectories improved dropout prediction. In integrated treatment, participants with consistently-high use and low-and-improving use had the highest dropout. In phased treatment, participants with the highest and lowest PTSD symptoms had lower dropout; participants with the lowest substance use had higher dropout. Conclusions: Identifying within-treatment symptom trajectories associated with dropout can help clinicians intervene to maximize outcomes. ClinicalTrials.gov Identifier: NCT01211106.

Keywords:

• Posttraumatic stress disorder
• substance use disorder
• therapy process
• dropout
• latent trajectory analysis
• bayesian statistics

Disclosure statement

The authors report there are no competing interests to declare.

Data availability statement

Data from this study are available by emailing the secondary corresponding author and completion of VA regulatory requirements for data sharing; analysis code is available by emailing the primary or secondary corresponding author.

Additional information

Funding

This work was funded by the United States Department of Veterans Affairs, Clinical Science Research & Development under Merit Review Award ZDA1-03-W10 to David W. Oslin, Ph.D.