Advancing Personalized Medicine: Application of a Novel Statistical Method to Identify Treatment Moderators in the Coordinated Anxiety Learning and Management Study.

ABSTRACT

There has been increasing recognition of the value of personalized medicine where the most effective treatment is selected based on individual characteristics. This study used a new method to identify a composite moderator of response to evidence-based anxiety treatment (CALM) compared to Usual Care. Eight hundred seventy-six patients diagnosed with one or multiple anxiety disorders were assigned to CALM or Usual Care. Using the method proposed by Kraemer (2013), 35 possible moderators were examined for individual effect sizes then entered into a forward-stepwise regression model predicting differential treatment response. K-fold cross validation was used to identify the number of variables to include in the final moderator. Ten variables were selected for a final composite moderator. The composite moderator effect size (r = .20) was twice as large as the strongest individual moderator effect size (r = .10). Although on average patients benefitted more from CALM, 19% of patients had equal or greater treatment response in Usual Care. The effect size for the CALM intervention increased from d = .34 to d = .54 when accounting for the moderator. Findings support the utility of composite moderators. Results were used to develop a program that allows mental health professionals to prescribe treatment for anxiety based on baseline characteristics (http//anxiety.psych.ucla.edu/treatmatch.html).