Are Multiple Contrast Tests Superior to the ANOVA?

Konietschke, Frank; Bösiger, Sandra; Brunner, Edgar; Hothorn, Ludwig A.

doi:10.1515/ijb-2012-0020

Cited by 27 publications

(38 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The developed methods are compared with global testing procedures in extensive simulation studies. It should be noted that multiple comparisons and global testing procedures are basically not comparable at hand, 8 because global testing methods are quadratic, while the multiple contrast test consists of linear statistics. However, both methods can be used to test the same global null hypothesis and their properties can be compared in simulation studies.…”

Section: States That "Multiple Observers Are Needed To Document and Amentioning

confidence: 99%

Simultaneous inference for factorial multireader diagnostic trials

Konietschke

Aguayo

Staab

2017

Statistics in Medicine

Self Cite

View full text Add to dashboard Cite

We study inference methods for the analysis of multireader diagnostic trials. In these studies, data are usually collected in terms of a factorial design involving the factors Modality and Reader. Furthermore, repeated measures appear in a natural way since the same patient is observed under different modalities by several readers and the repeated measures may have a quite involved dependency structure. The hypotheses are formulated in terms of the areas under the ROC curves. Currently, only global testing procedures exist for the analysis of such data. We derive rank-based multiple contrast test procedures and simultaneous confidence intervals which take the correlation between the test statistics into account. The procedures allow for testing arbitrary multiple hypotheses. Extensive simulation studies show that the new approaches control the nominal type 1 error rate very satisfactorily. A real data set illustrates the application of the proposed methods.

show abstract

Section: States That "Multiple Observers Are Needed To Document and Amentioning

confidence: 99%

Simultaneous inference for factorial multireader diagnostic trials

Konietschke

Aguayo

Staab

2017

Statistics in Medicine

Self Cite

View full text Add to dashboard Cite

show abstract

“…To this end we investigate the respective statistical powers of ANOM and ANOVA for different configurations of the alternative. To ensure a somewhat 'fair' comparison, we follow the approach of Hayter and Liu (1990) and Konietschke et al (2013), who identified the so-called least favorable configurations (LFCs).…”

Section: Powermentioning

confidence: 99%

Analysis of means: a generalized approach using R

Pallmann

Hothorn

2016

Journal of Applied Statistics

Self Cite

View full text Add to dashboard Cite

Papers on the analysis of means (ANOM) have been circulating in the quality control literature for decades, routinely describing it as a statistical stand-alone concept. Therefore we clarify that ANOM should rather be regarded as a special case of a much more universal approach known as multiple contrast tests (MCTs). Perceiving ANOM as a grand-mean-type MCT paves the way for implementing it in the opensource software R. We give a brief tutorial on how to exploit R's versatility and introduce R package ANOM for drawing the familiar decision charts. Beyond that, we illustrate two practical aspects of data analysis with ANOM: firstly, we compare merits and drawbacks of ANOM-type MCTs and ANOVA F-test and assess their respective statistical powers, and secondly, we show that the benefit of using critical values from multivariate t-distributions for ANOM instead of simple Bonferroni quantiles is oftentimes negligible.

show abstract

“…Thus, the power of both tests are quite different for different alternatives Konietschke et al (2013). The F-test is a global test only, whereas Dunnett's test provides both a global test decision for any difference-to-control, and local information regarding which particular difference-to-control is significant (by means of either multiplicity-adjusted p-values or simultaneous confidence intervals).…”

Section: Decision-tree Approach Vs Dunnett's Testmentioning

confidence: 99%