title: Predicting long-term depression outcome using a three-mode principal component model for depression heterogeneity.
abstract: BACKGROUND
  Depression heterogeneity has hampered development of adequate prognostic models. Therefore, more homogeneous clinical entities (e.g. dimensions, subtypes) have been developed, but their differentiating potential is limited because neither captures all relevant variation across persons, symptoms and time. To address this, three-mode Principal Component Analysis (3MPCA) was previously applied to capture person-, symptom- and time-level variation in a single model (Monden et al., 2015). This study evaluated the added prognostic value of such an integrated model for longer-term depression outcomes.
  
  
  METHODS
  The Beck Depression Inventory (BDI) was administered quarterly for two years to major depressive disorder outpatients participating in a randomized controlled trial. A previously developed 3MPCA model decomposed the data into 2 symptom-components ('somatic-affective', 'cognitive'), 2 time-components ('recovering', 'persisting') and 3 person-components ('severe non-persisting depression', 'somatic depression' and 'cognitive depression'). The predictive value of the 3MPCA model for BDI scores at 3-year (n=136) and 11-year follow-up (n=145) was compared with traditional latent variable models and traditional prognostic factors (e.g. baseline BDI component scores, personality).
  
  
  RESULTS
  3MPCA components predicted 41% and 36% of the BDI variance at 3- and 11-year follow-up, respectively. A latent class model, growth mixture model and other known prognostic variables predicted 4-32% and 3-24% of the BDI variance at 3- and 11-year follow-up, respectively.
  
  
  CONCLUSIONS
  Only primary care patients were included. There was no independent validation sample.
  
  
  CONCLUSIONS
  Accounting for depression heterogeneity at the person-, symptom- and time-level improves longer-term predictions of depression severity, underlining the potential of this approach for developing better prognostic models.
authors.affiliation: University of Groningen, University Medical Center Groningen, Interdisciplinary Center Psychopathology and Emotion regulation (ICPE), Department of Psychiatry, (CC-72), PO Box 30.001, 9700 Groningen, The Netherlands. Electronic address: r.tendeiro-monden@umcg.nl.
authors.forename: Rei
authors.initials: R
authors.lastname: Monden
authors.affiliation: University of Groningen, Heijmans Institute of Psychological Research, Groningen, The Netherlands.
authors.forename: Alwin
authors.initials: A
authors.lastname: Stegeman
authors.affiliation: University of Amsterdam, Department of Clinical Psychology, Amsterdam, The Netherlands.
authors.forename: Henk Jan
authors.initials: HJ
authors.lastname: Conradi
authors.affiliation: University of Groningen, University Medical Center Groningen, Interdisciplinary Center Psychopathology and Emotion regulation (ICPE), Department of Psychiatry, (CC-72), PO Box 30.001, 9700 Groningen, The Netherlands.
authors.forename: Peter
authors.initials: P
authors.lastname: de Jonge
authors.affiliation: University of Groningen, University Medical Center Groningen, Interdisciplinary Center Psychopathology and Emotion regulation (ICPE), Department of Psychiatry, (CC-72), PO Box 30.001, 9700 Groningen, The Netherlands.
authors.forename: Klaas J
authors.initials: KJ
authors.lastname: Wardenaar
mesh_terms: D000223:Adaptation, Psychological; D000328:Adult; D000368:Aged; D003865:Major Depressive Disorder / Q000175:diagnosis* / Q000523:psychology* / Q000628:therapy; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008960:Models, Psychological; D010045:Outpatients / Q000706:statistics & numerical data; D010555:Personality Inventory / Q000706:statistics & numerical data; D025341:Principal Component Analysis; D011379:Prognosis; D011569:Psychiatric Status Rating Scales; D011594:Psychometrics
keywords: Beck Depression Inventory (BDI); Course; Major depressive disorder; Prognosis; Three-mode Principal Component Analysis (3MPCA)