Predictors of treatment response in a web-based intervention for cannabis users

Background: Trials demonstrate the effectiveness of web-based interventions for cannabis-related disorders. For further development of these interventions, it is of vital interest to identify user characteristics which predict treatment response. Methods: Data from a randomized factorial trial on a web-based intervention for cannabis-users (n = 534) was reanalyzed. As potential predictors for later treatment response, 31 variables from the following categories were tested: socio-demographics, substance use and cognitive processing. The association of predictors and treatment outcome was analyzed using unbiased recursive partitioning and represented as classification tree. Predictive performance of the tree was assessed by comparing its cross-validated results to models derived with all-subsets logistic regression and random forest. Results: Goal commitment (p < .001), the extent of self-reflection (p < .001), the preferred effect of cannabis (p = .005) and initial cannabis use (p = .015) significantly differentiate between successful and non-successful participants in all three analysis methods. The predictive accuracy of all three models is comparable and modest. Conclusions: Participants who commit to quit using cannabis, who at least have moderate levels of self-reflection and who prefer mild intoxicating effects were most likely to respond to treatment. To predict treatment response on an individual level, the classification tree should only be used as one of several sources of information.