Predictors of study dropout in cognitive-behavioural therapy with a trauma focus for post-traumatic stress disorder in adults: An individual participant data meta-analysis - Eirini Karyotaki; Pim Cuijpers; Sonya B Norman; Davide Papola; Marit Sijbrandij; Soraya Seedat; Miranda Olff; Sudie E Back; Richard Gray; Rolf J Kleber; Francisco Lotufo Neto; Jonathan Bisson; et al. (BMJ Mental Health)
Background Available empirical evidence on participant-level factors associated with dropout from psychotherapies for post-traumatic stress disorder (PTSD) is both limited and inconclusive. More comprehensive understanding of the various factors that contribute to study dropout from cognitive-behavioural therapy with a trauma focus (CBT-TF) is crucial for enhancing treatment outcomes.Objective Using an individual participant data meta-analysis (IPD-MA) design, we examined participant-level predictors of study dropout from CBT-TF interventions for PTSD.Methods A comprehensive systematic literature search was undertaken to identify randomised controlled trials comparing CBT-TF with waitlist control, treatment-as-usual or another therapy. Academic databases were screened from conception until 11 January 2021. Eligible interventions were required to be individual and in-person delivered. Participants were considered dropouts if they did not complete the post-treatment assessment.Findings The systematic literature search identified 81 eligible studies (n=3330). Data were pooled from 25 available CBT-TF studies comprising 823 participants. Overall, 221 (27%) of the 823 dropped out. Of 581 civilians, 133 (23%) dropped out, as did 75 (42%) of 178 military personnel/veterans. Bivariate and multivariate analyses indicated that military personnel/veterans (RR 2.37) had a significantly greater risk of dropout than civilians. Furthermore, the chance of dropping out significantly decreased with advancing age (continuous; RR 0.98).Conclusions These findings underscore the risk of premature termination from CBT-TF among younger adults and military veterans/personnel.Clinical implication Understanding predictors can inform the development of retention strategies tailored to at-risk subgroups, enhance engagement, improve adherence and yield better treatment outcomes.