Speaker
Description
We consider the problem of testing multiple null hypotheses, where a decision to reject or retain must be made for each one and embedding incorrect decisions into a real life context may inflict different losses. We argue that traditional methods controlling the Type I error rate may be too restrictive in this situation and that the standard familywise error rate may not be appropriate. For example, when disjoint sub-populations are considered, no multiplicity adjustment appears necessary, since a claim in one sub-population does not affect patients in another. Maurer et al. (2023) formalized this perspective by introducing familywise expected loss control by defining suitable loss functions for a given decision rule, where incorrect decisions can be treated unequally by assigning different loss values. For intersecting sub-populations, Brannath et al. (2023) proposed the population-wise error rate, defined as the probability that a randomly selected patient will be exposed to an inefficient treatment. In this talk, we review these approaches, discuss their connections, and explore possible extensions based on the generalized closed testing procedure recently introduced by Xu et al. (2025).
References
Brannath, Hillner, Rohmeyer (2023). The population-wise error rate for clinical trials with overlapping populations. Statistical Methods in Medical Research 32:334–352.
Maurer, Bretz, Xun (2023). Optimal test procedures for multiple hypotheses controlling the familywise expected loss (with Discussion). Biometrics 79:2781–2793.
Xu, Solari, Fischer, de Heide, Ramdas, Goeman (2025). Bringing Closure to False Discovery Rate Control: A General Principle for Multiple Testing. arXiv:2509.02517v1 [stat.ME]
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