Predicting individual responses to treatment for alcohol use disorder.

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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.