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With advancements in biomarkers and momentum in precision medicine, biomarker-guided trials such as basket trials and umbrella trials have been developed under the master protocol framework. A master protocol refers to a single, over-arching design developed to evaluate multiple hypotheses with the general goal of improving the efficiency of trial evaluation. One type of master protocol is the basket trial, in which a targeted therapy is evaluated for multiple diseases that share com-mon molecular alterations or risk factors that may help predict whether the patients will respond to the given therapy. Another variant of a master protocol is the umbrella trial, in which multiple targeted therapies are evaluated for a single disease that is stratified into multiple subgroups based on different molecular or other predictive risk factors. Both designs follow the core principle of precision medicine-to tailor intervention strategies based on the patient's risk factor(s) that can help predict whether they will respond to a specific treatment. There have been increasing num-bers of basket and umbrella trials, but they are still poorly understood. This article re-views common characteristics of basket and umbrella trials, key trials and recent US Food and Drug Administration approvals for precision oncology, and important con-siderations for clinical readers when critically evaluating future publications on bas-ket trials and umbrella trials and for researchers when designing these clinical trials.

Background Master protocols, classified as basket trials, umbrella trials, and platform trials, are novel designs that investigate multiple hypotheses through concurrent sub-studies (e.g., multiple treatments or populations or that allow adding/removing arms during the trial), offering enhanced efficiency and a more ethical approach to trial evaluation. Despite the many advantages of these designs, they are infrequently used. Methods We conducted a landscape analysis of master protocols using a systematic literature search to determine what trials have been conducted and proposed for an overall goal of improving the literacy in this emerging concept. On July 8, 2019, English-language studies were identified from MEDLINE, EMBASE, and CENTRAL databases and hand searches of published reviews and registries. Results We identified 83 master protocols (49 basket, 18 umbrella, and 16 platform trials). The number of master protocols has increased rapidly over the last five years. Most have been conducted in the US ( n = 44/83) and investigated experimental drugs ( n = 82/83) in the field of oncology ( n = 76/83). The majority of basket trials were exploratory (i.e., phase I/II; n = 47/49) and not randomized ( n = 44/49), and more than half ( n = 28/48) investigated only a single intervention. The median sample size of basket trials was 205 participants (interquartile range, Q3-Q1 [IQR]: 500–90 = 410), and the median study duration was 22.3 (IQR: 74.1–42.9 = 31.1) months. Similar to basket trials, most umbrella trials were exploratory ( n = 16/18), but the use of randomization was more common ( n = 8/18). The median sample size of umbrella trials was 346 participants (IQR: 565–252 = 313), and the median study duration was 60.9 (IQR: 81.3–46.9 = 34.4) months. The median number of interventions investigated in umbrella trials was 5 (IQR: 6–4 = 2). The majority of platform trials were randomized ( n = 15/16), and phase III investigation ( n = 7/15; one did not report information on phase) was more common in platform trials with four of them using seamless II/III design. The median sample size was 892 (IQR: 1835–255 = 1580), and the median study duration was 58.9 (IQR: 101.3–36.9 = 64.4) months. Conclusions We anticipate that the number of master protocols will continue to increase at a rapid pace over the upcoming decades. More efforts to improve awareness and training are needed to apply these innovative trial design methods to fields outside of oncology.

The objective of the study was to outline key considerations for general clinical readers when critically evaluating publications on platform trials and for researchers when designing these types of clinical trials. In this review, we describe key concepts of platform trials with case study discussion of two hallmark platform trials in STAMPEDE and I-SPY2. We provide reader's guide to platform trials with a critical appraisal checklist. Platform trials offer flexibilities of dropping ineffective arms early based on interim data and introducing new arms into the trial. For platform trials, it is important to consider how interventions are compared and evaluated throughout and how new interventions are introduced. For intervention comparisons, it is important to consider what the primary analysis is, what and how many interventions are active simultaneously, and allocation between different arms. Interim evaluation considerations should include the number and timing of interim evaluations and outcomes and statistical rules used to drop interventions. New interventions are usually introduced based on scientific merits, so consideration of these merits is important, together with the timing and mechanisms in which new interventions are added. More efforts are needed to improve the scientific literacy of platform trials. Our review provides an overview of the important concepts of platform trials. •In this review article, we provide reader's guide to platform trials with a critical appraisal checklist.•Platform trials are an extension of adaptive multiarm, multistage trial designs that allow for evaluation of multiple interventions using interim evaluations and addition of new interventions during the trial.•For platform trials, it is important to consider how interventions are compared, how interim evaluations are conducted, and how new interventions are introduced in a given platform trial.•For comparison of interventions, it is important to consider what the primary analysis is, whether the platform trial addresses subgroup effects, the number of interventions that are active at once, and allocation between intervention and control groups.•Interim evaluation considerations should include the frequency, timing, and outcome used for interim evaluations, as well as the statistical rules that are used to drop or graduate interventions onto the next stage.•The scientific merits used to determine what interventions are added into the trial should be considered as well as the timing and how they are added.

Recent years have seen a change in the way that clinical trials are being conducted. There has been a rise of designs more flexible than traditional adaptive and group sequential trials which allow the investigation of multiple substudies with possibly different objectives, interventions, and subgroups conducted within an overall trial structure, summarized by the term master protocol. This review aims to identify existing master protocol studies and summarize their characteristics. The review also identifies articles relevant to the design of master protocol trials, such as proposed trial designs and related methods. We conducted a comprehensive systematic search to review current literature on master protocol trials from a design and analysis perspective, focusing on platform trials and considering basket and umbrella trials. Articles were included regardless of statistical complexity and classified as reviews related to planned or conducted trials, trial designs, or statistical methods. The results of the literature search are reported, and some features of the identified articles are summarized. Most of the trials using master protocols were designed as single-arm (n = 29/50), Phase II trials (n = 32/50) in oncology (n = 42/50) using a binary endpoint (n = 26/50) and frequentist decision rules (n = 37/50). We observed an exponential increase in publications in this domain during the last few years in both planned and conducted trials, as well as relevant methods, which we assume has not yet reached its peak. Although many operational and statistical challenges associated with such trials remain, the general consensus seems to be that master protocols provide potentially enormous advantages in efficiency and flexibility of clinical drug development. Master protocol trials and especially platform trials have the potential to revolutionize clinical drug development if the methodologic and operational challenges can be overcome.

Clinical trials are constantly evolving in the context of increasingly complex research questions and potentially limited resources. In this review article, we discuss the emergence of “adaptive” clinical trials that allow for the preplanned modification of an ongoing clinical trial based on the accumulating evidence with application across translational research. These modifications may include terminating a trial before completion due to futility or efficacy, re-estimating the needed sample size to ensure adequate power, enriching the target population enrolled in the study, selecting across multiple treatment arms, revising allocation ratios used for randomization, or selecting the most appropriate endpoint. Emerging topics related to borrowing information from historic or supplemental data sources, sequential multiple assignment randomized trials (SMART), master protocol and seamless designs, and phase I dose-finding studies are also presented. Each design element includes a brief overview with an accompanying case study to illustrate the design method in practice. We close with brief discussions relating to the statistical considerations for these contemporary designs.

Background There is growing interest in utilizing Bayesian approaches to borrow information across tumor types in basket trials. Several innovative designs, primarily extensions of the Bayesian hierarchical model (BHM), have been proposed to dynamically borrow information based on observed data. However, there is no recognized solution to quantify the degree of information borrowing in such a context, posing a great challenge for non-statisticians to understand these complex designs. Methods The tool of effective sample size (ESS) is leveraged to Bayesian basket trials and several ESS-based borrowing strategies are proposed. The mean squared error (MSE), which explicitly accounts for the trade-off between estimation bias and variance reduction, is selected as the target measure for deriving ESS. Through a reanalysis of the RAGNAR study as well as simulation studies, the interpretability of ESS is demonstrated at both the analysis and design stages of basket trials. Results ESS reflects the impact of information borrowing on MSE and intuitively characterizes the degree of borrowing. It aligns with the type I error rate and power, showing potential as a valuable complement in statistical analyses and simulation studies. Conclusions Quantifying the degree of information borrowing by ESS can greatly help trialists design Bayesian basket trials, reasonably evaluate and interpret the results of Bayesian analyses, conduct sensitivity analyses, and ultimately borrow proper amount of information in basket trials.

Background Master protocols leverage a common trial infrastructure for launching multiple sub-studies. Translational research aims to progress scientific discoveries toward public health impact, which depends on establishing an intervention’s efficacy, effectiveness in real-world conditions, and successful strategies for implementation. While master protocols have been designed to improve the efficiency of clinical trials as sub-studies addressing a particular disease, their application with effectiveness-implementation hybrid studies is yet to be explored. The aim of this study was to develop recommendations for adapting mater protocol methods for effectiveness-implementation research. Methods A method of consultation with translational research networks was undertaken between January and December 2024. Consideration was given to the requirements for service providers to engage in translational research, and how master protocols could support effectiveness-implementation hybrid sub-studies. The underlying rationale for potential adaptations is provided with reference to implementation frameworks, discussion of advantages and disadvantages, and summary recommendations. Results Recommendations are proposed on establishing common trial infrastructure, aims and hypotheses, data collection, control groups, adaptive elements, and eligibility criteria. By leveraging cross-sectoral partnerships, co-producing research and dissemination, and incorporating adaptive elements, master protocols may offer a promising approach for accelerating progress along the translational research pipeline. Conclusions The adaptation of master protocols for hybrid sub-studies could enable evidence-based interventions to be more effectively implemented in routine care settings. The feasibility of master protocols for effectiveness-implementation research is yet to be tested, and further development in this area is needed to trial the proposed methodology.

We develop a general class of response-adaptive Bayesian designs using hierarchical models, and provide open source software to implement them. Our work is motivated by recent master protocols in oncology, where several treatments are investigated simultaneously in one or multiple disease types, and treatment efficacy is expected to vary across biomarkerdefined subpopulations. Adaptive trials such as I-SPY-2 (Barker et al., 2009) and BATTLE (Zhou et al., 2008) are special cases within our framework. We discuss the application of our adaptive scheme to two distinct research goals. The first is to identify a biomarker subpopulation for which a therapy shows evidence of treatment efficacy, and to exclude other subpopulations for which such evidence does not exist. This leads to a subpopulation-finding design. The second is to identify, within biomarkerdefined subpopulations, a set of cancer types for which an experimental therapy is superior to the standard-of-care. This goal leads to a subpopulat ion-stratified design. Using simulations constructed to faithfully represent ongoing cancer sequencing projects, we quantify the potential gains of our proposed designs relative to conventional non-adaptive designs.

Objectives Stage 1: To evaluate the safety and efficacy of candidate agents as add-on therapies to standard of care (SoC) in patients hospitalised with COVID-19 in a screening stage. Stage 2: To confirm the efficacy of candidate agents selected on the basis of evidence from Stage 1 in patients hospitalised with COVID-19 in an expansion stage. Trial design ACCORD is a seamless, Phase 2, adaptive, randomised controlled platform study, designed to rapidly test candidate agents in the treatment of COVID-19. Designed as a master protocol with each candidate agent being included via its own sub-protocol, initially randomising equally between each candidate and a single contemporaneous SoC arm (which can adapt into 2:1). Candidate agents currently include bemcentinib, MEDI3506, acalabrutinib, zilucoplan and nebulised heparin. For each candidate a total of 60 patients will be recruited in Stage 1. If Stage 1 provides evidence of efficacy and acceptable safety the candidate will enter Stage 2 where a total of approximately 126 patients will be recruited into each study arm sub-protocol. Enrollees and outcomes will not be shared across the Stages; the endpoint, analysis and sample size for Stage 2 may be adjusted based on evidence from Stage 1. Additional arms may be added as new potential candidate agents are identified via candidate agent specific sub-protocols. Participants The study will include hospitalised adult patients (≥18 years) with confirmed SARS-CoV-2 infection, the virus that causes COVID-19, that clinically meet Grades 3 (hospitalised – mild disease, no oxygen therapy), Grades 4 (hospitalised, oxygen by mask or nasal prongs) and 5 (hospitalised, non-invasive ventilation or high flow oxygen) of the WHO Working Group on the Clinical Characteristics of COVID-19 9-point category ordinal scale. Participants will be recruited from England, Northern Ireland, Wales and Scotland. Intervention and comparator Comparator is current standard of care (SoC) for the treatment of COVID-19. Current candidate experimental arms include bemcentinib, MEDI3506, acalabrutinib, zilucoplan and nebulised heparin with others to be added over time. Bemcentinib could potentially reduce viral infection and blocks SARS-CoV-2 spike protein; MEDI3506 is a clinic-ready anti-IL-33 monoclonal antibody with the potential to treat respiratory failure caused by COVID; acalabrutinib is a BTK inhibitor which is anti-viral and anti-inflammatory; zilucoplan is a complement C5 inhibitor which may block the severe inflammatory response in COVID-19 and; nebulised heparin has been shown to bind with the spike protein. ACCORD is linked with the UK national COVID therapeutics task force to help prioritise candidate agents. Main outcomes Time to sustained clinical improvement of at least 2 points (from randomisation) on the WHO 9-point category ordinal scale, live discharge from the hospital, or considered fit for discharge (a score of 0, 1, or 2 on the ordinal scale), whichever comes first, by Day 29 (this will also define the “responder” for the response rate analyses). Randomisation An electronic randomization will be performed by Cenduit using Interactive Response Technology (IRT). Randomisation will be stratified by baseline severity grade. Randomisation will proceed with an equal allocation to each arm and a contemporaneous SoC arm (e.g. 1:1 if control and 1 experimental arm; 1:1:1 if two experimental candidate arms etc) but will be reviewed as the trial progresses and may be changed to 2:1 in favour of the candidate agents. Blinding (masking) The trial is open label and no blinding is currently planned in the study. Numbers to be randomised (sample size) This will be in the order of 60 patients per candidate agent for Stage 1, and 126 patients for Stage 2. However, sample size re-estimation may be considered after Stage 1. It is estimated that up to 1800 patients will participate in the overall study. Trial Status Master protocol version ACCORD-2-001 - Master Protocol (Amendment 1) 22 nd April 2020, the trial has full regulatory approval and recruitment is ongoing in the bemcentinib (first patient recruited 6/5/2020), MEDI3506 (first patient recruited 19/5/2020), acalabrutinib (first patient recruited 20/5/2020) and zilucoplan (first patient recruited 19/5/2020) candidates (and SoC). The recruitment dates of each arm will vary between candidate agents as they are added or dropped from the trial, but will have recruited and reported within a year. Trial registration EudraCT 2020-001736-95 , registered 28 th April 2020. Full protocol The full protocol (Master Protocol with each of the candidate sub-protocols) is attached as an additional file, accessible from the Trials website (Additional file 1 ). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol.

Background The INNODIA consortium has established a pan-European infrastructure using validated centres to prospectively evaluate clinical data from individuals with newly diagnosed type 1 diabetes combined with centralised collection of clinical samples to determine rates of decline in beta-cell function and identify novel biomarkers, which could be used for future stratification of phase 2 clinical trials. Methods In this context, we have developed a Master Protocol, based on the “backbone” of the INNODIA natural history study, which we believe could improve the delivery of phase 2 studies exploring the use of single or combinations of Investigational Medicinal Products (IMPs), designed to prevent or reverse declines in beta-cell function in individuals with newly diagnosed type 1 diabetes. Although many IMPs have demonstrated potential efficacy in phase 2 studies, few subsequent phase 3 studies have confirmed these benefits. Currently, phase 2 drug development for this indication is limited by poor evaluation of drug dosage and lack of mechanistic data to understand variable responses to the IMPs. Identification of biomarkers which might permit more robust stratification of participants at baseline has been slow. Discussion The Master Protocol provides (1) standardised assessment of efficacy and safety, (2) comparable collection of mechanistic data, (3) the opportunity to include adaptive designs and the use of shared control groups in the evaluation of combination therapies, and (4) benefits of greater understanding of endpoint variation to ensure more robust sample size calculations and future baseline stratification using existing and novel biomarkers.