Vandemeulebroecke, Marc and Bornkamp, Björn and Krahnke, Tillmann and Mielke, Johanna and Monsch, Andreas and Quarg, Peter. (2017) A Longitudinal Item Response Theory Model to Characterize Cognition Over Time in Elderly Subjects. CPT: Pharmacometrics and Systems Pharmacology, 6 (9). pp. 635-641.
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Official URL: https://edoc.unibas.ch/66746/
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Abstract
For drug development in neurodegenerative diseases such as Alzheimer's disease, it is important to understand which cognitive domains carry the most information on the earliest signs of cognitive decline, and which subject characteristics are associated with a faster decline. A longitudinal Item Response Theory (IRT) model was developed for the Basel Study on the Elderly, in which the Consortium to Establish a Registry for Alzheimer's Disease - Neuropsychological Assessment Battery (with additions) and the California Verbal Learning Test were measured on 1,750 elderly subjects for up to 13.9 years. The model jointly captured the multifaceted nature of cognition and its longitudinal trajectory. The word list learning and delayed recall tasks carried the most information. Greater age at baseline, fewer years of education, and positive APOEɛ4 carrier status were associated with a faster cognitive decline. Longitudinal IRT modeling is a powerful approach for progressive diseases with multifaceted endpoints.
Faculties and Departments: | 03 Faculty of Medicine > Bereich Medizinische Fächer (Klinik) > Geriatrie > Geriatrie (Kressig) 03 Faculty of Medicine > Departement Klinische Forschung > Bereich Medizinische Fächer (Klinik) > Geriatrie > Geriatrie (Kressig) 07 Faculty of Psychology > Departement Psychologie 07 Faculty of Psychology > Departement Psychologie > Ehemalige Einheiten Psychologie > Molecular Neuroscience (Papassotiropoulos) |
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UniBasel Contributors: | Monsch, Andreas U. U |
Item Type: | Article, refereed |
Article Subtype: | Research Article |
ISSN: | 2163-8306 |
Note: | Publication type according to Uni Basel Research Database: Journal article |
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Last Modified: | 12 Feb 2019 17:58 |
Deposited On: | 12 Feb 2019 17:34 |
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