Nonlinear Z-score modeling for improved detection of cognitive abnormality.

ABSTRACT:

Introduction

Conventional Z-scores are generated by subtracting the mean and dividing by the standard deviation. More recent methods linearly correct for age, sex, and education, so that these "adjusted" Z-scores better represent whether an individual's cognitive performance is abnormal. Extreme negative Z-scores for individuals relative to this normative distribution are considered indicative of cognitive deficiency.

Methods

In this article, we consider nonlinear shape constrained additive models accounting for age, sex, and education (correcting for nonlinearity). Additional shape constrained additive models account for varying standard deviation of the cognitive scores with age (correcting for heterogeneity of variance).

Results

Corrected Z-scores based on nonlinea

SUBMITTER: Kornak J 

PROVIDER: S-EPMC6911910 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature