Generating normative data from web-based administration of the Cambridge Neuropsychological Test Automated Battery using a Bayesian framework.

ABSTRACT:

Introduction

Normative cognitive data can distinguish impairment from healthy cognitive function and pathological decline from normal ageing. Traditional methods for deriving normative data typically require extremely large samples of healthy participants, stratifying test variation by pre-specified age groups and key demographic features (age, sex, education). Linear regression approaches can provide normative data from more sparsely sampled datasets, but non-normal distributions of many cognitive test results may lead to violation of model assumptions, limiting generalisability.

Method

The current study proposes a novel Bayesian framework for normative data generation. Participants (n = 728; 368 male and 360 female, age 18-75 years), completed the Cambridge Neuropsy

SUBMITTER: Wragg E 

PROVIDER: S-EPMC11451437 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature