Modelling the Distribution of Cognitive Outcomes for Early-Stage Neurocognitive Disorders: A Model Comparison Approach

Seyed Ehsan Saffari; See Ann Soo; Raziyeh Mohammadi; Kok Pin Ng; William Greene; Negaenderan Kandiah

doi:10.3390/biomedicines12020393

Modelling the Distribution of Cognitive Outcomes for Early-Stage Neurocognitive Disorders: A Model Comparison Approach

Biomedicines. 2024 Feb 8;12(2):393. doi: 10.3390/biomedicines12020393.

Authors

Seyed Ehsan Saffari^{1

2}, See Ann Soo³, Raziyeh Mohammadi¹, Kok Pin Ng^{2

4}, William Greene⁵, Negaenderan Kandiah^{3

4}

Affiliations

¹ Centre for Quantitative Medicine, Duke-NUS Medical School, National University of Singapore, Singapore 169857, Singapore.
² Department of Neurology, National Neuroscience Institute, Singapore 308433, Singapore.
³ Dementia Research Centre (Singapore), Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore.
⁴ Duke-NUS Medical School, National University of Singapore, Singapore 169857, Singapore.
⁵ Stern School of Business (Emeritus), New York University, New York, NY 10012, USA.

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.

Keywords: Montreal Cognitive Assessment; cognitive impairment; cognitive screening tool; skewness; visual cognitive assessment test.

Grants and funding

This research is supported by the Ministry of Education, Singapore, under its MOE AcRF Tier 3 Award MOE2017-T3-1-002, National Medical Research Council (NMRC), Singapore, under its Clinician Scientist Award (MOH-CSAINV18nov-0007), National Neuroscience Institute-Health Research Endowment Fund, Singapore (NNI-HREF 991016), and National Medical Research Council (NMRC), Singapore, under its Clinician Scientist Award (CNIG22jul-0004).