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Background Many recent Stroke trials fail to show a beneficial effect of the intervention late in the development. Currently a large number of new treatment options are being developed. Multi-arm multi-stage (MAMS) designs offer one potential strategy to avoid lengthy studies of treatments without beneficial effects while at the same time allowing evaluation of several novel treatments. In this paper we provide a review of what MAMS designs are and argue that they are of particular value for Stroke trials. We illustrate this benefit through a case study based on previous published trials of endovascular treatment for acute ischemic stroke. We show in this case study that MAMS trials provide additional power for the same sample size compared to alternative trial designs. This level of additional power depends on the recruitment length of the trial, with most efficiency gained when recruitment is relatively slow. We conclude with a discussion of additional considerations required when starting a MAMS trial. Conclusion MAMS trial designs are potentially very useful for stroke trials due to their improved statistical power compared to the traditional approach.

Adaptive designs for clinical trials permit alterations to a study in response to accumulating data in order to make trials more flexible, ethical, and efficient. These benefits are achieved while preserving the integrity and validity of the trial, through the pre-specification and proper adjustment for the possible alterations during the course of the trial. Despite much research in the statistical literature highlighting the potential advantages of adaptive designs over traditional fixed designs, the uptake of such methods in clinical research has been slow. One major reason for this is that different adaptations to trial designs, as well as their advantages and limitations, remain unfamiliar to large parts of the clinical community. The aim of this paper is to clarify where adaptive designs can be used to address specific questions of scientific interest; we introduce the main features of adaptive designs and commonly used terminology, highlighting their utility and pitfalls, and illustrate their use through case studies of adaptive trials ranging from early-phase dose escalation to confirmatory phase III studies.

Background Late-phase platform protocols (including basket, umbrella, multi-arm multi-stage (MAMS), and master protocols) are generally agreed to be more efficient than traditional two-arm clinical trial designs but are not extensively used. We have gathered the experience of running a number of successful platform protocols together to present some operational recommendations. Methods Representatives of six UK clinical trials units with experience in running late-phase platform protocols attended a 1-day meeting structured to discuss various practical aspects of running these trials. We report and give guidance on operational aspects which are either harder to implement compared to a traditional late-phase trial or are specific to platform protocols. Results We present a list of practical recommendations for trialists intending to design and conduct late-phase platform protocols. Our recommendations cover the entire life cycle of a platform trial: from protocol development, obtaining funding, and trial set-up, to a wide range of operational and regulatory aspects such as staffing, oversight, data handling, and data management, to the reporting of results, with a particular focus on communication with trial participants and stakeholders as well as public and patient involvement. Discussion Platform protocols enable many questions to be answered efficiently to the benefit of patients. Our practical lessons from running platform trials will support trial teams in learning how to run these trials more effectively and efficiently.

Background Multi-arm multi-stage (MAMS) randomised trial designs have been proposed to evaluate multiple research questions in the confirmatory setting. In designs with several interventions, such as the 8-arm 3-stage ROSSINI-2 trial for preventing surgical wound infection, there are likely to be strict limits on the number of individuals that can be recruited or the funds available to support the protocol. These limitations may mean that not all research treatments can continue to accrue the required sample size for the definitive analysis of the primary outcome measure at the final stage. In these cases, an additional treatment selection rule can be applied at the early stages of the trial to restrict the maximum number of research arms that can progress to the subsequent stage(s). This article provides guidelines on how to implement treatment selection within the MAMS framework. It explores the impact of treatment selection rules, interim lack-of-benefit stopping boundaries and the timing of treatment selection on the operating characteristics of the MAMS selection design. Methods We outline the steps to design a MAMS selection trial. Extensive simulation studies are used to explore the maximum/expected sample sizes, familywise type I error rate (FWER), and overall power of the design under both binding and non-binding interim stopping boundaries for lack-of-benefit. Results Pre-specification of a treatment selection rule reduces the maximum sample size by approximately 25% in our simulations. The familywise type I error rate of a MAMS selection design is smaller than that of the standard MAMS design with similar design specifications without the additional treatment selection rule. In designs with strict selection rules - for example, when only one research arm is selected from 7 arms - the final stage significance levels can be relaxed for the primary analyses to ensure that the overall type I error for the trial is not underspent. When conducting treatment selection from several treatment arms, it is important to select a large enough subset of research arms (that is, more than one research arm) at early stages to maintain the overall power at the pre-specified level. Conclusions Multi-arm multi-stage selection designs gain efficiency over the standard MAMS design by reducing the overall sample size. Diligent pre-specification of the treatment selection rule, final stage significance level and interim stopping boundaries for lack-of-benefit are key to controlling the operating characteristics of a MAMS selection design. We provide guidance on these design features to ensure control of the operating characteristics.

Background Parallel-group multi-arm trials are randomised controlled trials (RCTs) where participants are allocated to three or more concurrent treatment groups. Multiplicity occurs when several statistical tests are conducted within the same study. Statistical adjustments to the design and analysis of multi-arm trials can be used to control the study-wise type I error rate. There is no clear guidance or consensus on the necessity of multiplicity adjustment in multi-arm trials, nor on which methods are most appropriate. This comprehensive review aimed to investigate the design, analysis and reporting of publicly funded parallel-group multi-arm trials and to report the approach to multiplicity in these trials with respect to sample size and statistical analysis. Methods We searched the United Kingdom’s National Institute for Health and Care Research (NIHR) online Journals Library, from 1 January 1997 to 31 December 2024 for reports of multi-arm RCTs. Information on the trial characteristics, the sample size estimation and analysis of the primary outcome was extracted. Two researchers conducted the search and selected reports for inclusion. Data from each report was independently extracted by two reviewers, and any disagreement was resolved by discussion. Results A total of 2452 reports, published online in the NIHR Journals Library, were screened for eligibility; 97 reports of multi-arm parallel-group trials met the inclusion criteria. Of these, 90 included the results of a multi-arm efficacy analysis. In the review, 35% (34/97) of the trials did adjust for multiplicity in the sample size calculation; in 84% (76/90), the potential between-arm comparisons were described in the methods, and 37% (33/90) made a multiplicity adjustment in the analysis. A further 86% (77/86) reported 95% confidence intervals. For the minority of multi-arm trials that did adjust for multiplicity, the most common adjustment method was Bonferroni. Conclusions The majority of the publicly funded multi-arm trials did not adjust for multiplicity in the sample size, statistical analysis, or estimation of confidence intervals. Researchers should follow the Consolidated Standards of Reporting Trials guidelines for multi-arm trials and clearly state in protocols and trial reports whether a multiplicity adjustment was made or provide a reason if no adjustment was made.

Background Bayesian adaptive designs can be more efficient than traditional methods for multi-arm randomised controlled trials. The aim of this work was to demonstrate how Bayesian adaptive designs can be constructed for multi-arm phase III clinical trials and assess potential benefits that these designs offer. Methods We constructed several alternative Bayesian adaptive designs for the Collaborative Ankle Support Trial (CAST), which was a randomised controlled trial that compared four treatments for severe ankle sprain. These designs incorporated response adaptive randomisation (RAR), arm dropping, and early stopping for efficacy or futility. We studied the operating characteristics of the Bayesian designs via simulation. We then virtually re-executed the trial by implementing the Bayesian adaptive designs using patient data sampled from the CAST study to demonstrate the practical applicability of the designs. Results We constructed five Bayesian adaptive designs, each of which had high power and recruited fewer patients on average than the original designs target sample size. The virtual executions showed that most of the Bayesian designs would have led to trials that declared superiority of one of the interventions over the control. Bayesian adaptive designs with RAR or arm dropping were more likely to allocate patients to better performing arms at each interim analysis. Similar estimates and conclusions were obtained from the Bayesian adaptive designs as from the original trial. Conclusions Using CAST as an example, this case study shows how Bayesian adaptive designs can be constructed for phase III multi-arm trials using clinically relevant decision criteria. These designs demonstrated that they can potentially generate earlier results and allocate more patients to better performing arms. We recommend the wider use of Bayesian adaptive approaches in phase III clinical trials. Trial registration CAST study registration ISRCTN, ISRCTN37807450 . Retrospectively registered on 25 April 2003.

Background Multi-arm trials enable the evaluation of multiple treatments within a single trial. They provide a way of substantially increasing the efficiency of the clinical development process. However, since multi-arm trials test multiple hypotheses, some regulators require that a statistical correction be made to control the chance of making a type-1 error (false-positive). Several conflicting viewpoints are expressed in the literature regarding the circumstances in which a multiple-testing correction should be used. In this article we discuss these conflicting viewpoints and review the frequency with which correction methods are currently used in practice. Methods We identified all multi-arm clinical trials published in 2012 by four major medical journals. Summary data on several aspects of the trial design were extracted, including whether the trial was exploratory or confirmatory, whether a multiple-testing correction was applied and, if one was used, what type it was. Results We found that almost half (49%) of published multi-arm trials report using a multiple-testing correction. The percentage that corrected was higher for trials in which the experimental arms included multiple doses or regimens of the same treatments (67%). The percentage that corrected was higher in exploratory than confirmatory trials, although this is explained by a greater proportion of exploratory trials testing multiple doses and regimens of the same treatment. Conclusions A sizeable proportion of published multi-arm trials do not correct for multiple-testing. Clearer guidance about whether multiple-testing correction is needed for multi-arm trials that test separate treatments against a common control group is required.

Background: Molecular characteristics of cancer vary between individuals. In future, most trials will require assessment of biomarkers to allocate patients into enriched populations in which targeted therapies are more likely to be effective. The MRC FOCUS3 trial is a feasibility study to assess key elements in the planning of such studies. Patients and Methods: Patients with advanced colorectal cancer were registered from 24 centres between February 2010 and April 2011. With their consent, patients’ tumour samples were analysed for KRAS/BRAF oncogene mutation status and topoisomerase 1 (topo-1) immunohistochemistry. Patients were then classified into one of four molecular strata; within each strata patients were randomised to one of two hypothesis-driven experimental therapies or a common control arm (FOLFIRI chemotherapy). A 4-stage suite of patient information sheets (PISs) was developed to avoid patient overload. Results: A total of 332 patients were registered, 244 randomised. Among randomised patients, biomarker results were provided within 10 working days (w.d.) in 71%, 15 w.d. in 91% and 20 w.d. in 99%. DNA mutation analysis was 100% concordant between two laboratories. Over 90% of participants reported excellent understanding of all aspects of the trial. In this randomised phase II setting, omission of irinotecan in the low topo-1 group was associated with increased response rate and addition of cetuximab in the KRAS, BRAF wild-type cohort was associated with longer progression-free survival. Conclusions: Patient samples can be collected and analysed within workable time frames and with reproducible mutation results. Complex multi-arm designs are acceptable to patients with good PIS. Randomisation within each cohort provides outcome data that can inform clinical practice.

Most Bayesian response-adaptive designs unbalance randomization rates toward the most promising arms with the goal of increasing the number of positive treatment outcomes during the study, even though the primary aim of the trial is different. We discuss Bayesian uncertainty directed designs (BUD), a class of Bayesian designs in which the investigator specifies an information measure tailored to the experiment. All decisions during the trial are selected to optimize the available information at the end of the study. The approach can be applied to several designs, ranging from early stage multi-arm trials to biomarker-driven and multi-endpoint studies. We discuss the asymptotic limit of the patient allocation proportion to treatments, and illustrate the finite-sample operating characteristics of BUD designs through examples, including multi-arm trials, biomarker-stratified trials, and trials with multiple co-primary endpoints. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.