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Change point models for cognitive tests using semi-parametric maximum likelihood.

Ardo van den Hout1, Graciela Muniz-Terrera, Fiona E Matthews

  • 1Department of Statistical Science, University College London, 1-19 Torrington Place, London WC1E 7HB, UK.

Computational Statistics & Data Analysis
|March 9, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces advanced change point models for analyzing cognitive test data, offering better alternatives to normal distributions for discrete scores. These models help understand cognitive decline in older adults, particularly in the years preceding death.

Keywords:
Beta-binomial distributionLatent class modelMini-mental state examinationRandom-effects model

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Area of Science:

  • Statistics
  • Gerontology
  • Cognitive Science

Background:

  • Longitudinal cognitive data often exhibits discrete scores and ceiling effects.
  • Traditional change point models frequently assume normal distributions, which may not fit such data accurately.
  • Accurate modeling is crucial for understanding cognitive trajectories in aging populations.

Purpose of the Study:

  • To develop and present novel random-effects change point models for longitudinal cognitive test data.
  • To propose binomial and beta-binomial distributions as alternatives to the normal distribution for discrete cognitive scores.
  • To investigate cognitive change patterns in elderly individuals, especially in the period before death.

Main Methods:

  • Formulation of random-effects change point models incorporating smooth shapes.
  • Utilizing marginal maximum likelihood for estimation, combining parametric and non-parametric distributions.
  • Exploring extensions to latent class modeling for individuals without cognitive change.
  • Application to longitudinal data from a study of Swedish octogenarians and nonagenarians.

Main Results:

  • The study demonstrates the utility of binomial and beta-binomial distributions for modeling discrete cognitive test scores within change point frameworks.
  • The proposed models effectively capture cognitive changes over time, even with data exhibiting ceiling effects.
  • The methodology is successfully applied to real-world longitudinal data, providing insights into late-life cognitive trajectories.

Conclusions:

  • Random-effects change point models with appropriate distributions (binomial, beta-binomial) offer a robust approach for analyzing longitudinal cognitive data.
  • These models enhance the understanding of cognitive aging and decline, particularly in the final years of life.
  • The findings have implications for gerontological research and the study of cognitive trajectories in aging populations.