Online control of the False Discovery Rate in group-sequential platform trials

Zehetmayer, Sonja; Posch, Martin; König, Franz

doi:10.1177/09622802221129051

Cited by 11 publications

(7 citation statements)

References 31 publications

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“…For further discussion and proposals for this setting, we refer the interested reader to Zrnic et al, 23 who proposed a framework for asynchronous online testing. As well, recent work by Zehetmayer et al 36 shows how to use LOND specifically for group-sequential platform trials.…”

Section: Discussionmentioning

confidence: 99%

Online error rate control for platform trials

Robertson

Wason

König

et al. 2023

Statistics in Medicine

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Platform trials evaluate multiple experimental treatments under a single master protocol, where new treatment arms are added to the trial over time. Given the multiple treatment comparisons, there is the potential for inflation of the overall type I error rate, which is complicated by the fact that the hypotheses are tested at different times and are not necessarily pre-specified. Online error rate control methodology provides a possible solution to the problem of multiplicity for platform trials where a relatively large number of hypotheses are expected to be tested over time. In the online multiple hypothesis testing framework, hypotheses are tested one-by-one over time, where at each time-step an analyst decides whether to reject the current null hypothesis without knowledge of future tests but based solely on past decisions. Methodology has recently been developed for online control of the false discovery rate as well as the familywise error rate (FWER). In this article, we describe how to apply online error rate control to the platform trial setting, present extensive simulation results, and give some recommendations for the use of this new methodology in practice. We show that the algorithms for online error rate control can have a substantially lower FWER than uncorrected testing, while still achieving noticeable gains in power when compared with the use of a Bonferroni correction. We also illustrate how online error rate control would have impacted a currently ongoing platform trial.

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Section: Discussionmentioning

confidence: 99%

Online error rate control for platform trials

Robertson

Wason

König

et al. 2023

Statistics in Medicine

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“…Stallard et al, 7 Collignon et al 9 ) and alternatives (like FDR -control) have been suggested rather recently (e.g. Zehetmayer et al, 25 Robertson et al 26 ).…”

Section: Discussionmentioning

confidence: 99%

The population-wise error rate for clinical trials with overlapping populations

Brannath

Hillner

Rohmeyer

2022

Stat Methods Med Res

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We introduce a new multiple type I error criterion for clinical trials with multiple, overlapping populations. Such trials are of interest in precision medicine where the goal is to develop treatments that are targeted to specific sub-populations defined by genetic and/or clinical biomarkers. The new criterion is based on the observation that not all type I errors are relevant to all patients in the overall population. If disjoint sub-populations are considered, no multiplicity adjustment appears necessary, since a claim in one sub-population does not affect patients in the other ones. For intersecting sub-populations we suggest to control the average multiple type I error rate, i.e. the probability that a randomly selected patient will be exposed to an inefficient treatment. We call this the population-wise error rate, exemplify it by a number of examples and illustrate how to control it with an adjustment of critical boundaries or adjusted [Formula: see text]-values. We furthermore define corresponding simultaneous confidence intervals. We finally illustrate the power gain achieved by passing from family-wise to population-wise error rate control with two simple examples and a recently suggested multiple-testing approach for umbrella trials.

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“…In addition, from a public health perspective, quantifying the risk of false positive conclusions at the platform level is essential and can be achieved with tailored, so called online multiple testing procedures. 24 , 25 …”

Section: Improving Efficiency Through Adaptive Clinical Trial Designmentioning

confidence: 99%

Current state-of-the-art and gaps in platform trials: 10 things you should know, insights from EU-PEARL

Koenig,

Spiertz,

Millar

et al. 2024

eClinicalMedicine

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Online control of the False Discovery Rate in group-sequential platform trials

Cited by 11 publications

References 31 publications

Online error rate control for platform trials

Online error rate control for platform trials

The population-wise error rate for clinical trials with overlapping populations

Current state-of-the-art and gaps in platform trials: 10 things you should know, insights from EU-PEARL

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