Project summary:
Treatment of alcohol use disorder (AUD) is characterized by common relapse, heterogeneity in findings, and
many diverse interventions which show modest efficacy but fail to out perform each other. Research aiming to
explain the existing heterogeneity has found many significant moderators of treatment effects but few of these
have effect sizes large enough to indicate that they should be used in clinical practice for targeting treatments.
New personalized medicine methods which use machine learning algorithms to create predictions of
responses to AUD treatment which take into account multiple predictors show early promise. This research
This research uses data from 11 randomized clinical trials, 6 of behavioral relapse prevention programs and 5
of pharmacological interventions to reduce heavy drinking, to develop and cross validate individual predictions
of treatment effects on heavy drinking. We will also test the significance of individual differences for each
intervention and provide predictive intervals for individuals describing their expected response to different
interventions. The study also aims to test new approaches for combining data across multiple trials and for
improving precision of predictions in order to make the use of the predicted individual treatment effects (PITEs)
framework more useful in clinical practice.
At the end of this study there will be published algorithms for comparing predictions of treatment effects for
new individuals across multiple treatments, predictive intervals for those effects, and an assessment of internal
and, where possible, external validation of those predictions. The work emphasizes replicability of results
through cross-validation (which will itself be tested with simulations), a priori specification of predictive methods
and covariates, and use of an expert panel to make theory and literature informed decisions. This research is
designed to make personalized medicine for treatment of AUD usable in clinical practice through its integration
of theory, clinical experience brought by the clinical advisory board, and clear communication of results to a
clinical audience.