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Smooth Curves, Similar Conclusions? Comparing Linear Regression and GAMLSS Neuropsychological Norms

· 2026-09-04 · 原文

DOI:10.64898/2026.09.01.26361590v1?rss=1预印本:medRxiv

Objective: Regression-based normative approaches are widely used in neuropsychology but often rely on score transformations to satisfy model assumptions. We compared previously published linear regression (LR)-based norms with norms derived using Generalized Additive Models for Location, Scale and Shape (GAMLSS) for the brief cognitive battery used in the Norwegian Dementia Disease Initiation (DDI) cohort. Method: GAMLSS norms were developed using the same normative samples as the original LR norms for the Consortium to Establish a Registry for Alzheimers Disease (CERAD) word list test, Trail Making Test (TMT) A and B, FAS phonemic fluency, and Visual Object and Space Perception Battery (VOSP) Silhouettes. Expected low-score frequencies and empirical base rates were assessed in a normative

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1. 人话版

Objective: Regression-based normative approaches are widely used in neuropsychology but often rely on score transformations to satisfy model assumptions.

We compared previously published linear regression (LR)-based norms with norms derived using Generalized Additive Models for Location, Scale and Shape (GAMLSS) for the brief cognitive battery used in the Norwegian Dementia Disease Initiation (DDI) cohort.

2. 领域脉络

Method: GAMLSS norms were developed using the same normative samples as the original LR norms for the Consortium to Establish a Registry for Alzheimers Disease (CERAD) word list test, Trail Making Test (TMT) A and B, FAS phonemic fluency, and Visual Object and Space Perception Battery (VOSP) Silhouettes.

3. 机制拆解

Expected low-score frequencies and empirical base rates were assessed in a normative

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5. 反例与边界

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