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

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.

Authors

Publication Details

Journal
JP Journal of Biostatistics
Published
2024-11-22
DOI
https://doi.org/10.17654/0973514324030
Primary Topic
Statistical Methods in Clinical Trials
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Emmanuel Ehnon Gongnet, Codjo Emile Agbangba, Sika Fidele Tchando
JP Journal of Biostatistics
Statistical Methods in Clinical Trials
article

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

Emmanuel Ehnon Gongnet, Codjo Emile Agbangba, Sika Fidele Tchando
article en

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.

JP Journal of BiostatisticsVol. 24(3)
Peace, Justice and strong institutions
Openalex Percentile: Top 78%
Statistical Methods in Clinical Trials
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.