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Multilevel time series models with applications to repeated measures data

H Goldstein1, M J Healy, J Rasbash

  • 1Institute of Education, University of London, U.K.

Statistics in Medicine
|August 30, 1994
PubMed
Summary
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Reply from j.g. Oakeshott et Al.

Trends in ecology & evolution·2011

This study introduces a time series model to analyze repeated measures data, accounting for within-individual correlations often missed by standard models. The enhanced model improves accuracy for time-series-dependent measurements.

Area of Science:

  • Statistics
  • Biostatistics
  • Time Series Analysis

Background:

  • Repeated measures data analysis commonly uses two-level random coefficients models.
  • A key assumption is uncorrelated within-individual residuals, which may be violated with closely spaced measurements.

Purpose of the Study:

  • To propose a time series model for repeated measures data that incorporates autocorrelation in level 1 residuals.
  • To address limitations of standard models when within-individual residuals are correlated.

Main Methods:

  • Augmenting standard multilevel models with autocorrelation models for level 1 residuals.
  • Considering first- and second-order autoregressive models, including seasonal components.
  • Examining both discrete and continuous time models and structuring autocorrelation parameters with explanatory variables.

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Main Results:

  • The proposed time series model effectively models autocorrelation in residuals for repeated measures data.
  • Autocorrelation parameters can be explained by additional variables.
  • The model was successfully applied to a dataset of children's repeated height measurements.

Conclusions:

  • The enhanced time series model provides a more accurate analysis of repeated measures data with correlated residuals.
  • This approach is valuable for longitudinal studies where temporal dependencies exist.
  • The methodology offers flexibility in modeling complex correlation structures.