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Computing for incomplete repeated measures.

K Berk

    Biometrics
    |June 1, 1987
    PubMed
    Summary
    This summary is machine-generated.

    Analyzing incomplete repeated-measures data is complex. This study presents methods for unbalanced linear models and maximum likelihood procedures, including mixed models, for easier implementation with available software.

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    Area of Science:

    • Statistics
    • Biostatistics
    • Experimental Design

    Background:

    • Repeated-measures experiments collect multiple data points per subject.
    • Standard analysis is straightforward with complete, balanced data.
    • Incomplete or unbalanced data significantly complicates analysis.

    Purpose of the Study:

    • To provide accessible methods for analyzing incomplete repeated-measures data.
    • To discuss procedures for unbalanced linear models and maximum likelihood estimation.
    • To offer practical solutions implementable with existing software.

    Main Methods:

    • Exploration of simple, though not optimal, analytical approaches.
    • Detailed discussion of unbalanced linear model analysis.
    • Application of normal maximum likelihood (ML) procedures, including ML and restricted maximum likelihood (REML) estimators for mixed models.

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  • Estimation for models accommodating arbitrary within-subject covariance structures.

    • Main Results:

    • Outlines procedures for handling complex repeated-measures data.
    • Demonstrates the utility of ML and REML for mixed models.
    • Provides methods for arbitrary within-subject covariance matrices.
    • Focuses on software implementability.

    Conclusions:

    • Offers practical statistical procedures for challenging repeated-measures designs.
    • Enhances the analysis of incomplete and unbalanced experimental data.
    • Facilitates the application of advanced statistical techniques using readily available software.