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P3‐134: Assessment of Japanese‐ and English‐language equivalence of the cognitive abilities screening instrument (CASI) among Japanese‐Americans in two large epidemiological studies
Author(s) -
Gibbons Laura E.,
McCurry Susan M.,
Rhoads Kristoffer,
Launer Lenore,
Masaski Kamal,
White Lon,
Borenstein Amy R.,
Larson Eric B.,
Crane Paul K.
Publication year - 2008
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 - 10.1016/j.jalz.2008.05.1699
Subject(s) - differential item functioning , equivalence (formal languages) , boston naming test , logistic regression , psychology , test (biology) , language assessment , item response theory , clinical psychology , cognition , demography , medicine , psychometrics , linguistics , mathematics education , neuropsychology , psychiatry , paleontology , biology , philosophy , sociology
memory, the Neuropsychological Assessment Battery (NAB) List Learning (LL) test (Stern & White, 2003) is a promising tool for the assessment of older adults due to its relative simplicity, excellent psychometric validation, equivalent forms, and extensive normative data in older individuals. This study examined the diagnostic utility of the NAB LL test in differentiating cognitively normal, MCI, and AD participants. Methods: Extant data from the Boston University Alzheimer’s Disease Center Registry was retrospectively analyzed for 153 participants (57-94 years, 74 8 years; 61% women) diagnosed by a consensus team as cognitively normal (n 98), amnestic MCI (single or multiple domain; n 29), or AD (n 26). Diagnoses were made independent of NAB LL performance. To determine classification accuracy, receiver operating characteristics analyses were conducted for seven NAB LL variables (List A Immediate Recall, List B Immediate Recall, Short Delay Recall, Long Delay Recall, Percent Retention, and Recognition Hits and False Alarms). In addition, ordinal multiple regression was used to create a model to predict group membership based on a combination of the NAB LL variables. Results: The sensitivities and specificities of the NAB LL test variables were good to excellent, yielding positive likelihood ratios ranging from 1.8 to 30.2 (Mdn 4.2). The ordinal regression produced a model that predicted group membership with an overall accuracy of 85%. The model was most accurate at classifying controls (97%), followed by AD (74%) and MCI (52%), which indicates that it can very accurately rule out the latter two diagnoses. Conclusions: Both the regression-based model and the individual NAB LL variables were highly accurate in differentiating cognitively normal controls, MCI, and AD groups. Compared to published data, the classification accuracy statistics presented here appear to match or exceed those reported for other common list learning tests (e.g., HVLT-R, CERAD, CVLT) in this population.