title: Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder.
abstract: Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful.
  
  
  
  We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments.
  
  
  
  Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials.
  
  
  
  Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.
authors.affiliation: Department of Health Care Policy,Harvard Medical School,Boston, MA,USA.
authors.forename: R C
authors.initials: RC
authors.lastname: Kessler
authors.affiliation: Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE),University of Groningen, University Medical Center Groningen,Groningen,The Netherlands.
authors.forename: H M
authors.initials: HM
authors.lastname: van Loo
authors.affiliation: Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE),University of Groningen, University Medical Center Groningen,Groningen,The Netherlands.
authors.forename: K J
authors.initials: KJ
authors.lastname: Wardenaar
authors.affiliation: Department of Veterans Affairs,Office of Public Health,Washington, DC,USA.
authors.forename: R M
authors.initials: RM
authors.lastname: Bossarte
authors.affiliation: VISN 19 Mental Illness Research Education and Clinical Center,University of Colorado,Anschutz Medical Campus,Anschulz, CO,USA.
authors.forename: L A
authors.initials: LA
authors.lastname: Brenner
authors.affiliation: Department of Health Care Policy,Harvard Medical School,Boston, MA,USA.
authors.forename: D D
authors.initials: DD
authors.lastname: Ebert
authors.affiliation: Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE),University of Groningen, University Medical Center Groningen,Groningen,The Netherlands.
authors.forename: P
authors.initials: P
authors.lastname: de Jonge
authors.affiliation: Department of Psychiatry and Depression Clinical and Research Program,Harvard Medical School and Massachusetts General Hospital,Boston, MA,USA.
authors.forename: A A
authors.initials: AA
authors.lastname: Nierenberg
authors.affiliation: Department of Health Care Policy,Harvard Medical School,Boston, MA,USA.
authors.forename: A J
authors.initials: AJ
authors.lastname: Rosellini
authors.affiliation: Department of Health Care Policy,Harvard Medical School,Boston, MA,USA.
authors.forename: N A
authors.initials: NA
authors.lastname: Sampson
authors.affiliation: Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE),University of Groningen, University Medical Center Groningen,Groningen,The Netherlands.
authors.forename: R A
authors.initials: RA
authors.lastname: Schoevers
authors.affiliation: Department of Epidemiology,Janssen Research and Development,Titusville, NJ,USA.
authors.forename: M A
authors.initials: MA
authors.lastname: Wilcox
authors.affiliation: Department of Health Care Policy,Harvard Medical School,Boston, MA,USA.
authors.forename: A M
authors.initials: AM
authors.lastname: Zaslavsky
mesh_terms: D000328:Adult; D000928:Antidepressive Agents / Q000627:therapeutic use*; D020000:Decision Support Systems, Clinical; D003865:Major Depressive Disorder / Q000188:drug therapy / Q000523:psychology / Q000628:therapy*; D019317:Evidence-Based Medicine; D005260:Female; D006801:Humans; D011613:Psychotherapy / Q000379:methods*; D057566:Self Report; D016896:Treatment Outcome
chemical_list: D000928:Antidepressive Agents
keywords: Depression; epidemiology; evidence-based psychiatry; research design and methods; treatment allocation
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