Item response theory analysis of the Clinical Dementia Rating

Yan Li, Chengjie Xiong, Andrew J. Aschenbrenner, Chih Hung Chang, Michael W. Weiner, Rachel L. Nosheny, Dan Mungas, Randall J. Bateman, Jason Hassenstab, Krista L. Moulder, John C. Morris

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Introduction: The Clinical Dementia Rating (CDR) is widely used in Alzheimer's disease research studies and has well established reliability and validity. To facilitate the development of an online, electronic CDR (eCDR) for more efficient clinical applications, this study aims to produce a shortened version of the CDR, and to develop the statistical model for automatic scoring. Methods: Item response theory (IRT) was used for item evaluation and model development. An automatic scoring algorithm was validated using existing CDR global and domain box scores as the reference standard. Results: Most CDR items discriminate well at mild and very mild levels of cognitive impairment. The bi-factor IRT model fits best and the shortened CDR still demonstrates very high classification accuracy (81%∼92%). Discussion: The shortened version of the CDR and the automatic scoring algorithm has established a good foundation for developing an eCDR and will ultimately improve the efficiency of cognitive assessment.

Original languageEnglish (US)
JournalAlzheimer's and Dementia
DOIs
StateAccepted/In press - 2020

Keywords

  • Alzheimer's disease
  • bi-factor model
  • Clinical Dementia Rating
  • cognitive assessment
  • dementia severity
  • item response theory

ASJC Scopus subject areas

  • Epidemiology
  • Health Policy
  • Developmental Neuroscience
  • Clinical Neurology
  • Geriatrics and Gerontology
  • Psychiatry and Mental health
  • Cellular and Molecular Neuroscience

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