A Dunnett-Type Procedure for Multiple Endpoints

Hasler, Mario; Hothorn, Ludwig A.

doi:10.2202/1557-4679.1258

Cited by 25 publications

(36 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As already noted in Hasler and Hothorn [1], the following procedures could also be formulated in terms of ratios of means, see Hasler and Hothorn [3] as an example. The aim is to test the hypotheses…”

Section: Testing Problem and Test Procedures 21 Testing Problemmentioning

confidence: 99%

“…This procedure is a direct generalization of the procedure by Hasler and Hothorn [1], and it is referred to here as the HOM procedure. Because it does not take the real nature of the data into account, it is clear that the HOM procedure does not control the FWE if the assumption of equal covariance matrices is violated.…”

Section: Test Proceduresmentioning

confidence: 99%

“…Hasler and Hothorn [1] have described an extension of the Dunnett procedure [2] to the case of multiple, correlated endpoints. The focus is on simultaneous confidence intervals (SCIs) for differences of means.…”

Section: Introductionmentioning

confidence: 99%

“…This is not fulfilled in situations when variances or correlations differ due to the treatment groups. This problem is briefly addressed by Hasler and Hothorn [1,3], but a solution is only suggested. In addition, the suggestions made in these two papers are slightly different.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, manyto-one comparisons based on Dunnett [2], or trend test based on Williams [4] and Bretz [5] are special cases of multiple contrast tests (MCTs), which allow the evaluation of a broad class of linear testing problems, such as the all-pair comparison of Tukey [8], or any user-defined contrast tests. This article presents an extension of the methods of Hasler and Hothorn [1,3] to the general case of MCTs for multivariate normally distributed endpoints with unequal covariance matrices for the treatment groups. Former suggestions are considered in detail.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Multiple Contrast Tests for Multiple Endpoints in the Presence of Heteroscedasticity

Hasler

2014

The International Journal of Biostatistics

Self Cite

View full text Add to dashboard Cite

This article describes an extension of multiple contrast tests to the case of multiple, correlated endpoints. These endpoints are assumed to be normally distributed with different scales and variances. Unlike in previous articles, the covariance matrices are also assumed to be unequal for the treatment groups. Approximate multivariate t-distributions are used to obtain multiplicity-adjusted p-values and quantiles for test decisions or simultaneous confidence intervals. Simulation results show that this approach controls the family-wise error type I over both the comparisons and the endpoints in an admissible range. The approach will be applied to a semi-synthetic example data set of a randomized, placebo-controlled phase IIb dose-finding study of a novel anti-muscarinic agent for five continuous endpoints. A related R package is available.

show abstract

Section: Testing Problem and Test Procedures 21 Testing Problemmentioning

confidence: 99%

Section: Test Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multiple Contrast Tests for Multiple Endpoints in the Presence of Heteroscedasticity

Hasler

2014

The International Journal of Biostatistics

Self Cite

View full text Add to dashboard Cite

show abstract

Sample size planning for multiple contrast tests

Pöhlmann,

Konietschke

2023

Biometrical J

View full text Add to dashboard Cite

Sample size calculations for two (independent) samples are well established and applied in (pre‐)clinical research. When planning several samples, which is common in, for example, preclinical studies, sample size planning tools based on analysis of variance methods are available. Since the underlying effect sizes of these methods are often hard to interpret and to provide for the sample size planning, we employ multiple contrast test procedures for sample size computations in both parametric (under normality assumption) and nonparametric designs using Steel‐type tests. Since the exact distributions of the test statistics are unknown under the alternative and variance heterogeneity, we use approximate solutions. Furthermore, since no closed formula for the sample size is available, we use numerical approximations for their computation. Extensive simulation studies are finally conducted to assess the quality of the approximations. It turns out that the methods are accurate in the sense that the multiple contrast test procedures reach the target power to detect the alternative of interest with the sample size computed. The developed procedures are a valuable tool to plan (pre‐)clinical trials with several samples and are easily accessible in publicly available software.

show abstract

Multiplicity adjustments for the Dunnett procedure under heterokcedasticity

Tamhane,

2023

Biometrical J

View full text Add to dashboard Cite

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.

show abstract

A Dunnett-Type Procedure for Multiple Endpoints

Cited by 25 publications

References 24 publications

Multiple Contrast Tests for Multiple Endpoints in the Presence of Heteroscedasticity

Multiple Contrast Tests for Multiple Endpoints in the Presence of Heteroscedasticity

Sample size planning for multiple contrast tests

Multiplicity adjustments for the Dunnett procedure under heterokcedasticity

Contact Info

Product

Resources

About