title: The Personalized Advantage Index: translating research on prediction into individualized treatment recommendations. A demonstration.
abstract: BACKGROUND
  Advances in personalized medicine require the identification of variables that predict differential response to treatments as well as the development and refinement of methods to transform predictive information into actionable recommendations.
  
  
  OBJECTIVE
  To illustrate and test a new method for integrating predictive information to aid in treatment selection, using data from a randomized treatment comparison.
  
  
  METHODS
  Data from a trial of antidepressant medications (N = 104) versus cognitive behavioral therapy (N = 50) for Major Depressive Disorder were used to produce predictions of post-treatment scores on the Hamilton Rating Scale for Depression (HRSD) in each of the two treatments for each of the 154 patients. The patient's own data were not used in the models that yielded these predictions. Five pre-randomization variables that predicted differential response (marital status, employment status, life events, comorbid personality disorder, and prior medication trials) were included in regression models, permitting the calculation of each patient's Personalized Advantage Index (PAI), in HRSD units.
  
  
  RESULTS
  For 60% of the sample a clinically meaningful advantage (PAI≥3) was predicted for one of the treatments, relative to the other. When these patients were divided into those randomly assigned to their "Optimal" treatment versus those assigned to their "Non-optimal" treatment, outcomes in the former group were superior (d = 0.58, 95% CI .17-1.01).
  
  
  CONCLUSIONS
  This approach to treatment selection, implemented in the context of two equally effective treatments, yielded effects that, if obtained prospectively, would rival those routinely observed in comparisons of active versus control treatments.
authors.affiliation: Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Robert J
authors.initials: RJ
authors.lastname: DeRubeis
authors.affiliation: Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Zachary D
authors.initials: ZD
authors.lastname: Cohen
authors.affiliation: Department of Psychiatry, The Ohio State University, Columbus, Ohio, United States of America.
authors.forename: Nicholas R
authors.initials: NR
authors.lastname: Forand
authors.affiliation: Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Jay C
authors.initials: JC
authors.lastname: Fournier
authors.affiliation: Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Lois A
authors.initials: LA
authors.lastname: Gelfand
authors.affiliation: Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Lorenzo
authors.initials: L
authors.lastname: Lorenzo-Luaces
mesh_terms: D000928:Antidepressive Agents / Q000627:therapeutic use; D015928:Cognitive Behavioral Therapy; D003865:Major Depressive Disorder / Q000188:drug therapy; D006287:Health Planning Guidelines; D006801:Humans; D057285:Precision Medicine; D011379:Prognosis; D011569:Psychiatric Status Rating Scales; D057170:Translational Research, Biomedical
chemical_list: D000928:Antidepressive Agents