BACKGROUND: After traumatic events, such as disaster, war trauma, and injuries including burns (which is the focus here), the risk to develop posttraumatic stress disorder (PTSD) is approximately 10% (Breslau & Davis, 1992). Latent Growth Mixture Modeling can be used to classify individuals into distinct groups exhibiting different patterns of PTSD (Galatzer-Levy, 2015). Currently, empirical evidence points to four distinct trajectories of PTSD patterns in those who have experienced burn trauma. These trajectories are labeled as: resilient, recovery, chronic, and delayed onset trajectories (e.g., Bonanno, 2004; Bonanno, Brewin, Kaniasty, & Greca, 2010; Maercker, Gäbler, ONeil, Schützwohl, & Müller, 2013; Pietrzak et al., 2013). The delayed onset trajectory affects only a small group of individuals, that is, about 4-5% (ODonnell, Elliott, Lau, & Creamer, 2007). In addition to its low frequency, the later onset of this trajectory may contribute to the fact that these individuals can be easily overlooked by professionals. In this special symposium on Estimating PTSD trajectories (Van de Schoot, 2015a), we illustrate how to properly identify this small group of individuals through the Bayesian estimation framework using previous knowledge through priors (see, e.g., Depaoli & Boyajian, 2014; Van de Schoot, Broere, Perryck, Zondervan-Zwijnenburg, & Van Loey, 2015). METHOD: We used latent growth mixture modeling (LGMM) (Van de Schoot, 2015b) to estimate PTSD trajectories across 4 years that followed a traumatic burn. We demonstrate and compare results from traditional (maximum likelihood) and Bayesian estimation using priors (see, Depaoli, 2012, 2013). Further, we discuss where priors come from and how to define them in the estimation process. RESULTS: We demonstrate that only the Bayesian approach results in the desired theory-driven solution of PTSD trajectories. Since the priors are chosen subjectively, we also present a sensitivity analysis of the Bayesian results to illustrate how to check the impact of the prior knowledge integrated into the model. CONCLUSIONS: We conclude with recommendations and guidelines for researchers looking to implement theory-driven LGMM, and we tailor this discussion to the context of PTSD research.

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.