ArticleBMC medical research methodology2020
Optimality criteria for futility stopping boundaries for group sequential designs with a continuous endpoint.
Article in BMC medical research methodology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Automated Reminders Enhance Mailed Fecal Immunochemical Test Completion Among Veterans: a Randomized Controlled Trial.Journal of general internal medicine · 2024Trial
- Benefits of group sequential design and sample size re-estimation for randomised controlled trials evaluating the prevention of ventilator-associated pneumonia: a simulation study informed by real world data.BMC medical research methodology · 2025Article
- Futility Interim Analyses - A Plea for Simplicity.Therapeutic innovation & regulatory science · 2025Article
- A Preplanned Multi-Stage Platform Trial for Discovering Multiple Superior Treatments With Control of FWER and Power.Biometrical journal. Biometrische Zeitschrift · 2025Article
- Using Bayesian statistics in confirmatory clinical trials in the regulatory setting: a tutorial review.BMC medical research methodology · 2024Review
- Optimal futility stopping boundaries for binary endpoints.BMC medical research methodology · 2024Article
- Futility monitoring for randomized clinical trials with non-proportional hazards: An optimal conditional power approach.Clinical trials (London, England) · 2023Article
- Correction to: Optimality criteria for futility stopping boundaries for group sequential designs with a continuous endpoint.BMC medical research methodology · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn clinical trials with fixed study designs, statistical inference is only made when the trial is completed. In contrast, group sequential designs allow an early stopping of the trial at interim, either for efficacy when the treatment effect is significant or for futility when the treatment effect seems too small to justify a continuation of the trial. Efficacy stopping boundaries based on alpha spending functions have been widely discussed in the statistical literature, and there is also solid work on the choice of adequate futility stopping boundaries. Still, futility boundaries are often chosen with little or completely without theoretical justification, in particular in investigator initiated trails. Some authors contributed to fill this gap. In here, we rely on an idea of Schüler et al. (2017) who discuss optimality criteria for futility boundaries for the special case of trials with (multiple) time-to-event endpoints. Their concept can be adopted to define "optimal" futility boundaries (with respect to given performance indicators) for continuous endpoints.
methodsWe extend Schülers' definition for "optimal" futility boundaries to the most common study situation of a single continuous primary endpoint compared between two groups. First, we introduce the analytic algorithm to derive these futility boundaries. Second, the new concept is applied to a real clinical trial example. Finally, the performance of a study design with an "optimal" futility boundary is compared to designs with arbitrarily chosen futility boundaries.
resultsThe presented concept of deriving futility boundaries allows to control the probability of wrongly stopping for futility, that means stopping for futility even if the treatment effect is promizing. At the same time, the loss in power is also controlled by this approach. Moreover, "optimal" futility boundaries improve the probability of correctly stopping for futility under the null hypothesis of no difference between two groups.
conclusionsThe choice of futility boundaries should be thoroughly investigated at the planning stage. The sometimes met, arbitrary choice of futility boundaries can lead to a substantial negative impact on performance. Applying futility boundaries with predefined optimization criteria increases efficiency of group sequential designs. Other optimization criteria than proposed in here might be incorporated.
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