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Related Experiment Video

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

Seyed Ehsan Saffari1,2, See Ann Soo3, Raziyeh Mohammadi1

  • 1Centre for Quantitative Medicine, Duke-NUS Medical School, National University of Singapore, Singapore 169857, Singapore.

Biomedicines
|February 24, 2024
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Summary
This summary is machine-generated.

This study introduces an inverse transformation method to improve the modeling of cognitive scores, like the Visual Cognitive Assessment Test (VCAT), for neurocognitive disorders. This approach enhances the robustness of association analyses for skewed cognitive data.

Keywords:
Montreal Cognitive Assessmentcognitive impairmentcognitive screening toolskewnessvisual cognitive assessment test

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Area of Science:

  • Neuroscience
  • Biostatistics
  • Gerontology

Background:

  • Cognitive assessments like MoCA and VCAT are crucial for neurocognitive disorders.
  • Cognitive scores are often left-skewed, potentially compromising association analysis robustness.
  • Existing methods may not accurately model skewed cognitive outcome distributions.

Purpose of the Study:

  • To investigate the distribution of cognitive outcomes in neurocognitive disorders.
  • To propose and evaluate statistical solutions for modeling skewed cognitive scores.
  • To enhance the reliability of association analyses in early-stage neurocognitive disorders.

Main Methods:

  • Retrospective cohort study of individuals with subjective cognitive decline or mild cognitive impairment.
  • Modeling inverse-transformed cognitive outcomes (VCAT, MoCA) using various statistical distributions.
  • Assessing model robustness through different scenarios, including covariate adjustment and bootstrapping.

Main Results:

  • Inverse transformation significantly improved cognitive score modeling compared to conventional methods.
  • Bootstrap methods enhanced estimate precision and robustness of standard errors.
  • Proposed models demonstrated superior goodness-of-fit compared to standard normal distribution models.

Conclusions:

  • The inverse transformation method is recommended for modeling left-skewed cognitive outcomes in early neurocognitive disorders.
  • This approach offers a more robust alternative to conventional methods using original cognitive scores.
  • Improved modeling enhances the accuracy of association analyses in clinical research.