ArticleFrontiers in digital health2024
Generating normative data from web-based administration of the Cambridge Neuropsychological Test Automated Battery using a Bayesian framework.
Article in Frontiers in digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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 ( Results: 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. Conclusion: This study documents a novel, reproducible and robust method for describing normative cognitive performance with ageing using a large dataset.
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