Modelling the Distribution of Cognitive Outcomes for Early-Stage Neurocognitive Disorders: A Model Comparison Approach
<b>Background</b>: 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 associ...
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| Format: | Article |
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MDPI AG
2024-02-01
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| Series: | Biomedicines |
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| Online Access: | https://www.mdpi.com/2227-9059/12/2/393 |
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| author | Seyed Ehsan Saffari See Ann Soo Raziyeh Mohammadi Kok Pin Ng William Greene Negaenderan Kandiah |
| author_facet | Seyed Ehsan Saffari See Ann Soo Raziyeh Mohammadi Kok Pin Ng William Greene Negaenderan Kandiah |
| author_sort | Seyed Ehsan Saffari |
| collection | DOAJ |
| description | <b>Background</b>: 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. <b>Materials and Methods</b>: 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. <b>Results</b>: 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. <b>Conclusions</b>: 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. |
| first_indexed | 2024-03-07T22:40:24Z |
| format | Article |
| id | doaj.art-22c5ada1904542c88dc926c369cd93d9 |
| institution | Directory Open Access Journal |
| issn | 2227-9059 |
| language | English |
| last_indexed | 2024-03-07T22:40:24Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomedicines |
| spelling | doaj.art-22c5ada1904542c88dc926c369cd93d92024-02-23T15:08:42ZengMDPI AGBiomedicines2227-90592024-02-0112239310.3390/biomedicines12020393Modelling the Distribution of Cognitive Outcomes for Early-Stage Neurocognitive Disorders: A Model Comparison ApproachSeyed Ehsan Saffari0See Ann Soo1Raziyeh Mohammadi2Kok Pin Ng3William Greene4Negaenderan Kandiah5Centre for Quantitative Medicine, Duke-NUS Medical School, National University of Singapore, Singapore 169857, SingaporeDementia Research Centre (Singapore), Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, SingaporeCentre for Quantitative Medicine, Duke-NUS Medical School, National University of Singapore, Singapore 169857, SingaporeDepartment of Neurology, National Neuroscience Institute, Singapore 308433, SingaporeStern School of Business (Emeritus), New York University, New York, NY 10012, USADementia Research Centre (Singapore), Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore<b>Background</b>: 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. <b>Materials and Methods</b>: 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. <b>Results</b>: 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. <b>Conclusions</b>: 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.https://www.mdpi.com/2227-9059/12/2/393cognitive impairmentcognitive screening toolMontreal Cognitive Assessmentvisual cognitive assessment testskewness |
| spellingShingle | Seyed Ehsan Saffari See Ann Soo Raziyeh Mohammadi Kok Pin Ng William Greene Negaenderan Kandiah Modelling the Distribution of Cognitive Outcomes for Early-Stage Neurocognitive Disorders: A Model Comparison Approach Biomedicines cognitive impairment cognitive screening tool Montreal Cognitive Assessment visual cognitive assessment test skewness |
| title | Modelling the Distribution of Cognitive Outcomes for Early-Stage Neurocognitive Disorders: A Model Comparison Approach |
| title_full | Modelling the Distribution of Cognitive Outcomes for Early-Stage Neurocognitive Disorders: A Model Comparison Approach |
| title_fullStr | Modelling the Distribution of Cognitive Outcomes for Early-Stage Neurocognitive Disorders: A Model Comparison Approach |
| title_full_unstemmed | Modelling the Distribution of Cognitive Outcomes for Early-Stage Neurocognitive Disorders: A Model Comparison Approach |
| title_short | Modelling the Distribution of Cognitive Outcomes for Early-Stage Neurocognitive Disorders: A Model Comparison Approach |
| title_sort | modelling the distribution of cognitive outcomes for early stage neurocognitive disorders a model comparison approach |
| topic | cognitive impairment cognitive screening tool Montreal Cognitive Assessment visual cognitive assessment test skewness |
| url | https://www.mdpi.com/2227-9059/12/2/393 |
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