Learning health systems, clinical equipoise and the ethics of response adaptive randomisation

Transforming research to improve therapies for trauma in the twenty-first century

Improvements have been made in optimizing initial care of trauma patients, both in prehospital systems as well as in the emergency department, and these have also favorably affected longer term outcomes. However, as specific treatments for bleeding are largely lacking, many patients continue to die from hemorrhage. Also, major knowledge gaps remain on the impact of tissue injury on the host immune and coagulation response, which hampers the development of interventions to treat or prevent organ failure, thrombosis, infections or other complications of trauma. Thereby, trauma remains a challenge for intensivists. This review describes the most pressing research questions in trauma, as well as new approaches to trauma research, with the aim to bring improved therapies to the bedside within the twenty-first century.

Ethics of Adaptive Designs for Randomized Controlled Trials

Over recent decades, adaptive trial designs have been used more and more often for clinical trials, including randomized controlled trials (RCTs). This rise in the use of adaptive RCTs has been accompanied by debates about whether such trials offer ethical and methodological advantages over traditional, fixed RCTs. This study examined how experts on clinical trial methods and ethics believe that adaptive RCTs, compared to fixed ones, affect the ethical character of clinical research. We conducted in-depth interviews with 17 researchers from bioethics, epidemiology, biostatistics, and/or medical backgrounds. While about half believed that adaptive trials are more complex and may thus threaten autonomy, these respondents also expressed that this challenge is not insurmountable. Most respondents expressed that efficiency and potential for participant benefit were the main justifications for adaptive trials. There was tension about whether adaptive randomization in response to increasing information disrupts clinical equipoise, with some respondents insisting that uncertainty still exists and therefore clinical equipoise is not disrupted. These findings suggest that further discussion is needed to increase the awareness and utility of these study designs.

Response-adaptive randomization in clinical trials: from myths to practical considerations

Response-Adaptive Randomization (RAR) is part of a wider class of data-dependent sampling algorithms, for which clinical trials are typically used as a motivating application. In that context, patient allocation to treatments is determined by randomization probabilities that change based on the accrued response data in order to achieve experimental goals. RAR has received abundant theoretical attention from the biostatistical literature since the 1930's and has been the subject of numerous debates. In the last decade, it has received renewed consideration from the applied and methodological communities, driven by well-known practical examples and its widespread use in machine learning. Papers on the subject present different views on its usefulness, and these are not easy to reconcile. This work aims to address this gap by providing a unified, broad and fresh review of methodological and practical issues to consider when debating the use of RAR in clinical trials.

Integrated rapid-cycle comparative effectiveness trials using flexible point of care randomisation in electronic health record systems

Whilst the Randomised Controlled Trial remains the gold standard for deriving robust causal estimates of treatment efficacy, too often a traditional design proves prohibitively expensive or cumbersome when it comes to assessing questions regarding the comparative effectiveness of routinely used treatments. As a result, patients experience variation in practice as clinicians lack the evidence needed to personalise treatments effectively. This variation may be classified as unwarranted, where existing evidence is ignored, or legitimate where in the absence of evidence, clinicians rely on experience, expert opinion, and inferred principles from basic science to make decisions. We argue that within the right ethical and technological framework, legitimate variation can be transformed into a mechanism for evidence generation and learning. Learning Health Systems which harness existing variation in practice, represent a novel approach for generating evidence from everyday clinical practice. The development of these systems has gained traction due to the increased availability of modern Electronic Health Record Systems. However, despite their promise, overcoming hurdles to successfully integrating clinical trials within Learning Health Systems has proven challenging. This article describes the origins of integrated clinical trials and explores two main barriers to their further implementation - how best to obtain informed consent from patients to participate in routine comparative effectiveness research, and how to automate and integrate randomisation into a clinical workflow. Having described these barriers, we present a potential solution in the form of a research pipeline using a novel form of flexible point-of-care randomisation to allow clinicians and patients to participate in studies where there is clinical equipoise.

Randomised controlled trials in medical AI: ethical considerations

In recent years, there has been a surge of high-profile publications on applications of artificial intelligence (AI) systems for medical diagnosis and prognosis. While AI provides various opportunities for medical practice, there is an emerging consensus that the existing studies show considerable deficits and are unable to establish the clinical benefit of AI systems. Hence, the view that the clinical benefit of AI systems needs to be studied in clinical trials-particularly randomised controlled trials (RCTs)-is gaining ground. However, an issue that has been overlooked so far in the debate is that, compared with drug RCTs, AI RCTs require methodological adjustments, which entail ethical challenges. This paper sets out to develop a systematic account of the ethics of AI RCTs by focusing on the moral principles of clinical equipoise, informed consent and fairness. This way, the objective is to animate further debate on the (research) ethics of medical AI.

Non-static framework for understanding adaptive designs: an ethical justification in paediatric trials

Many drugs used in paediatric medicine are off-label. There is a rising call for the use of adaptive clinical trial designs (ADs) in responding to the need for safe and effective drugs given their potential to offer efficiency and cost-effective benefits compared with traditional clinical trials. ADs have a strong appeal in paediatric clinical trials given the small number of available participants, limited understanding of age-related variability and the desire to limit exposure to futile or unsafe interventions. Although the ethical value of adaptive trials has increasingly come under scrutiny, there is a paucity of literature on the ethical dilemmas that may be associated with paediatric adaptive designs (PADs). This paper highlights some of these ethical concerns around safety, scientific/social value and caregiver/guardian comprehension of the trial design. Against this background, the paper develops a non-static conceptual lens for understanding PADs. It shows that ADs are epistemically open and reduce some of the knowledge-associated uncertainties inherent in clinical trials as well as fast-track the time to draw conclusions about the value of evaluated drugs/treatments. On this note, the authors argue that PADs are ethically justifiable given they (1) have multiple layers of safety, exposing enrolled children to lesser potential risks, (2) create social/scientific value generally and for paediatric populations in particular, (3) specifically foster the flourishing of paediatric populations and (4) can significantly improve paediatric trial efficiency when properly designed and implemented. However, because PADs are relatively new and their regulatory, ethical and logistical characteristics are yet to be clarified in some jurisdictions, the cooperation of various public and private stakeholders is required to ensure that the interests of children, their caregivers and parents/guardians are best served while exposing paediatric research subjects to the most minimal of risks when they are enrolled in paediatric trials that use ADs.

Embedded point of care randomisation for evaluating comparative effectiveness questions: PROSPECTOR-critical care feasibility study protocol

INTRODUCTION

Many routinely administered treatments lack evidence as to their effectiveness. When treatments lack evidence, patients receive varying care based on the preferences of clinicians. Standard randomised controlled trials are unsuited to comparisons of different routine treatment strategies, and there remains little economic incentive for change.Integrating clinical trial infrastructure into electronic health record systems offers the potential for routine treatment comparisons at scale, through reduced trial costs. To date, embedded trials have automated data collection, participant identification and eligibility screening, but randomisation and consent remain manual and therefore costly tasks.This study will investigate the feasibility of using computer prompts to allow flexible randomisation at the point of clinical decision making. It will compare the effectiveness of two prompt designs through the lens of a candidate research question-comparing liberal or restrictive magnesium supplementation practices for critical care patients. It will also explore the acceptability of two consent models for conducting comparative effectiveness research.

METHODS AND ANALYSIS

We will conduct a single centre, mixed-methods feasibility study, aiming to recruit 50 patients undergoing elective surgery requiring postoperative critical care admission. Participants will be randomised to either 'Nudge' or 'Preference' designs of electronic point-of-care randomisation prompt, and liberal or restrictive magnesium supplementation.We will judge feasibility through a combination of study outcomes. The primary outcome will be the proportion of prompts displayed resulting in successful randomisation events (compliance with the allocated magnesium strategy). Secondary outcomes will evaluate the acceptability of both prompt designs to clinicians and ascertain the acceptability of pre-emptive and opt-out consent models to patients.

ETHICS AND DISSEMINATION

This study was approved by Riverside Research Ethics Committee (Ref: 21/LO/0785) and will be published on completion.

TRIAL REGISTRATION NUMBER

NCT05149820.

Clinical deployment environments: Five pillars of translational machine learning for health

Machine Learning for Health (ML4H) has demonstrated efficacy in computer imaging and other self-contained digital workflows, but has failed to substantially impact routine clinical care. This is no longer because of poor adoption of Electronic Health Records Systems (EHRS), but because ML4H needs an infrastructure for development, deployment and evaluation within the healthcare institution. In this paper, we propose a design pattern called a Clinical Deployment Environment (CDE). We sketch the five pillars of the CDE: (1) real world development supported by live data where ML4H teams can iteratively build and test at the bedside (2) an ML-Ops platform that brings the rigour and standards of continuous deployment to ML4H (3) design and supervision by those with expertise in AI safety (4) the methods of implementation science that enable the algorithmic insights to influence the behaviour of clinicians and patients and (5) continuous evaluation that uses randomisation to avoid bias but in an agile manner. The CDE is intended to answer the same requirements that bio-medicine articulated in establishing the translational medicine domain. It envisions a transition from "real-world" data to "real-world" development.

Ethically designing research to inform multidimensional, rapidly evolving policy decisions: Lessons learned from the PROMISE HIV Perinatal Prevention Trial

Research in rapidly evolving policy contexts can lead to the following ethical challenges for sponsors and researchers: the study's standard of care can become different than what patients outside the study receive, there may be political or other pressure to move ahead with unproven interventions, and new findings or revised policies may decrease the relevance of ongoing studies. These ethical challenges are considerable, but not unprecedented. In this article, we review the case of a multinational, randomized, controlled perinatal HIV prevention trial, the "PROMISE" (Promoting Maternal Infant Survival Everywhere) study. PROMISE compared the relative efficacy and safety of interventions to prevent mother to child transmission of HIV. The sponsor engaged an independent international ethics panel to address controversy about the study's standard of care and relevance as national and international guidelines changed. This ethics panel concluded that continuing the PROMISE trial as designed was ethically permissible because: (1) participants in all arms received interventions that were effective, and there was insufficient evidence about whether one intervention was more effective or safer than the other, and (2) data from PROMISE could be useful for a diverse range of stakeholders. In general, trials designed to inform rapidly evolving policy issues should develop mechanisms to revisit social value while recognizing that the value of research varies for diverse stakeholders with legitimate reasons to weigh evidence differently. We conclude by providing four reasons that trials may depart from the standard of care after a change in policy, while remaining ethically justifiable, and by suggesting how to improve existing trial oversight mechanisms to address evolving social value.

Distinctive aspects of consent in pilot and feasibility studies

Prior to a main randomized clinical trial, investigators often carry out a pilot or feasibility study in order to test certain trial processes or estimate key statistical parameters, so as to optimize the design of the main trial and/or determine whether it can feasibly be run. Pilot studies reflect the design of the intended main trial, whereas feasibility studies may not do so, and may not involve allocation to different treatments. Testing relative clinical effectiveness is not considered an appropriate aim of pilot or feasibility studies. However, consent is no less important than in a main trial as a means of morally legitimizing the investigator's actions. Two misperceptions are central to consent in clinical studies-therapeutic misconception (a tendency to conflate research and therapy) and therapeutic misestimation (a tendency to overestimate possible benefits and/or underestimate possible harms associated with participation). These phenomena may take a distinctive form in pilot and feasibility studies, owing to potential participants' likely prior unfamiliarity with the nature and purposes of such studies. Thus, participants may confuse the aims of a pilot or feasibility study (developing or optimizing trial design and processes) with those of a main trial (testing treatment effectiveness) and base consent on this misconstrual. Similarly, a misunderstanding of the ability of pilot and feasibility studies to provide information that will inform clinical care, or the underdeveloped nature of interventions included in such studies, may lead to inaccurate assessments of the objective possibility of benefit, and weaken the epistemic basis of consent accordingly. Equipoise may also be particularly challenging to grasp in the context of a pilot study. The consent process in pilot and feasibility studies requires a particular focus, and careful communication, if it is to carry the appropriate moral weight. There are corresponding implications for the process of ethical approval.

Statistical consideration when adding new arms to ongoing clinical trials: the potentials and the caveats

BACKGROUND

Platform trials improve the efficiency of the drug development process through flexible features such as adding and dropping arms as evidence emerges. The benefits and practical challenges of implementing novel trial designs have been discussed widely in the literature, yet less consideration has been given to the statistical implications of adding arms. MAIN: We explain different statistical considerations that arise from allowing new research interventions to be added in for ongoing studies. We present recent methodology development on addressing these issues and illustrate design and analysis approaches that might be enhanced to provide robust inference from platform trials. We also discuss the implication of changing the control arm, how patient eligibility for different arms may complicate the trial design and analysis, and how operational bias may arise when revealing some results of the trials. Lastly, we comment on the appropriateness and the application of platform trials in phase II and phase III settings, as well as publicly versus industry-funded trials.

CONCLUSION

Platform trials provide great opportunities for improving the efficiency of evaluating interventions. Although several statistical issues are present, there are a range of methods available that allow robust and efficient design and analysis of these trials.

Evaluating Bayesian adaptive randomization procedures with adaptive clip methods for multi-arm trials

Bayesian adaptive randomization is a heuristic approach that aims to randomize more patients to the putatively superior arms based on the trend of the accrued data in a trial. Many statistical aspects of this approach have been explored and compared with other approaches; yet only a limited number of works has focused on improving its performance and providing guidance on its application to real trials. An undesirable property of this approach is that the procedure would randomize patients to an inferior arm in some circumstances, which has raised concerns in its application. Here, we propose an adaptive clip method to rectify the problem by incorporating a data-driven function to be used in conjunction with Bayesian adaptive randomization procedure. This function aims to minimize the chance of assigning patients to inferior arms during the early time of the trial. Moreover, we propose a utility approach to facilitate the selection of a randomization procedure. A cost that reflects the penalty of assigning patients to the inferior arm(s) in the trial is incorporated into our utility function along with all patients benefited from the trial, both within and beyond the trial. We illustrate the selection strategy for a wide range of scenarios.

Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs

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.

[Studies on novel immune therapies: challenges from an ethical point of view]

Neue Immuntherapien werden aufgrund der immer weiter reichenden molekularen Differenzierung von Erkrankungsmustern immer häufiger in sogenannten adaptiven, also fortlaufend an Ergebnisse angepassten Studiendesigns (Umbrella- oder Basket-Studien beziehungsweise Plattformstudien) klinisch erprobt. Der hier vorgelegte Beitrag diskutiert diese Studiendesigns jenseits der Feststellung von Regulierungsbedarf, um ausgehend von typischen Strukturmerkmalen ethische Probleme zu identifizieren und – wo möglich – Lösungsvorschläge zu machen. Neben dem Verhältnis von wissenschaftlichen und sozialen Werten in klinischen Studien werden insbesondere die wissenschaftliche Validität von Evidenz, Fragen des Einschlusses von Studienteilnehmern unter der Bedingung von relativer Unsicherheit, spezifische Herausforderungen für die ethische Bewertung adaptiver Studien sowie die ethischen und praktischen Herausforderungen im Bereich der Patientenaufklärung und -einwilligung in den Blick genommen.

The ProBio trial: molecular biomarkers for advancing personalized treatment decision in patients with metastatic castration-resistant prostate cancer

BACKGROUND

Multiple therapies exist for patients with metastatic castration-resistant prostate cancer (mCRPC). However, their improvement on progression-free survival (PFS) remains modest, potentially explained by tumor molecular heterogeneity. Several prognostic molecular biomarkers have been identified for mCRPC that may have predictive potential to guide treatment selection and prolong PFS. We designed a platform trial to test this hypothesis.

METHODS

The Prostate-Biomarker (ProBio) study is a multi-center, outcome-adaptive, multi-arm, biomarker-driven platform trial for tailoring treatment decisions for men with mCRPC. Treatment decisions in the experimental arms are based on biomarker signatures defined as mutations in certain genes/pathways suggested in the scientific literature to be important for treatment response in mCRPC. The biomarker signatures are determined by targeted sequencing of circulating tumor and germline DNA using a panel specifically designed for mCRPC.

DISCUSSION

Patients are stratified based on the sequencing results and randomized to either current clinical practice (control), where the treating physician decides treatment, or to molecularly driven treatment selection based on the biomarker profile. Outcome-adaptive randomization is implemented to early identify promising treatments for a biomarker signature. Biomarker signature-treatment combinations graduate from the platform when they demonstrate 85% probability of improving PFS compared to the control arm. Graduated combinations are further evaluated in a seamless confirmatory trial with fixed randomization. The platform design allows for new drugs and biomarkers to be introduced in the study.

CONCLUSIONS

The ProBio design allows promising treatment-biomarker combinations to quickly graduate from the platform and be confirmed for rapid implementation in clinical care.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier NCT03903835. Date of registration: April 4, 2019. Status: Recruiting.

Implementing learning health systems in the UK NHS: Policy actions to improve collaboration and transparency and support innovation and better use of analytics

Learning health systems (LHS) use digital health and care data to improve care, shorten the timeframe of improvement projects, and ensure these are based on real-world data. In the United Kingdom, policymakers are depending on digital innovation, driven by better use of data about current health service performance, to enable service transformation and a more sustainable health system. This paper examines what would be needed to develop LHS in the United Kingdom, considering national policy implications and actions, which local organisations and health systems could take. The paper draws on a seminar attended by academics, policymakers, and practitioners, a brief literature review, and feedback from policy experts and National Health Service (NHS) stakeholders. Although there are examples of some aspects of LHS in the UK NHS, it is hard to find examples where there is a continuous cycle of improvement driven by information and where analysis of data and implementing improvements is part of usual ways of working. The seminar and literature identified a number of barriers. Incentives and capacity to develop LHS are limited, and requires a shift in analytic capacity from regulation and performance, to quality improvement and transformation. The balance in priority given to research compared with implementation also needs to change. Policy initiatives are underway which address some barriers, including building analytical capacity, developing infrastructure, and data standards. The NHS and research partners are investing in infrastructure which could support LHS, although clinical buy in is needed to bring about improvement or address operational challenges. We identify a number of opportunities for local NHS organisations and systems to make better use of health data, and for ways that national policy could promote the collaboration and greater use of analytics which underpin the LHS concept.

Optimal delay time to initiate anticoagulation after ischemic stroke in atrial fibrillation (START): Methodology of a pragmatic, response-adaptive, prospective randomized clinical trial

RATIONALE

An estimated 15% of all strokes are associated with untreated atrial fibrillation. Long-term secondary stroke prevention in atrial fibrillation is anticoagulation, increasingly with non-vitamin K oral anticoagulants. The optimal time to initiate anticoagulation following an atrial fibrillation-related stroke that balances hemorrhagic conversion with recurrent stroke is not yet known.

AIMS

To determine if there is an optimal delay time to initiate anticoagulation after atrial fibrillation-related stroke that optimizes the composite outcome of hemorrhagic conversion and recurrent ischemic stroke.

SAMPLE SIZE ESTIMATES

The study will enroll 1500 total subjects split between a mild to moderate stroke cohort (1000) and a severe stroke cohort (500).

METHODS AND DESIGN

This study is a multi-center, prospective, randomized, pragmatic, adaptive trial that randomizes subjects to four arms of time to start of anticoagulation. The four arms for mild to moderate stroke are: Day 3, Day 6, Day 10, and Day 14. The time intervals for severe stroke are: Day 6, Day 10, Day 14, and Day 21. Allocation involves a response adaptive randomization via interim analyses to favor the arms that have a better risk-benefit profile.

STUDY OUTCOMES

The primary outcome event is the composite occurrence of an ischemic or hemorrhagic event within 30 days of the index stroke. Secondary outcomes are also collected at 30 and 90 days.

DISCUSSION

The optimal timing of direct oral anticoagulants post-ischemic stroke requires prospective randomized testing. A pragmatically designed trial with adaptive allocation and randomization to multiple time intervals such as the START trial is best suited to answer this question in order to directly inform current practice on this question.

Comparison of methods for control allocation in multiple arm studies using response adaptive randomization

BACKGROUND/AIMS

Response adaptive randomization has many polarizing properties in two-arm settings comparing control to a single treatment. The generalization of these features to the multiple arm setting has been less explored, and existing comparisons in the literature reach disparate conclusions. We investigate several generalizations of two-arm response adaptive randomization methods relating to control allocation in multiple arm trials, exploring how critiques of response adaptive randomization generalize to the multiple arm setting.

METHODS

We perform a simulation study to investigate multiple control allocation schemes within response adaptive randomization, comparing the designs on metrics such as power, arm selection, mean square error, and the treatment of patients within the trial.

RESULTS

The results indicate that the generalization of two-arm response adaptive randomization concerns is variable and depends on the form of control allocation employed. The concerns are amplified when control allocation may be reduced over the course of the trial but are mitigated in the methods considered when control allocation is maintained or increased during the trial. In our chosen example, we find minimal advantage to increasing, as opposed to maintaining, control allocation; however, this result reflects an extremely limited exploration of methods for increasing control allocation.

CONCLUSION

Selection of control allocation in multiple arm response adaptive randomization has a large effect on the performance of the design. Some disparate comparisons of response adaptive randomization to alternative paradigms may be partially explained by these results. In future comparisons, control allocation for multiple arm response adaptive randomization should be chosen to keep in mind the appropriate match between control allocation in response adaptive randomization and the metric or metrics of interest.

Outcome-adaptive randomization in clinical trials: issues of participant welfare and autonomy

Outcome-adaptive randomization (OAR) has been proposed as a corrective to certain ethical difficulties inherent in the traditional randomized clinical trial (RCT) using fixed-ratio randomization. In particular, it has been suggested that OAR redresses the balance between individual and collective ethics in favour of the former. In this paper, I examine issues of welfare and autonomy arising in relation to OAR. A central issue in discussions of welfare in OAR is equipoise, and the moral status of OAR is crucially influenced by the way in which this concept is construed. If OAR is based on a model of equipoise that demands strict indifference between competing interventions throughout the trial, such equipoise is disturbed by accruing data favouring one treatment over another; OAR seeks to redress this by weighting randomization to the seemingly superior treatment. However, this is a partial response, as patients continue to be allocated to the inferior therapy. Moreover, it rests upon considerations of aggregate harms and benefits, and does not therefore uphold individual ethics. Issues of fairness also arise, as early and late enrollees are randomized on a different basis. Fixed-ratio randomization represents a fuller and more consistent response to a loss of equipoise, as so construed. With regard to consent, the complexity of OAR poses challenges to adequate disclosure and comprehension. Additionally, OAR does not offer a remedy to the therapeutic misconception-participants' tendency to attribute treatment allocation in an RCT to individual clinical judgments, rather than to scientific considerations-and, if anything, accentuates rather than alleviates this misconception. In relation to these issues, OAR fails to offer ethical advantages over fixed-ratio randomization. More broadly, the ethical basis of OAR can be seen to lie more in collective than in individual ethics, and overall it fares worse in this territory than fixed-ratio randomization.

Heterogeneous perception of the ethical legitimacy of unbalanced randomization by institutional review board members: a clinical vignette-based survey

Background

Institutional review boards must guarantee the ethical acceptability of a randomized controlled trial before it is conducted. However, some may regard an unbalanced randomization ratio as reflecting an absence of uncertainty between the groups being compared. The objective was to assess institutional review board members’ perceptions of whether unbalanced randomization in randomized controlled trials is justified and ethically acceptable.

Methods

Institutional review board members worldwide completed a survey involving clinical vignettes modeling situations classically advocated to explain the use of unbalanced randomization. Institutional review board members were asked whether unbalanced randomization was justified and ethically sound. Answers were collected by using visual analog scales. Data were analyzed by principal component analysis, and a hierarchical ascending classification was created. Verbatim answers were assessed by qualitative content analysis.

Results

We analyzed responses from 148 institutional review board members. Three classes of respondents were identified: class 1 (n = 58; 39.2%), mostly skeptics who disagreed with unbalanced randomization, whatever the justification; class 2 (n = 46; 31.1%), believers who considered that unbalanced randomization was acceptable whatever the justification, except cost; and class 3 (n = 44; 29.7%), circumstantial believers for whom unbalanced randomization may be justified for methodological and safety issues but not cost or ethical issues. When institutional review board members were asked whether unbalanced randomization respected the equipoise principle, the mean quotation was low (4.5 ± 3.3 out of 10), especially for class 1 members.

Conclusions

Institutional review board members perceive unbalanced randomization heterogeneously in terms of its justification and its ethical validity.

Electronic supplementary material

The online version of this article (10.1186/s13063-018-2822-1) contains supplementary material, which is available to authorized users.