Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/178478
Title: Modelling the distribution of cognitive outcomes for early-stage neurocognitive disorders: a model comparison approach
Authors: Saffari, Seyed Ehsan
Soo, See Ann
Mohammadi, Raziyeh
Ng, Kok Pin
Greene, William
Kandiah, Negaenderan
Keywords: Medicine, Health and Life Sciences
Issue Date: 2024
Source: Saffari, S. E., Soo, S. A., Mohammadi, R., Ng, K. P., Greene, W. & Kandiah, N. (2024). Modelling the distribution of cognitive outcomes for early-stage neurocognitive disorders: a model comparison approach. Biomedicines, 12(2), 393-. https://dx.doi.org/10.3390/biomedicines12020393
Project: MOE2017-T3-1-002 
MOH-CSAINV18nov-0007 
NNI-HREF 991016 
CNIG22jul-0004 
Journal: Biomedicines 
Abstract: Background: Cognitive assessments for patients with neurocognitive disorders are mostly measured by the Montreal Cognitive Assessment (MoCA) and Visual Cognitive Assessment Test (VCAT) as screening tools. These cognitive scores are usually left-skewed and the results of the association analysis might not be robust. This study aims to study the distribution of the cognitive outcomes and to discuss potential solutions. Materials and Methods: In this retrospective cohort study of individuals with subjective cognitive decline or mild cognitive impairment, the inverse-transformed cognitive outcomes are modelled using different statistical distributions. The robustness of the proposed models are checked under different scenarios: with intercept-only, models with covariates, and with and without bootstrapping. Results: The main results were based on the VCAT score and validated via the MoCA score. The findings suggested that the inverse transformation method improved the modelling the cognitive scores compared to the conventional methods using the original cognitive scores. The association of the baseline characteristics (age, gender, and years of education) and the cognitive outcomes were reported as estimates and 95% confidence intervals. Bootstrap methods improved the estimate precision and the bootstrapped standard errors of the estimates were more robust. Cognitive outcomes were widely analysed using linear regression models with the default normal distribution as a conventional method. We compared the results of our suggested models with the normal distribution under various scenarios. Goodness-of-fit measurements were compared between the proposed models and conventional methods. Conclusions: The findings support the use of the inverse transformation method to model the cognitive outcomes instead of the original cognitive scores for early-stage neurocognitive disorders where the cognitive outcomes are left-skewed.
URI: https://hdl.handle.net/10356/178478
ISSN: 2227-9059
DOI: 10.3390/biomedicines12020393
Schools: Lee Kong Chian School of Medicine (LKCMedicine) 
Organisations: Duke-NUS Medical School 
Research Centres: Dementia Research Centre
Rights: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:LKCMedicine Journal Articles

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