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Item response theory analysis of the Clinical Dementia Rating
Author(s) -
Li Yan,
Xiong Chengjie,
Aschenbrenner Andrew J.,
Chang ChihHung,
Weiner Michael W,
Nosheny Rachel L,
Mungas Dan,
Bateman Randall J,
Hassenstab Jason,
Moulder Krista L,
Morris John C
Publication year - 2021
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.12210
Subject(s) - clinical dementia rating , item response theory , dementia , reliability (semiconductor) , computer science , rating scale , cognitive impairment , artificial intelligence , cognition , machine learning , clinical psychology , psychology , disease , psychometrics , medicine , psychiatry , developmental psychology , power (physics) , physics , quantum mechanics
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.