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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…

Methodology · Statistics 2026-02-09 Remi Luschei , Werner Brannath

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…

Methodology · Statistics 2025-11-13 Remi Luschei , Werner Brannath

We introduce a new multiple type I error criterion for clinical trials with multiple populations. Such trials are of interest in precision medicine where the goal is to develop treatments that are targeted to specific sub-populations…

Methodology · Statistics 2021-02-05 Werner Brannath , Charlie Hillner , Kornelius Rohmeyer

Background: Experimental treatments pass through various stages of development. If a treatment passes through early phase experiments, the investigators may want to assess it in a late phase randomised controlled trial. An efficient way to…

Clinical study populations often differ meaningfully from the broader populations to which results are intended to generalize. Weighting methods such as inverse probability of sampling weights (IPSW) reweight study participants to resemble…

Methodology · Statistics 2025-12-02 William Stewart , Carly L. Brantner , Elizabeth A. Stuart , Laine Thomas

Randomized controlled trials (RCTs) provide strong internal validity compared with observational studies. However, selection bias threatens the external validity of randomized trials. Thus, RCT results may not apply to either broad public…

Methodology · Statistics 2017-04-26 Ziyue Chen , Eloise Kaizar

There has been a misconception that only one type of error rate control is necessary in clinical trials, leading to debates over whether to prioritize Familywise Error Rate (FWER) or False Discovery Rate (FDR). This misconception has led to…

Methodology · Statistics 2026-03-26 Xinping Cui , Emily Ouyang , Yi Liu , Jingjing Yan Schneider , Hong Tian , Bushi Wang , Jason C. Hsu

Causal inference in a program evaluation setting faces the problem of external validity when the treatment effect in the target population is different from the treatment effect identified from the population of which the sample is…

Methodology · Statistics 2021-12-23 Kyungchul Song , Zhengfei Yu

In observational studies, the recorded treatment assignment is not purely random, but it is influenced by external factors such as patient characteristics, reimbursement policies, and existing guidelines. Therefore, the treatment effect can…

Methodology · Statistics 2024-09-02 Sara Poletto , Enrico Longato , Erica Tavazzi , Martina Vettoretti

When simultaneously testing multiple hypotheses, the usual approach in the context of confirmatory clinical trials is to control the familywise error rate (FWER), which bounds the probability of making at least one false rejection. In many…

Methodology · Statistics 2021-05-20 David S. Robertson , James M. S. Wason , Frank Bretz

We propose a method for multiple hypothesis testing with familywise error rate (FWER) control, called the i-FWER test. Most testing methods are predefined algorithms that do not allow modifications after observing the data. However, in…

Methodology · Statistics 2021-04-20 Boyan Duan , Aaditya Ramdas , Larry Wasserman

Epidemiologic screening programs often make use of tests with small, but non-zero probabilities of misdiagnosis. In this article, we assume the target population is finite with a fixed number of true cases, and that we apply an imperfect…

Methodology · Statistics 2024-04-22 Lin Ge , Yuzi Zhang , Lance A. Waller , Robert H. Lyles

The progression-free survival ratio (PFSr) is a widely used measure in personalized oncology trials. It evaluates the effectiveness of treatment by comparing two consecutive event times - one under standard therapy and one under an…

Methodology · Statistics 2025-12-23 Merle Munko , Simon Mack , Marc Ditzhaus , Stefan Fröhling , Dennis Dobler , Dominic Edelmann

There is a growing interest in the implementation of platform trials, which provide the flexibility to incorporate new treatment arms during the trial and the ability to halt treatments early based on lack of benefit or observed…

Methodology · Statistics 2023-08-25 Peter Greenstreet , Thomas Jaki , Alun Bedding , Pavel Mozgunov

Biological research often involves testing a growing number of null hypotheses as new data is accumulated over time. We study the problem of online control of the familywise error rate (FWER), that is testing an apriori unbounded sequence…

Methodology · Statistics 2020-03-10 Jinjin Tian , Aaditya Ramdas

In online multiple testing, an a priori unknown number of hypotheses are tested sequentially, i.e. at each time point a test decision for the current hypothesis has to be made using only the data available so far. Although many powerful…

Methodology · Statistics 2025-03-11 Vincent Jankovic , Lasse Fischer , Werner Brannath

Randomized Controlled Trials (RCTs) may suffer from limited scope. In particular, samples may be unrepresentative: some RCTs over- or under- sample individuals with certain characteristics compared to the target population, for which one…

Methodology · Statistics 2024-03-15 Bénédicte Colnet , Julie Josse , Gaël Varoquaux , Erwan Scornet

Platform trials offer a framework to study multiple interventions in a single trial with the opportunity of opening and closing arms. The use of a common control in platform trials can increase efficiency as compared to individual control…

Methodology · Statistics 2023-02-10 Quynh Nguyen , Katharina Hees , Benjamin Hofner

The topic of multiple hypotheses testing now has a potpourri of novel theories and ubiquitous applications in diverse scientific fields. However, the universal utility of this field often hinders the possibility of having a generalized…

Statistics Theory · Mathematics 2025-04-25 Monitirtha Dey , Subir Kumar Bhandari

Estimating the prevalence of a disease is necessary for evaluating and mitigating risks of its transmission within or between populations. Estimates that consider how prevalence changes with time provide more information about these risks…

Applications · Statistics 2021-11-12 Braden Scherting , Alison Peel , Raina Plowright , Andrew Hoegh
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