Investigating predictive factors of dialectical behavior therapy skills training efficacy for alcohol and concurrent substance use disorders: A machine learning study

Dialectical Behavior Therapy Skills Training (DBT-ST) as stand-alone treatment has demonstrated
promising outcomes for the treatment of alcohol use disorder (AUD) and concurrent substance use disorders
(SUDs). However, no studies have so far empirically investigated factors that might predict efficacy of this
therapeutic model.275 treatment-seeking individuals with AUD and other SUDs were consecutively admitted to a 3-month DBT-ST program (in- + outpatient; outpatient settings). The machine learning routine applied (i.e. penalized regression combined with a nested cross-validation procedure) was conducted in order to estimate predictive values of a wide panel of clinical variables in a single statistical framework on drop-out and substance-use behaviors, dealing with related multicollinearity, and eliminating redundant variables.
DOI: 10.1016/j.drugalcdep.2021.108723