Applying methods for personalized medicine to the treatment of alcohol use disorder.

OBJECTIVE

Numerous behavioral treatments for alcohol use disorder (AUD) are effective, but there are substantial individual differences in treatment response. This study examines the potential use of new methods for personalized medicine to test for individual differences in the effects of cognitive behavioral therapy (CBT) versus motivational enhancement therapy (MET) and to provide predictions of which will work best for individuals with AUD. We highlight both the potential contribution and the limitations of these methods.

METHOD

We performed secondary analyses of abstinence among 1,144 participants with AUD participating in either outpatient or aftercare treatment who were randomized to receive either CBT or MET in Project MATCH. We first obtained predicted individual treatment effects (PITEs), as a function of 19 baseline client characteristics identified a priori by MATCH investigators. Then, we tested for the significance of individual differences and examined the predicted individual differences in abstinence 1 year following treatment. Predictive intervals were estimated for each individual to determine if they were 80% more likely to achieve abstinence in one treatment versus the other.

RESULTS

Results indicated that individual differences in the likelihood of abstinence at 1 year following treatment were significant for those in the outpatient sample, but not for those in the aftercare sample. Individual predictive intervals showed that 37% had a better chance of abstinence with CBT than MET, and 16% had a better chance of abstinence with MET. Obtaining predictions for a new individual is demonstrated.

CONCLUSIONS

Personalized medicine methods, and PITE in particular, have the potential to identify individuals most likely to benefit from one versus another intervention. New personalized medicine methods play an important role in putting together differential effects due to previously identified variables into one prediction designed to be useful to clinicians and clients choosing between treatment options. (PsycInfo Database Record (c) 2021 APA, all rights reserved).