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Variance components testing in the longitudinal mixed effects model

D O Stram1, J W Lee

  • 1Department of Preventive Medicine, University of Southern California, Arcadia 91066-6012.

Biometrics
|December 1, 1994
PubMed
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This study examines likelihood ratio tests for variance components in longitudinal mixed effects models. It analyzes their large-sample behavior using established nonstandard testing results.

Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Longitudinal mixed effects linear models are widely used for analyzing repeated measures data.
  • Assessing variance components is crucial for model interpretation and inference.
  • Laird and Ware (1982) introduced a foundational framework for these models.

Purpose of the Study:

  • To investigate the asymptotic behavior of likelihood ratio tests (LRTs).
  • To specifically analyze LRTs for nonzero variance components within the Laird and Ware longitudinal model.
  • To provide theoretical underpinnings for statistical inference in these models.

Main Methods:

  • Utilizing asymptotic theory for likelihood ratio tests.
  • Applying results for nonstandard testing situations as described by Self and Liang (1987).

Related Experiment Videos

  • Focusing on the large-sample properties of the statistical tests.

    • Main Results:

    • The study provides insights into the convergence properties of LRTs for variance components.
    • Characterizes the behavior of these tests as sample sizes increase.
    • Establishes theoretical foundations for hypothesis testing in this context.

    Conclusions:

    • The asymptotic behavior of LRTs for variance components in longitudinal mixed models is well-defined.
    • The findings support the use of LRTs for hypothesis testing in such models.
    • This work contributes to the robust statistical inference for longitudinal data.