JP Journal of Biostatistics

The JP Journal of Biostatistics is a highly regarded open-access international journal indexed in the Emerging Sources Citation Index (ESCI). It focuses on the application of statistical theory and methods in resolving problems in biological, biomedical, and agricultural sciences. The journal encourages the submission of experimental papers that employ relevant algorithms and also welcomes survey articles in the fields of biostatistics and epidemiology.

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A SIMULATION STUDY ON DUNNETT TEST ROBUSTNESS TO GROUP SIZE AND HETEROSCEDASTICITY IN LINEAR MIXED-EFFECTS MODELS

Authors

  • Codjo Emile Agbangba
  • Sika Fidele Tchando
  • Emmanuel Ehnon Gongnet

Keywords:

Dunnett procedure, assumptions violation, test performance, control group, means comparison

DOI:

https://doi.org/10.17654/0973514324030

Abstract

In linear mixed-effects models, the Dunnett procedure is commonly employed for comparing multiple groups against a control group. While the influence of group number on the Dunnett test’s performance is well-documented, the effects of heteroscedasticity and unequal group sizes remain underexplored. This study investigates the robustness of the Dunnett test within linear mixed-effects models, particularly in the presence of heteroscedasticity, varying group sizes, and different group numbers. Through data simulation, we assessed performance using four key metrics: any-pairwise-power, all-pairwise-power, type I error rate, and false discovery rate. Our findings indicate that the procedure’s performance diminishes significantly when fewer than seven groups are involved, especially when group sizes are small and variances are heterogeneous. Under these circumstances, the type I error rate deviates from the nominal level, despite adequate power estimates being achieved.

Received: June 16, 2024
Revised: July 19, 2024
Accepted: October 23, 2024

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Published

2024-11-07

Issue

Section

Articles

How to Cite

A SIMULATION STUDY ON DUNNETT TEST ROBUSTNESS TO GROUP SIZE AND HETEROSCEDASTICITY IN LINEAR MIXED-EFFECTS MODELS. (2024). JP Journal of Biostatistics, 24(3), 555-572. https://doi.org/10.17654/0973514324030

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