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A discriminant analysis extension to mixed models.

L Tomasko1, R W Helms, S M Snapinn

  • 1Merck Research Laboratories, Merck & Co., Inc., West Point, PA 19486, USA. lisa_tomasko@merck.com

Statistics in Medicine
|June 11, 1999
PubMed
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This study extends discriminant analysis to utilize incomplete longitudinal data, improving classification accuracy by incorporating structured covariance. Random-effects models particularly enhance performance in small samples.

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Statistical Modeling

Background:

  • Discriminant analysis classifies observations using correlated measurements but traditionally requires complete data.
  • Existing methods may not fully leverage available longitudinal data when observations are missing.

Purpose of the Study:

  • To extend discriminant analysis for handling incomplete longitudinal data.
  • To incorporate structured covariance matrices into the discriminant function.
  • To evaluate the impact of different covariance structures on classification accuracy.

Main Methods:

  • Developed an extension of discriminant analysis to accommodate all available longitudinal data.
  • Modeled correlated measurements using structured covariance matrices (compound symmetric, heterogeneous compound symmetric, heterogeneous autoregressive).

Related Experiment Videos

  • Performed simulations to assess error rates and contrasted multivariate with random-effects covariance structures.

    • Main Results:

    • The proposed extension effectively utilizes incomplete longitudinal data.
    • Incorporating structured covariance improved the discrimination process compared to standard methods.
    • Random-effects covariance structures demonstrated improved error rates, especially in small sample sizes.

    Conclusions:

    • The developed discriminant analysis approach enhances classification with incomplete longitudinal data.
    • Structured covariance modeling, particularly with random-effects, offers advantages in accuracy and efficiency.
    • The method is applicable to clinical trial data for treatment unblinding based on repeated measures.