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A latent class model for repeated measurements experiments.

A M Skene1, S A White

  • 1British Heart Foundation Cardiovascular Statistics Group, University of Nottingham, University Park, U.K.

Statistics in Medicine
|December 1, 1992
PubMed
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This study introduces a novel latent variable model for analyzing repeated measurements, improving upon standard models by accounting for diverse response profiles within treatment groups. The method enhances understanding of treatment effects by identifying distinct response patterns and their proportions.

Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Experimental Design

Background:

  • Standard repeated measures models assume uniform response profiles across experimental units within a treatment group.
  • This assumption often fails to capture the heterogeneity of responses, such as distinct responder/non-responder groups or common response patterns across treatments.

Purpose of the Study:

  • To develop and present a statistical approach for analyzing experiments with heterogeneous response profiles in repeated measurements.
  • To characterize treatment effects by both the shape of response profiles and the proportion of subjects exhibiting each profile.

Main Methods:

  • Introduction of a latent variable into standard repeated measures models to accommodate distinct response profiles.
  • Utilizing the Expectation-Maximization (EM) algorithm for straightforward maximum likelihood estimation.

Related Experiment Videos

  • Employing residual analysis and empirical semi-variogram plots for model selection and validation.

    • Main Results:

    • The proposed latent variable model effectively analyzes experiments with multiple response profiles.
    • Treatment effects can be robustly investigated using likelihood-ratio statistics after determining the number of distinct profiles.
    • The methodology is demonstrated through a re-analysis of a dataset from Grizzle and Allen.

    Conclusions:

    • The latent variable approach provides a more realistic and powerful framework for analyzing repeated measurements with heterogeneous responses.
    • This method allows for a nuanced understanding of treatment efficacy by considering diverse response patterns.
    • The EM algorithm and diagnostic tools facilitate practical implementation and model assessment.