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Estimating correlation coefficient between two variables with repeated observations using mixed effects model.

Anuradha Roy1

  • 1Department of Management Science and Statistics, The University of Texas at San Antonio, 6900 N Loop 1604 West, San Antonio, Texas 78249, USA. aroy@utsa.edu

Biometrical Journal. Biometrische Zeitschrift
|May 20, 2006
PubMed
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This study introduces a generalized linear mixed effects (LME) model to accurately estimate correlation coefficients with repeated measures. The method accounts for the true correlation structure, improving upon previous approaches for analyzing linked data.

Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Longitudinal Data Analysis

Background:

  • Estimating correlation coefficients with repeated observations is crucial in various scientific fields.
  • Existing methods like those by Bland and Altman, Lam et al., and Hamlett et al. have limitations in handling complex correlation structures in repeated measures.
  • There is a need for a robust method that can identify and incorporate the specific correlation structure present in longitudinal data.

Purpose of the Study:

  • To develop and validate a generalized linear mixed effects (LME) model for estimating correlation coefficients with repeated observations.
  • To address the limitations of previous methods by accurately accounting for the inherent correlation structure within repeated measures.
  • To provide guidance on selecting the appropriate correlation structure for longitudinal data analysis.

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Main Methods:

  • Utilizing a generalized linear mixed effects (LME) model that assumes repeated measures are linked over time.
  • Extending previous models to accurately account for the specific correlation structure present in the data.
  • Employing Proc Mixed in SAS for analysis and investigating the impact of incorrect correlation structure assumptions.

Main Results:

  • The proposed generalized LME model successfully accounts for the actual correlation structure in repeated measures.
  • The study demonstrates how incorrect assumptions about the correlation structure can affect the estimated correlation coefficient.
  • Methods for selecting the appropriate correlation structure for repeated measures are described.

Conclusions:

  • The developed generalized LME model offers a significant advancement for correlation estimation in longitudinal studies.
  • Accurate modeling of the correlation structure is essential for reliable estimation of relationships between variables with repeated observations.
  • The model's flexibility, including handling missing data and incorporating random effects, enhances its utility in real-world research.