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ORIGINAL RESEARCH article

Front. Digit. Health
Sec. Health Informatics
Volume 6 - 2024 | doi: 10.3389/fdgth.2024.1294222

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

Provisionally accepted
Elizabeth Wragg Elizabeth Wragg 1Caroline Skirrow Caroline Skirrow 1,2Pasquale Dente Pasquale Dente 1Jack Cotter Jack Cotter 1Peter Annas Peter Annas 1,3Jasmin Kroll Jasmin Kroll 1*Milly Lowther Milly Lowther 1,4Rosa Backx Rosa Backx 1Jenny Barnett Jenny Barnett 1,5Fiona Cree Fiona Cree 1Francesca Cormack Francesca Cormack 1
  • 1 Cambridge Cognition (United Kingdom), Cambridge, United Kingdom
  • 2 University of Bristol, Bristol, England, United Kingdom
  • 3 Lundbeck Foundation, Copenhagen, Denmark
  • 4 Institute of Cognitive Neuroscience, Faculty of Brain Sciences, University College London, London, England, United Kingdom
  • 5 Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, England, United Kingdom

The final, formatted version of the article will be published soon.

    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. 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 Neuropsychological Test Automated Battery via the research crowdsourcing website Prolific.ac. Participants completed tests of visuospatial recognition memory (Spatial Working Memory test), visual episodic memory (Paired Associate Learning test) and sustained attention (Rapid Visual Information Processing test).Test outcomes were modelled as a function of age using Bayesian Generalised Linear Models, which were able to derive posterior distributions of the authentic data, drawing from a wide family of distributions. Markov Chain Monte Carlo algorithms generated a large synthetic dataset from posterior distributions for each outcome measure, capturing normative distributions of cognition as a function of age, sex and education. Comparison with stratified and linear regression methods showed converging results, with the Bayesian approach producing similar age, sex and education trends in the data, and similar categorisation of individual performance levels. This study documents a novel, reproducible and robust method for describing normative cognitive performance with ageing using a large dataset.

    Keywords: normative data, Cognition, Neuropsychology, Ageing, bayesian statistics

    Received: 18 Sep 2023; Accepted: 12 Jul 2024.

    Copyright: © 2024 Wragg, Skirrow, Dente, Cotter, Annas, Kroll, Lowther, Backx, Barnett, Cree and Cormack. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Jasmin Kroll, Cambridge Cognition (United Kingdom), Cambridge, United Kingdom

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.