arXiv subjects

Remi Luschei

Publications and source records attributed to Remi Luschei.

4 recordsLinked to original sources

arXiv2026

Multiple type I error concepts for clinical trials with overlapping populations

The population-wise error rate (PWER) was introduced as a more liberal alternative to the family-wise error rate (FWER) for clinical trials with multiple, overlapping patient populations. These trials are particularly relevant in personalized medicine, which aims to find therapies tailored to specific patient subgroups. By controlling an average multiple type I error probability over all population strata, the PWER can substantially improve statistical power. However, one disadvantage of this concept is that the error probability for a given population can strongly depend on the presence or absence of other populations included in the analysis. To address this issue, we propose two modifications of the PWER that enforce individual error control either for all target populations, or for all possible unions of target populations. We call these approaches the PWER over the populations (PWER-P) and the PWER over population unions (PWER-U). We investigate the properties of these new error rates and compare them with the PWER and FWER in terms of type I error control and power.

stat.ME↗

arXiv2026

A prediction interval for the population-wise error rate

We construct an asymptotic prediction interval for the population-wise error rate (PWER), which is a multiple type I error criterion for clinical trials with overlapping patient populations. The PWER is the probability that a randomly selected patient will receive an ineffective treatment. It must usually be estimated due to unknown population strata sizes, such that only an estimate can be controlled at the given significance level. We apply the delta method to find a prediction interval for the resulting true PWER, we demonstrate by simulations that the interval has the required coverage probability, and illustrate the approach with real data examples.

stat.ME↗

arXiv2025

Family-wise error rate control in clinical trials with overlapping populations

We consider clinical trials with multiple, overlapping patient populations, that test multiple treatment policies specifically tailored to these populations. Such designs may lead to multiplicity issues, as false statements will affect several populations. For type I error control, often the family-wise error rate (FWER) is controlled, which is the probability to reject at least one true null hypothesis. If the joint distribution of the test statistics is known, the FWER level can be exhausted by determining critical values or adjusted $\alpha$-levels. The adjustment is typically done under the common ANOVA assumptions. However, the performed tests are then only valid under the rather strong assumption of homogeneous null effects, i.e., when the null hypothesis applies to all subpopulations and their intersections. We show that under cancelling null effects, when heterogeneous effects cancel out in some or all subpopulations, this procedure does not provide FWER control. We also suggest different alternatives and compare them in terms of FWER control and their power.

stat.ME↗

arXiv2023

The effect of estimating prevalences on the population-wise error rate

The population-wise error rate (PWER) is a type I error rate for clinical trials with multiple target populations. In such trials, a treatment is tested for its efficacy in each population. The PWER is defined as the probability that a randomly selected, future patient will be exposed to an inefficient treatment based on the study results. It can be understood and computed as an average of strata-specific family wise error rates and involves the prevalences of these strata. A major issue of this concept is that the prevalences are usually unknown in practice, so that the PWER cannot be directly controlled. Instead, one could use an estimator based on the given sample, like their maximum-likelihood estimator under a multinomial distribution. In this article, we demonstrate through simulations that this does not substantially inflate the true PWER. We differentiate between the expected PWER, which is almost perfectly controlled, and study-specific values of the PWER which are conditioned on all subgroup sample sizes and vary within a narrow range. Thereby, we consider up to eight different overlapping populations and moderate to large sample sizes. In these settings, we also consider the maximum strata-wise family wise error rate, which is found to be, on average, at least bounded by twice the significance level used for PWER control.

stat.ME↗