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

Abstract

Objective 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. Method 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), thirty-five 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. Results 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. Conclusions 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).

Cognitive and Interpersonal Vulnerabilities to Adolescent Depression: Classification of Risk Profiles for a Personalized Prevention Approach

“…These intriguing results demonstrate that treatment outcomes can be improved by matching individuals to the intervention that would have best suited the individual based on their risk profile as determined by the PAI. In addition, other new machine learning and novel statistical methods are being developed and applied to create and examine evidence-based risk assessment to predict later depression using big data (Goldstein, Navar, Pencina, & Ioannidis, 2017; Iniesta, Stahl, & McGuffin, 2016; Kessler et al, 2016; Niles et al, 2017). We do not see the PAI, alternative machine learning methods, or our approach as opposing or contradictory solutions to tackling the significant problem of identifying risk relevant for individualizing interventions via evidence-based assessment.…”

Psychotherapy or medication for depression? Using individual symptom meta-analyses to derive a Symptom-Oriented Therapy (SOrT) metric for a personalised psychiatry

“…Attempts have been made and are underway towards establishing such personalised psychiatric care. These cover a wide range of different approaches such as using big data sets and machine learning models to predict the MDD course from baseline self-reports [19,20], explorative data-mining strategies in order to define decision trees for the treatment of depression [21], algorithmbased treatments associated with shorter treatment time [15], imaging-based functional connectivity indices for treatment selection [22], or statistical strategies to examine superiority between treatments depending on stratification variables [23,24]. Among the latter is a promising attempt by DeRubeis and colleagues who developed the Personalised Advantage Index (PAI) by re-analysing data from a randomised controlled trial (RCT) of cognitive behavioural therapy (CBT) versus ADM [25].…”

Personalized Medicine and Cognitive Behavioral Therapies for Depression: Small Effects, Big Problems, and Bigger Data

“…Our read of the literature suggests that many of the problems we have identified -small samples sizes, small effects, few studies with validation samples, and reasons to doubt the utility of PTRs -are equally present in research on other clinical problems. For example, Niles and colleagues have explored moderators of response in the treatment of anxiety disorders, comparing CBT to acceptance and commitment therapy (ACT) and also comparing coordinated anxiety management with CBT or antidepressants to usual care(Niles, Loerinc, et al, 2017;Niles, Wolitzky-Taylor, et al, 2017). These studies suggest small-to-medium effects matching patients to ACT vs. CBT (r = 0.28, N = 208) or coordinated anxiety management to usual care (r = 0.20, N = 876), and they have not been replicated.…”