Futility stopping in clinical trials, optimality and practical considerations.
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ABSTRACT:
SUBMITTER: Chang Y
PROVIDER: S-EPMC7954786 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND: Many clinical trials show no overall benefit. We examined futility analyses applied to trials with different effect sizes. METHODS: Ten randomised cancer trials were retrospectively analysed; target sample size reached in all. The hazard ratio indicated no overall benefit (n=5), or moderate (n=4) or large (n=1) treatment effects. Futility analyses were applied after 25, 50 and 75% of events were observed, or patients were recruited. Outcomes were conditional power (CP), and time and cost savings. RESULTS: Futility analyses could stop some trials with no benefit, but not all. After observing 50% of the target number of events, 3 out of 5 trials with no benefit could be stopped early (low CP ≤ 15%). Trial duration for two studies could be reduced by 4-24 months, saving £44 000-231 000, but the third had already stopped recruiting, hence no savings were made. However, of concern was that 2 of the 4 trials with moderate treatment effects could be stopped early at some point, although they eventually showed worthwhile benefits. CONCLUSIONS: Careful application of futility can lead to future patients in a trial not being given an ineffective treatment, and should therefore be used more often. A secondary consideration is that it could shorten trial duration and reduce costs. However, studies with modest treatment effects could be inappropriately stopped early. Unless there is very good evidence for futility, it is often best to continue to the planned end.
| S-EPMC3464764 | biostudies-other
Project description: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.
| S-EPMC7643306 | biostudies-literature
Project description:Limiting or withdrawing nonbeneficial medical care is considered ethically responsible throughout most of critical care and medical ethics literature. Practically, however, setting limits to treatment is often challenging. We review the literature to identify best practices for using the definition of futility as an anchoring concept to aid the ethical practice of ICU clinicians.Data sourcesSource data were obtained from a PubMed literature review.Study selectionEnglish language articles were chosen based on relevance to medical futility ethics, end-of-life care in the ICU, or communication and conflict mitigation strategies.Data extractionIndependent evaluation of selected articles for recurrent content themes as relevant to our clinical case were compared among authors and based on consensus, quantitative and qualitative data from these sources were referenced directly.Data synthesisWhen life-sustaining treatment is unlikely to achieve a meaningful benefit such as symptom improvement, continued care may be discordant with the patient's goals. Institutional and cultural norms, unconscious biases, and difficulty with navigating conflicts all influence how un(comfortable) clinicians feel in setting limits to futile care. Defining futility in light of the patient's goals and values, focusing on outcomes rather than interventions, and being proactive in communication with families are the staples of medically meaningful critical care. Palliative measures should be framed affirmatively, and clinicians should be transparent about the limits of medicine.ConclusionsClinicians have an ethical obligation not to provide futile care. To practice accordingly, we must clearly understand the nature and forms of futility. Armed with this understanding, our discussions with family and surrogates in the ICU should fundamentally comprise 1) eliciting the patient's values and goals, 2) communicating which interventions serve those values and goals and which do not, and 3) offering only those interventions whose likely outcomes are in line with said values and goals.
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| S-EPMC9149588 | biostudies-literature
Project description:Master protocol, categorized as basket trial, umbrella trial or platform trial, is an innovative clinical trial framework that aims to expedite clinical drug development, enhance trial efficiency, and eventually bring medicines to patients faster. Despite a clear uptake on the advantages in the concepts and designs, master protocols are still yet to be widely used. Part of that may be due to the fact that the master protocol framework comes with the need for new statistical designs and considerations for analyses and operational challenges. In this article, we provide an overview of the master protocol framework, unify the definitions with some examples, review the statistical methods for the designs and analyses, and focus our discussions on some practical considerations and recommendations of master protocols to help practitioners better design and implement such studies.
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