title: Major depressive episodes and random mood.
abstract: BACKGROUND
  Mathematical models describing changes in mood in affective disorders may assist in the identification of underlying pathologic and neurobiologic mechanisms and in differentiating between alternative interpretations of psychiatric data.
  
  
  OBJECTIVE
  Using time-to-event data from a large epidemiologic survey on recovery from major depression, to model the survival probability, in terms of an underlying process, with parameters which might be recognized and influenced in clinical practice.
  
  
  METHODS
  We present a sequential-phase model for survival analysis, which describes depression as a state with or without an additional incubation phase. Recovery is seen as the transition to a nondepressive state. We show that this sequential-phase model finds a microscopic realization in a dynamic description, the random-mood model, which depicts mood as governed by an Ornstein-Uhlenbeck type of stochastic process, driven by intermittent gaussian noise.
  
  
  RESULTS
  For reversible depression (80%), the fractional probability of recovery is remarkably independent of the history of the depression. Analysis with the sequential-phase model suggests single exponential decay in this group, possibly with a short incubation phase. Within the random-mood model, the data for this reversibly depressed cohort are compatible with an intermittent noise pattern of stimuli with average spacing of 4 months and incompatible with nonintermittent noise.
  
  
  CONCLUSIONS
  Time-to-event data from psychiatric epidemiologic studies can be conceptualized through modeling as intrasubject processes. The proposed random-mood model reproduces the time-to-event data and explains the incubation phase as an artifact due to the inclusion criterion of 14 days in most current psychiatric diagnostic systems. Depression is found to result more often from pileup of negative stimuli than from single life events. Time sequences, generated using the random-mood model, produce power plots, phase-space trajectories, and pair-correlation sums, similar to recent results for individual patients. This suggests possible clinical relevance along with the model's use as a tool in survival analysis.
authors.affiliation: Kernfysisch Versneller Instituut of the University of Groningen, The Netherlands. s.y.van.der.werf@rug.nl
authors.forename: Siebren Y
authors.initials: SY
authors.lastname: van der Werf
authors.forename: Kirsten I
authors.initials: KI
authors.lastname: Kaptein
authors.forename: Peter
authors.initials: P
authors.lastname: de Jonge
authors.forename: Jan
authors.initials: J
authors.lastname: Spijker
authors.forename: Ron
authors.initials: R
authors.lastname: de Graaf
authors.forename: Jakob
authors.initials: J
authors.lastname: Korf
mesh_terms: D000339:Affect; D015331:Cohort Studies; D003865:Major Depressive Disorder / Q000175:diagnosis* / Q000453:epidemiology / Q000523:psychology; D016021:Epidemiologic Studies; D006801:Humans; D008016:Life Change Events; D008433:Mathematics; D008960:Models, Psychological; D015233:Models, Statistical; D009426:Netherlands / Q000453:epidemiology; D016011:Normal Distribution; D011336:Probability; D011446:Prospective Studies; D013269:Stochastic Processes; D016019:Survival Analysis