Finding purpose: Integrated latent profile and machine learning analyses identify purpose in life as an important predictor of high-functioning recovery after alcohol treatment.

Background: Recent investigations of recovery from alcohol use disorder (AUD) have distinguished subgroups of high and low functioning recovery in data from randomized controlled trials of behavioral treatments for AUD. Analyses considered various indicators of alcohol use, life satisfaction, and psychosocial functioning, and identified four recovery profiles from AUD three years following treatment.

Objectives: The present study integrates these profiles into a two-part machine learning framework, using recursive partitioning and random forests to distinguish a) clinical cut-points across 28 end-of-treatment biopsychosocial measurements that are predictive of high or low functioning recovery three years after treatment; and b) a rank-ordered list of the most salient variables for predicting individual membership in the high-functioning recovery sub-groups.

Methods: This secondary data analysis includes individuals (n = 809; 29.7% female) in the outpatient arm of Project MATCH who completed the end-of-treatment assessment and three-year follow-up batteries.

Results: Recursive partitioning found individuals with low depressive symptoms and less than 25% drinking days were more likely to be in a high functioning recovery profile (68%), whereas those with at least mild depressive symptoms and low purpose in life were more likely to be in a low functioning recovery profile (70%). Random forests identified purpose in life, social functioning, and depressive symptoms as the best predictors of recovery profiles.

Conclusions: Recovery profiles are best predicted by variables often considered of secondary interest. We demonstrate the utility of two machine learning approaches, highlighting how random forests can overcome recursive partitioning limitations.