Aims
The purpose of this study was to explore the selection of predictor
variables in the evaluation of drug treatment completion using an ensemble
approach with classification trees. The basic methodology is reviewed and
the subagging procedure of random subsampling is applied.
Methods
Among 234 individuals with stimulant use disorders randomized to a
12-Step facilitative intervention shown to increase stimulant use
abstinence, 67.52% were classified as treatment completers. A total of 122
baseline variables were used to identify factors associated with
completion.
Findings
The number of types of self-help activity involvement prior to
treatment was the predominant predictor. Other effective predictors included
better coping self-efficacy for substance use in high-risk situations, more
days of prior meeting attendance, greater acceptance of the Disease model,
higher confidence for not resuming use following discharge, lower ASI Drug
and Alcohol composite scores, negative urine screens for cocaine or
marijuana, and fewer employment problems.
Conclusions
The application of an ensemble subsampling regression tree method
utilizes the fact that classification trees are unstable but, on average,
produce an improved prediction of the completion of drug abuse treatment.
The results support the notion there are early indicators of treatment
completion that may allow for modification of approaches more tailored to
fitting the needs of individuals and potentially provide more successful
treatment engagement and improved outcomes.