Multiplicity adjustments for the Dunnett procedure under heterokcedasticity
Year: 2023
Type: article
Abstract: We give a simulation-based method for computing the multiplicity adjusted p-values and critical constants for the Dunnett procedure for comparing treatments with a control under heteroskedasticity. The Welch-Satterthwaite test statistics used in this procedure do not have a simple multivariate t-distribution because their denominators are mixtures of chi-squares and are correlated because of the common control treatment sample variance present in all denominators. The joint distribution of the denominators of the test statistics is approximated by correlated chi-square variables and is generated using a novel algorithm proposed in this paper. This approximation is used to derive critical constants or adjusted p-values. The familywise error rate (FWER) of the proposed method is compared with some existing methods via simulation under different heteroskedastic scenarios. The results show that our proposed method controls the FWER most accurately, whereas other methods are either too conservative or liberal or control the FWER less accurately. The different methods considered are illustrated on a real data set.
Source: Biometrical Journal
Authors: Ajit C. Tamhane, Dong Xi
Institutions: Northwestern University, Gilead Sciences (United States)
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,120
OpenAlex ID: W4387326879