Multiple Contrast Tests in the Presence of Heteroscedasticity

Hasler, Mario; Hothorn, Ludwig A.

doi:10.1002/bimj.200710466

Cited by 89 publications

(80 citation statements)

References 18 publications

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“…; P p for the treatment groups. Approximate multivariate t-distributions will be applied, based on the approach of Hasler and Hothorn [10]. Correlations among both the contrasts and the endpoints will be taken into account.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Possible slight variations around the nominal FWE α are necessarily bounded by the versions CE and BON. A naive competing approach would obviously be the application of conventional MCTs for heteroscedastic data according to Hasler and Hothorn [10] for all the endpoints separately and to adjust for the multiple endpoints by methods of Holm [20] or Hommel [21]. Expectedly, it would also realize a FWE between the versions CE and BON.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…This procedure is referred to here as the MIN procedure, indicating that minimized contrast-specific degrees of freedom are used. Considering the elements of the correlation matrix belonging to CE and MIN, one can see that the correlations of MCTs in the presence of heteroscedasticity [10] are recovered for i ¼ i 0 . Hence, the conventional case of a single endpoint (k ¼ 1) is a special case of the method described in this article.…”

Section: Test Proceduresmentioning

confidence: 99%

“…However, in contrast to the homoscedastic case, the covariance matrices here additionally are unequal for the treatment groups. For that reason, the plug-in procedure (PI) for heterogeneous variances of Hasler and Hothorn [10] will be adopted and extended to the case of multiple endpoints, as was suggested by Hasler and Hothorn [1]. Therefore, qk different approximate qk-variate t-distributions must be applied, each with a contrast-and endpoint-specific degree of freedom according to eq.…”

Section: Test Proceduresmentioning

confidence: 99%

“…; p. The most critical situation in the context of heteroscedasticity is if the treatment group with the highest variance has the smallest sample size because approaches for homoscedastic data yield very liberal test decisions in this situation (e.g. see Hasler and Hothorn [10]). For that reason, the first p À 1 treatment groups had same covariance matrices,…”

Section: Simulations Concerning the Fwementioning

confidence: 99%

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Multiple Contrast Tests for Multiple Endpoints in the Presence of Heteroscedasticity

Hasler

2014

The International Journal of Biostatistics

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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.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Test Proceduresmentioning

confidence: 99%

Section: Test Proceduresmentioning

confidence: 99%

Section: Simulations Concerning the Fwementioning

confidence: 99%

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Multiple Contrast Tests for Multiple Endpoints in the Presence of Heteroscedasticity

Hasler

2014

The International Journal of Biostatistics

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Resampling in Multiple‐Dose Factorial Designs

Frommolt

Hellmich

2009

Biometrical J

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Multiple-dose factorial designs may provide confirmatory evidence that (fixed) combination drugs are superior to either component drug alone. Moreover, a useful and safe range of dose combinations may be identified. In our study, we focus on (A) adjustments of the overall significance level made necessary by multiple testing, (B) improvement of conventional statistical methods with respect to power, distributional assumptions and dimensionality, and (C) construction of corresponding simultaneous confidence intervals. We propose novel resampling algorithms, which in a simple way take the correlation of multiple test statistics into account, thus improving power. Moreover, these algorithms can easily be extended to combinations of more than two component drugs and binary outcome data. Published data summaries from a blood pressure reduction trial are analysed and presented as a worked example. An implementation of the proposed methods is available online as an R package.

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Sample size planning for multiple contrast tests

Pöhlmann,

Konietschke

2023

Biometrical J

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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.

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Multiple Contrast Tests in the Presence of Heteroscedasticity

Cited by 89 publications

References 18 publications

Multiple Contrast Tests for Multiple Endpoints in the Presence of Heteroscedasticity

Multiple Contrast Tests for Multiple Endpoints in the Presence of Heteroscedasticity

Resampling in Multiple‐Dose Factorial Designs

Sample size planning for multiple contrast tests

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