arXiv subjects

Liane Kluge

Publications and source records attributed to Liane Kluge.

4 recordsLinked to original sources

arXiv2026

Principles in harmony: Closed testing meets the partitioning principle for computational efficiency

We explore and utilize the algorithmic relationship between the closed testing principle for multiple tests with family-wise error rate (FWER) control and the partitioning principle for the construction of simultaneous confidence intervals. Starting with the simple observation that a multiple test with FWER control is formally equivalent to a one-sided simultaneous confidence interval for the vector of binary parameter indicating whether the null or alternative hypothesis is true, we show that the closed testing and partitioning principles follow the same computational approach. We will then utilise this relationship to extend concepts of consonance for closed tests to the partitioning principle, with the aim of deriving computationally feasible and efficient algorithms for the calculation of simultaneous confidence intervals. We will also utilize the relationship between closed testing and partitioning principle to extend common closed testing procedures to simultaneous confidence intervals, referencing the existing literature on informative simultaneous confidence intervals. The relationships and extensions will be illustrated by simple, instructive examples.

stat.ME↗

arXiv2026

Informative Simultaneous Confidence Intervals for Graphical Group Sequential Test Procedures

Test procedures for multiple hypotheses in a group sequential clinical trial that control the family-wise error rate are considered. Several graphical group sequential tests suggested in the literature, which are special cases of Bonferroni-closure tests, are discussed. The focus is on the question of whether to consider at the current stage only the evidence of the current repeated p-value or the evidence over all repeated p-values from the previous stages. A new test strategy controlling the family-wise error rate is introduced that consistently works across all hypotheses, with the evidence (i.e., repeated p-value) from the current stage. The strategy is more powerful than similar previously suggested test procedures. This is achieved by using the evidence from previous stages to increase the significance levels. For the test procedures, corresponding compatible simultaneous confidence intervals are presented, having the disadvantage of often not providing additional information on the treatment effects. For this reason, we extend previous work about informative simultaneous confidence intervals for one-stage graphical tests to graphical group sequential trials. Iterative algorithms are introduced that calculate these informative bounds that have a small power loss compared to the original graphical group sequential test. The boundaries can be calculated after each stage. In addition, previous work is extended by a criterion to estimate the accuracy of the numerically calculated boundaries. The suggested informative bounds can be used to provide median-conservative, i.e., reliable estimators, for estimating the treatment effects in a group sequential test with multiple hypotheses.

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arXiv2025

Adaptive Designs in Fast-Track Registration Processes for Digital Health Applications

Fast-track procedures play an important role in the context of conditional registration of medical devices, such as listing processes for digital health applications. They offer the potential for earlier patient access to innovative products and involve two registration steps. The applicants can apply first for conditional registration. A successful conditional registration provides a limited funding or approval period and time to prepare the application for permanent registration (the second registration step). For conditional registration, products have to fulfill only a part of the requirements necessary for permanent registration. There is interest in valid and efficient study designs for fast-track procedures. This will be addressed in this paper. A motivating example is the German fast-track registration process of digital health applications (DiGA) for reimbursement by statutory health insurances. The main focus of the paper is the systematic statistical investigation of the utility of adaptive designs in the context of fast-track registration processes like the DiGA fast-track. We demonstrate that, in most cases, such designs are much more efficient than the current standard of two separate studies. A careful statistical discussion of the registration requirements and their consequences is also included. The results are based on numerical calculations supported by mathematical arguments.

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arXiv2024

Informative Simultaneous Confidence Intervals for Graphical Test Procedures

Simultaneous confidence intervals (SCIs) that are compatible with a given closed test procedure are often non-informative. More precisely, for a one-sided null hypothesis, the bound of the SCI can stick to the border of the null hypothesis, irrespective of how far the point estimate deviates from the null hypothesis. This has been illustrated for the Bonferroni-Holm and fall-back procedures, for which alternative SCIs have been suggested, that are free of this deficiency. These informative SCIs are not fully compatible with the initial multiple test, but are close to it and hence provide similar power advantages. They provide a multiple hypothesis test with strong family-wise error rate control that can be used in replacement of the initial multiple test. The current paper extends previous work for informative SCIs to graphical test procedures. The information gained from the newly suggested SCIs is shown to be always increasing with increasing evidence against a null hypothesis. The new SCIs provide a compromise between information gain and the goal to reject as many hypotheses as possible. The SCIs are defined via a family of dual graphs and the projection method. A simple iterative algorithm for the computation of the intervals is provided. A simulation study illustrates the results for a complex graphical test procedure.

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