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Random-effects models for longitudinal data

N M Laird, J H Ware

    Biometrics
    |December 1, 1982
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces flexible two-stage random-effects models for analyzing longitudinal data, offering a unified approach for complex datasets. These models accommodate unbalanced data, crucial for epidemiological research on air pollution health effects.

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    Area of Science:

    • Biostatistics
    • Epidemiology
    • Longitudinal Data Analysis

    Background:

    • Longitudinal data analysis requires accounting for serial correlations within subjects.
    • Multivariate models with general covariance structures are challenging with unbalanced data.
    • Two-stage random-effects models offer a practical solution for such data.

    Purpose of the Study:

    • To present a general family of two-stage random-effects models for longitudinal data analysis.
    • To provide a unified framework encompassing growth and repeated-measures models.
    • To demonstrate a unified fitting approach using empirical Bayes and maximum likelihood estimation.

    Main Methods:

    • Development of a general family of two-stage random-effects models.
    • Application of combined empirical Bayes and maximum likelihood estimation.

    Related Experiment Videos

  • Utilizing the Expectation-Maximization (EM) algorithm for parameter estimation.

    • Main Results:

    • The proposed models are flexible and can handle highly unbalanced longitudinal data.
    • The unified fitting approach is effective for estimating model parameters.
    • Demonstrated applicability in an epidemiological study on air pollution.

    Conclusions:

    • Two-stage random-effects models provide a robust and flexible method for longitudinal data analysis.
    • The unified estimation approach simplifies the analysis of complex models.
    • These methods are valuable for epidemiological studies, particularly those with air pollution data.