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Related Concept Videos

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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Sigmoidal mixed models for longitudinal data.

Ana W Capuano1, Robert S Wilson1, Sue E Leurgans1

  • 11 Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.

Statistical Methods in Medical Research
|May 1, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a flexible sigmoidal mixed model for analyzing nonlinear cognitive trajectories, offering accurate estimates even with model misspecification. The model can anticipate cognitive decline by considering factors like body mass changes.

Keywords:
Alzheimer’s diseaseNonlinear modelscognitive declinelongitudinal datamixed modelsterminal decline

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Area of Science:

  • Neuroscience
  • Biostatistics
  • Gerontology

Background:

  • Longitudinal cognitive data analysis often employs linear mixed models.
  • Cognitive function trajectories are frequently nonlinear, exhibiting accelerated decline near death.
  • Existing nonlinear models (polynomial, piecewise linear) have limitations.

Purpose of the Study:

  • To introduce and evaluate a flexible sigmoidal mixed model for nonlinear cognitive trajectories.
  • To assess the model's accuracy in estimating cognitive levels over time, even with misspecification.
  • To explore the relationship between physiological changes (body mass) and cognitive decline trajectories.

Main Methods:

  • Development of a general sigmoidal mixed model with up to five parameters (final level, rate, midpoint, initial level, asymmetry).
  • Focus on a four-parameter symmetric sub-class with random effects on two parameters.
  • Application of a likelihood approach for model fitting and analysis of longitudinal data, including deceased participants with cognitive and body mass data.

Main Results:

  • The likelihood approach accurately estimates mean cognitive levels over time, robust to model misspecification.
  • The sigmoidal mixed model effectively characterizes nonlinear cognitive decline.
  • Deviations in body mass correlate with modified cognitive trajectory curves and can anticipate decline.

Conclusions:

  • Flexible sigmoidal mixed models provide a powerful alternative for analyzing nonlinear cognitive data.
  • The proposed method offers accurate estimation and robust performance.
  • Physiological factors like body mass stability are significant predictors of cognitive decline trajectories.