A factor mixture model for multivariate survival data: an application to the analysis of lifetime mental disorders

Josue Almansa*, Jeroen K. Vermunt, Carlos G. Forero, Jordi Alonso

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)

Abstract

The assessment of the lifetime prevalence of mental disorders under comorbidity conditions is an important area in mental health research. Because information on lifetime disorders is usually gathered retrospectively within cross-sectional studies, the information is necessarily right censored and this should be taken into account when setting up models for the estimation of lifetime prevalences. We propose a factor analytic discrete time survival model combining mixture item response theory and discrete time hazard functions to describe disorder associations while accounting for censoring. This model is used for describing the lifetime prevalence and comorbidity of eight depression and anxiety disorders from the European Study of the Epidemiology of Mental Disorders.

Original languageEnglish
Pages (from-to)85-102
Number of pages18
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume63
Issue number1
DOIs
Publication statusPublished - Jan-2014
Externally publishedYes

Keywords

  • Cure fraction
  • Disorder diathesis
  • Internalizing disorders
  • Item response theory
  • Latent classes
  • Psychiatric comorbidity
  • NATIONAL COMORBIDITY SURVEY
  • LONG-TERM SURVIVORS
  • DSM-IV DISORDERS
  • AGE-OF-ONSET
  • SURVEY REPLICATION
  • ESEMED PROJECT
  • JOINT ANALYSIS
  • TIME
  • PREVALENCE
  • EVENT

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