Simultaneous inference for factorial multireader diagnostic trials

Abstract

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.

Inference for all variants of the multivariate coefficient of variation in factorial designs

“…Furthermore, we provided a post hoc strategy for a more in-depth analysis. Therefore, we combined the well-known tool of multiple contrast tests (Mukerjee et al, 1987;Bretz et al, 2001;Konietschke et al, 2018;Umlauft et al, 2019;Gunawardana and Konietschke, 2019;Wechsung and Konietschke, 2021) with our theoretical results. This resulted into asymptotically valid tests, which are rather liberal for small sample sizes, and a novel pooled bootstrap strategy.…”

“…Since Bonferroni correction leads partially to a significant power loss, strategies incorporating the concrete dependence structure are preferred. Therefor, multiple contrast tests and corresponding simultaneous confidence intervals are well established for means (Mukerjee et al, 1987;Bretz et al, 2001), the relative treatment effect (Umlauft et al, 2019;Gunawardana and Konietschke, 2019), and the area under the receiving operating curve (Konietschke et al, 2018;Wechsung and Konietschke, 2021). A respective multiple strategy for the MCV is missing.…”