Abstract
ABSTRACT Background: Intensive PTSD treatment programs (ITPs) are highly effective but tend to differ greatly in length and the number of adjunctive services that are provided in conjunction with evidence-based PTSD treatments. Individuals’ treatment response to more or less comprehensive ITPs is poorly understood. Objective: To apply a machine learning-based decision-making model (the Personalized Advantage Index (PAI)), using clinical and demographic factors to predict response to more or less comprehensive ITPs. Methods: The PAI was developed and tested on a sample of 747 veterans with PTSD who completed a 3-week (more comprehensive; n = 360) or 2-week (less comprehensive; n = 387) ITP. Results: Approximately 12.32% of the sample had a PAI value that suggests that individuals would have experienced greater PTSD symptom change (5 points) on the PTSD Checklist for DSM-5 in either a more- or less comprehensive ITP. For individuals with the highest 25% of PAI values, effect sizes for the amount of PTSD symptom change between those in their optimal vs. non-optimal programs was d = 0.35. Conclusions: Although a minority was predicted to have benefited more from a program, there generally was not a substantial difference in predicted outcomes. Less comprehensive and thus more financially sustainable ITPs appear to work well for most individuals with PTSD.
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