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The effect of random-effects misspecification on classification accuracy.

Riham El Saeiti1, Marta García-Fiñana1, David M Hughes1

  • 1Health Data Science, University of Liverpool Faculty of Health and Life Sciences, Liverpool, UK.

The International Journal of Biostatistics
|March 26, 2021
PubMed
Summary
This summary is machine-generated.

Generalized linear mixed models (GLMMs) analyze longitudinal data, but random effects misspecification impacts discrete outcomes. This study shows that while flexible distributions improve classification with severe non-normality, assuming a multivariate normal distribution often has minimal impact on patient group classification accuracy.

Keywords:
classificationgeneralised linear mixed modelslongitudinal discriminant analysismultivariate longitudinal datarandom effects

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Statistical Modeling

Background:

  • Mixed models are standard for longitudinal data, using random effects to model patient-specific trends.
  • Generalized linear mixed models (GLMMs) extend this to discrete outcomes, but robustness to random effects distribution misspecification is debated.
  • Previous research focused on parameter bias in single-outcome GLMMs.

Purpose of the Study:

  • To investigate the impact of random effects distribution misspecification on patient classification accuracy in GLMMs.
  • To evaluate classification performance with single versus multiple outcomes.
  • To compare the effectiveness of standard multivariate normal distributions against more flexible mixture distributions.

Main Methods:

  • Longitudinal discriminant analysis was employed to assess patient classification.
  • The study considered both single and multiple discrete outcomes.
  • Model performance was evaluated under various assumptions of the random effects distribution, including multivariate normal and mixture distributions.

Main Results:

  • Severe departures from normality in random effects distributions can be mitigated by using flexible mixture distributions, leading to improved classification accuracy.
  • However, assuming a standard multivariate normal distribution for random effects often had minimal impact on classification accuracy, even with misspecification.
  • The dimensionality of the random effects distribution increases with multiple outcomes, but the impact on classification accuracy varied.

Conclusions:

  • The assumption of a multivariate normal distribution for random effects in GLMMs may be acceptable for patient classification in many longitudinal studies, even when not strictly true.
  • Flexible mixture distributions offer benefits when significant deviations from normality are present, particularly for discrete outcomes.
  • The choice of random effects distribution warrants careful consideration, balancing model complexity with classification performance.