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AUEB-Stats Seminars 12/6/2024: "Modelling longitudinal cognitive test data with ceiling effects and left skewness", by Denitsa Grigorova (Faculty of Mathematics and Informatics, Sofia University, Sofia, Bulgaria) Forumgrstats

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AUEB-Stats Seminars 12/6/2024: "Modelling longitudinal cognitive test data with ceiling effects and left skewness", by Denitsa Grigorova (Faculty of Mathematics and Informatics, Sofia University, Sofia, Bulgaria) Forumgrstats
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AUEB-Stats Seminars 12/6/2024: "Modelling longitudinal cognitive test data with ceiling effects and left skewness", by Denitsa Grigorova (Faculty of Mathematics and Informatics, Sofia University, Sofia, Bulgaria) Empty AUEB-Stats Seminars 12/6/2024: "Modelling longitudinal cognitive test data with ceiling effects and left skewness", by Denitsa Grigorova (Faculty of Mathematics and Informatics, Sofia University, Sofia, Bulgaria)

Tue 11 Jun 2024 - 20:50
AUEB STATS SEMINARS 2024

Denitsa Grigorova
Faculty of Mathematics and Informatics, Sofia University, Sofia, Bulgaria

Title: Modelling longitudinal cognitive test data with ceiling effects and left skewness


Wednesday, 12/06/2024
12:00

Room: Troias Amphitheatre

🔎 https://www.dept.aueb.gr/el/stat/events/modelling-longitudinal-cognitive-test-data-ceiling-effects-and-left-skewness

ABSTRACT

Cognitive tests such as the Mini Mental State Examination (MMSE) may result in data with discrete and skewed distributions that necessitate proper statistical models for valid inference. We review different longitudinal approaches to model cognitive decline data in older individuals and provide recommendations for model choice and result interpretation.
Methods: We used data from Alzheimer’s Disease Neuroimaging Initiative study and focused on MMSE scores collected on up to four visits over a two-year period in older individuals (mean age 73 years). At baseline individuals were classified as having Alzheimer’s disease (AD), early or late mild cognitive impairment, subjective memory concern, or being cognitively normal. We considered generalized additive models for location, scale and shape (GAMLSS) with binomial/beta-binomial response distribution and parametric/non-parametric random effects, selected the best model and used graphs for illustration.

Results: Binomial model with non-parametric random intercept and slope fit the best according to the Bayesian Information Criterion. The three way-interaction between time, age and diagnostic group was statistically significant suggesting that AD individuals had the steepest cognitive decline among all groups, especially in younger individuals. Furthermore, males and APOE4 carriers had worse cognitive performance, while more educated people had better cognitive performance compared to less educated. Various plots were used to illustrate and aid in interpretation of the results.

Conclusion: GAMLSS are an appropriate class of models providing interpretable results for repeatedly measured cognitive test data. We recommend that they are used more widely, accompanied by effect estimation, statistical testing and visualizations for illustration.

Keywords: Alzheimer’s disease; generalized additive models for location, scale and shape (GAMLSS); longitudinal data; Mini Mental State Examination (MMSE); statistical modelling


AUEB-Stats Seminars 12/6/2024: "Modelling longitudinal cognitive test data with ceiling effects and left skewness", by Denitsa Grigorova (Faculty of Mathematics and Informatics, Sofia University, Sofia, Bulgaria) 1718116079769?e=1721260800&v=beta&t=FZdDKO3dNbji-bBL1UOCUesrSjUe8jXfGUnAXpK9bnc
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