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Cluster analysis using multivariate mixed effects models.

Luis Villarroel1, Guillermo Marshall, Anna E Barón

  • 1Departamento de Salud Publica, Facultad de Medicina, Pontificia Universidad Catolica de Chile, Santiago, Chile. lv@med.puc.cl

Statistics in Medicine
|June 19, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for grouping individuals based on longitudinal data from multiple variables. It uses non-linear multivariate mixed effects models for accurate clustering and classification in biological and social sciences.

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Area of Science:

  • Biological sciences
  • Social sciences
  • Biostatistics
  • Longitudinal data analysis

Background:

  • Longitudinal data analysis is common in biological and social sciences.
  • Clustering individuals based on longitudinal behavior is an area of growing interest.
  • Existing methods often use linear univariate mixed effects models.

Purpose of the Study:

  • To propose a novel method for clustering and classification using longitudinal data.
  • To extend existing methods to handle multiple longitudinal variables.
  • To develop a robust approach for both balanced and unbalanced data.

Main Methods:

  • Fitting non-linear multivariate mixed effect models.
  • Employing the Expectation-Maximization (EM) algorithm for parameter estimation.
  • Handling both balanced and unbalanced longitudinal data.

Main Results:

  • Successful identification of clusters based on multivariate longitudinal data.
  • Demonstration of the method's applicability with a real-world example.
  • Validation of classification accuracy against known individual group membership.

Conclusions:

  • The proposed method effectively clusters and classifies individuals using non-linear multivariate mixed effects models.
  • The EM algorithm provides robust parameter estimation for diverse data structures.
  • This approach offers a valuable tool for analyzing complex longitudinal data in various scientific fields.