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  3. Multiple Comparisons With Overdispersed Multinomial Data: Methods, Properties And Application.
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  3. Multiple Comparisons With Overdispersed Multinomial Data: Methods, Properties And Application.

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Multiple Comparisons With Overdispersed Multinomial Data: Methods, Properties and Application.

Sören Budig1, Charlotte Vogel2, Frank Schaarschmidt1

  • 1Department of Biostatistics, Leibniz University Hannover, Hannover, Germany.

Pharmaceutical Statistics
|January 19, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Addressing overdispersion in clustered multinomial data is crucial for accurate statistical inference. This study recommends specific methods for multiple comparisons and multiplicity adjustments, balancing error control and statistical power.

Keywords:
categorical data analysisclustered datamultiple contrastsquasi‐likelihoodzero counts

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Area of Science:

  • Statistics
  • Biostatistics
  • Data Analysis

Background:

  • Overdispersion in clustered multinomial data can compromise statistical inference.
  • Standard methods may yield biased standard errors when overdispersion is present.

Purpose of the Study:

  • To develop and evaluate methods for multiple comparisons and multiplicity adjustments in clustered, overdispersed multinomial data.
  • To compare the performance of different estimators and models for handling overdispersion.

Main Methods:

  • Investigated four quasi-likelihood estimators and the Dirichlet-multinomial model.
  • Conducted a simulation study evaluating family-wise error rate, statistical power, and coverage probability.
  • Incorporated pseudo-observations to address zero-count categories.

Main Results:

  • The Afroz quasi-likelihood estimator is recommended for strict error control.
  • The Dirichlet-multinomial model is preferred for higher statistical power, with a slight increase in false positives.
  • Pseudo-observations effectively mitigated estimation issues with zero-count data.

Conclusions:

  • The proposed methods offer robust solutions for analyzing clustered, overdispersed multinomial data.
  • The choice between Afroz quasi-likelihood and Dirichlet-multinomial depends on the balance between error control and statistical power.
  • The methods are practically useful, as demonstrated in toxicology and flow cytometry datasets.