Seeing the trees despite the forest: applying recursive partitioning to the evaluation of drug treatment retention.
OBJECTIVE
The aim of this study is to demonstrate the utility of recursive partitioning (RP) for analyzing process and outcome data in drug treatment research. The basic methodology of RP is introduced and applied to the prediction of treatment retention.


METHODS
A total of 315 individuals randomly assigned to one of two treatment conditions; 289 (91.7%) completed a comprehensive baseline assessment battery. Treatment retention was assessed at a 52-week follow-up interview.


RESULTS
The RP approach was successful in generating a parsimonious decision tree that predicted drug treatment retention from the 195 input variables. Severity of drug use (as indicated by length of time speedballing), criminal behavior (as indicated by history of property crimes), level of insight, social network, and age at intake were predictive of treatment retention. The model is estimated to explain 32% of the variability in the population.


CONCLUSIONS
RP supports the notion that there are early indicators of treatment retention and that specific approaches that are tailored to individuals' needs will be potentially more successful in treatment engagement and retention than the typical "one size fits all" approach. The results also demonstrate the utility of RP for the detection of complex relationships between diverse and interdependent predictors.
Hellemann
Gerhard
G
SIStat, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA. ghellemann@mednet.ucla.edu
Conner
Bradley T
BT
Anglin
M Douglas
MD
Longshore
Douglas
D
D000328:Adult; D000368:Aged; D003415:Crime / Q000706:statistics & numerical data; D003625:Data Collection; D003627:Data Interpretation, Statistical; D003663:Decision Trees; D005500:Follow-Up Studies; D005544:Forecasting; D006556:Heroin Dependence / Q000523:psychology / Q000534:rehabilitation*; D006801:Humans; D008875:Middle Aged; D015233:Models, Statistical; D009294:Narcotics / Q000627:therapeutic use; D010043:Outcome and Process Assessment, Health Care / Q000379:methods; D010352:Patient Dropouts / Q000523:psychology / Q000706:statistics & numerical data; D055502:Secondary Prevention; D012720:Severity of Illness Index; D012944:Social Support; D013997:Time Factors; D016896:Treatment Outcome; D055815:Young Adult
D009294:Narcotics