Abstract Number:
1800
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Peijun Liu (1), John Kornak (2), Adam Staffaroni (1), Julie Fields (3), Jingxuan Wang (4), Elena Tsoy (5)
Institutions:
(1) University of California San Francisco, N/A, (2) University of California-San Francisco, N/A, (3) Mayo Clinic, MN, (4) University of California, San Francisco, MA, (5) University of California, San Francisco, CA
Co-Author(s):
Elena Tsoy
University of California, San Francisco
First Author:
Presenting Author:
Abstract Text:
Traditional linear methods for generating age, sex, and education level corrected Z-scores in neuropsychological assessments can be problematic because of nonlinearity and bounded test scores. We propose a nonlinear censored regression model for generating Z-scores that adjusts for age, sex, education, and race, while incorporating age-varying residual standard deviations. This approach addresses non-normal score distributions and boundary censoring, enhancing the detection of abnormal cognitive performance. Application to diverse normative datasets demonstrates improved accuracy and sensitivity over traditional methods, as corroborated by clinician feedback. Our results advocate for adopting this model to refine neuropsychological evaluations across varied populations.
Keywords:
Neuropsychological Testing
|Z-Score Adjustment|Censored Regression|Nonlinear Modeling|Cognitive Assessment|
Sponsors:
Mental Health Statistics Section
Tracks:
Big data/machine learning
Can this be considered for alternate subtype?
Yes
Are you interested in volunteering to serve as a session chair?
Yes
I have read and understand that JSM participants must abide by the Participant Guidelines.
Yes
I understand that JSM participants must register and pay the appropriate registration fee by June 3, 2025. The registration fee is non-refundable.
I understand