Are outcome-adaptive allocation trials ethical?

Two-to-One Randomization: Rarely Advisable

In a randomized clinical trial, instead of allocating patients equally between the treatment arms, some trials in oncology assign a higher proportion of patients to receive the experimental treatment arm (eg, a two-to-one randomization). In this commentary, we first briefly review the common reasons given for the use of a two-to-one randomization and provide some examples of trials using these designs. We then explain why the risk-benefit ratio of this approach may not be favorable as is commonly assumed.

Effects of duration of follow-up and lag in data collection on the performance of adaptive clinical trials

Different combined outcome-data lags (follow-up durations plus data-collection lags) may affect the performance of adaptive clinical trial designs. We assessed the influence of different outcome-data lags (0-105 days) on the performance of various multi-stage, adaptive trial designs (2/4 arms, with/without a common control, fixed/response-adaptive randomisation) with undesirable binary outcomes according to different inclusion rates (3.33/6.67/10 patients/day) under scenarios with no, small, and large differences. Simulations were conducted under a Bayesian framework, with constant stopping thresholds for superiority/inferiority calibrated to keep type-1 error rates at approximately 5%. We assessed multiple performance metrics, including mean sample sizes, event counts/probabilities, probabilities of conclusiveness, root mean squared errors (RMSEs) of the estimated effect in the selected arms, and RMSEs between the analyses at the time of stopping and the final analyses including data from all randomised patients. Performance metrics generally deteriorated when the proportions of randomised patients with available data were smaller due to longer outcome-data lags or faster inclusion, that is, mean sample sizes, event counts/probabilities, and RMSEs were larger, while the probabilities of conclusiveness were lower. Performance metric impairments with outcome-data lags ≤45 days were relatively smaller compared to those occurring with ≥60 days of lag. For most metrics, the effects of different outcome-data lags and lower proportions of randomised patients with available data were larger than those of different design choices, for example, the use of fixed versus response-adaptive randomisation. Increased outcome-data lag substantially affected the performance of adaptive trial designs. Trialists should consider the effects of outcome-data lags when planning adaptive trials.

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.

Research objectives and general considerations for pragmatic clinical trials of pain treatments: IMMPACT statement

ABSTRACT

Many questions regarding the clinical management of people experiencing pain and related health policy decision-making may best be answered by pragmatic controlled trials. To generate clinically relevant and widely applicable findings, such trials aim to reproduce elements of routine clinical care or are embedded within clinical workflows. In contrast with traditional efficacy trials, pragmatic trials are intended to address a broader set of external validity questions critical for stakeholders (clinicians, healthcare leaders, policymakers, insurers, and patients) in considering the adoption and use of evidence-based treatments in daily clinical care. This article summarizes methodological considerations for pragmatic trials, mainly concerning methods of fundamental importance to the internal validity of trials. The relationship between these methods and common pragmatic trials methods and goals is considered, recognizing that the resulting trial designs are highly dependent on the specific research question under investigation. The basis of this statement was an Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) systematic review of methods and a consensus meeting. The meeting was organized by the Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks (ACTTION) public-private partnership. The consensus process was informed by expert presentations, panel and consensus discussions, and a preparatory systematic review. In the context of pragmatic trials of pain treatments, we present fundamental considerations for the planning phase of pragmatic trials, including the specification of trial objectives, the selection of adequate designs, and methods to enhance internal validity while maintaining the ability to answer pragmatic research questions.

Matching ratio and sample size for optimal sequential testing with binomial data

Statistical sequential analysis of binary data is an important tool in clinical trials such as placebo-controlled trials, where a total of K individuals are randomly allocated into two groups, one of size κ 1 receiving the treatment/drug, and the other of size κ 2 for placebo. The ratio z = κ 2 / κ 1 , namely "matching ratio," determines the expected proportion of adverse events from the treatment group among the κ 1 + κ 2 individuals. Bernoulli-based designs are used for monitoring the safety of post-licensed drugs and vaccines as well. For instance, in a self-control design, z is the ratio between the risk and the control time windows. Irrespective of the type of application, the choice of z is a critical design criterion as it determines the sample size, the statistical power, the expected sample size, and the expected time to signal the sequential procedure. In this paper, we run exact calculations to offer a statistical rule of thumb for the choice of z . All the calculations and examples are performed using the R Sequential package.

A rapid review of community engagement and informed consent processes for adaptive platform trials and alternative design trials for public health emergencies

Background : Public Health Emergencies (PHE) demand expeditious research responses to evaluate new or repurposed therapies and prevention strategies. Alternative Design Trials (ADTs) and Adaptive Platform Trials (APTs) have enabled efficient large-scale testing of biomedical interventions during recent PHEs. Design features of these trials may have implications for engagement and/or informed consent processes. We aimed to rapidly review evidence on engagement and informed consent for ADTs and APTs during PHE to consider what (if any) recommendations can inform practice. Method : In 2022, we searched 8 prominent databases for relevant peer reviewed publications and guidelines for ADTs/APTs in PHE contexts. Articles were selected based on pre-identified inclusion and exclusion criteria. We reviewed protocols and informed consent documents for a sample of large platform trials and consulted with key informants from ADTs/APT trial teams. Data were extracted and summarised using narrative synthesis. Results : Of the 49 articles included, 10 were guidance documents, 14 discussed engagement, 10 discussed informed consent, and 15 discussed both. Included articles addressed ADTs delivered during the West African Ebola epidemic and APTs delivered during COVID-19. PHE clinical research guidance documents highlight the value of ADTs/APTs and the importance of community engagement, but do not provide practice-specific guidance for engagement or informed consent. Engagement and consent practice for ADTs conducted during the West African Ebola epidemic have been well-documented. For COVID-19, engagement and consent practice was described for APTs primarily delivered in high income countries with well-developed health service structures. A key consideration is strong communication of the complexity of trial design in clear, accessible ways. Conclusion: We highlight key considerations for best practice in community engagement and informed consent relevant to ADTs and APTs for PHEs which may helpfully be included in future guidance. Protocol: The review protocol is published online at Prospero on 15/06/2022: registration number CRD42022334170.

The value of a two-armed Bayesian response adaptive randomization trial

We investigate the value of a two-armed Bayesian response adaptive randomization (RAR) design to investigate early preterm birth rates of high versus low dose of docosahexaenoic acid during pregnancy. Unexpectedly, the COVID-19 pandemic forced recruitment to pause at 1100 participants rather than the planned 1355. The difference in power between number of participants at the pause and planned was 87% and 90% respectively. We decided to stop the study. This paper describes how the RAR was used to execute the study. The value of RAR in two-armed studies is quite high and their use in the future is promising.

An overview of methodological considerations regarding adaptive stopping, arm dropping, and randomization in clinical trials

BACKGROUND AND OBJECTIVES

Adaptive features may increase flexibility and efficiency of clinical trials, and improve participants' chances of being allocated to better interventions. Our objective is to provide thorough guidance on key methodological considerations for adaptive clinical trials.

METHODS

We provide an overview of key methodological considerations for clinical trials employing adaptive stopping, adaptive arm dropping, and response-adaptive randomization. We cover pros and cons of different decisions and provide guidance on using simulation to compare different adaptive trial designs. We focus on Bayesian multi-arm adaptive trials, although the same general considerations apply to frequentist adaptive trials.

RESULTS

We provide guidance on 1) interventions and possible common control, 2) outcome selection, follow-up duration and model choice, 3) timing of adaptive analyses, 4) decision rules for adaptive stopping and arm dropping, 5) randomization strategies, 6) performance metrics, their prioritization, and arm selection strategies, and 7) simulations, assessment of performance under different scenarios, and reporting. Finally, we provide an example using a newly developed R simulation engine that may be used to evaluate and compare different adaptive trial designs.

CONCLUSION

This overview may help trialists design better and more transparent adaptive clinical trials and to adequately compare them before initiation.

Bayesian Strategies in Rare Diseases

Bayesian strategies for planning and analyzing clinical trials have become a viable choice, especially in rare diseases where drug development faces many challenges and stakeholders are interested in innovations that may help overcome them. Disease natural history and clinical outcomes occurrence and variability are often poorly understood. Standard trial designs are not optimized to obtain adequate safety and efficacy data from small numbers of patients. Bayesian methods are well-suited for adaptive trials, with an accelerated learning curve. Using Bayesian statistics can be advantageous in that design choices and their consequences are considered carefully, continuously monitored, and updated where necessary, which ultimately provides a natural and principled way of seamlessly combining prior clinical information with data, within a solid decision theoretical framework. In this article, we introduce the Bayesian option in the rare disease context to support clinical decision-makers in selecting the best choice for their drug development project. Many researchers in drug development show reluctance to using Bayesian statistics, and the top-two reported barriers are insufficient knowledge of Bayesian approaches and a lack of clarity or guidance from regulators. Here we introduce concepts of borrowing, extrapolation, adaptation, and modeling and illustrate them with examples that have been discussed or developed with regulatory bodies to show how Bayesian strategies can be applied to drug development in rare diseases.

Bounded-width confidence interval following optimal sequential analysis of adverse events with binary data

In sequential testing with binary data, sample size and time to detect a signal are the key performance measures to optimize. While the former should be optimized in Phase III clinical trials, minimizing the latter is of major importance in post-market drug and vaccine safety surveillance of adverse events. The precision of the relative risk estimator on termination of the analysis is a meaningful design criterion as well. This paper presents a linear programming framework to find the optimal alpha spending that minimizes expected time to signal, or expected sample size as needed. The solution enables (a) to bound the width of the confidence interval following the end of the analysis, (b) designs with outer signaling thresholds and inner non-signaling thresholds, and (c) sequential designs with variable Bernoulli probabilities. To illustrate, we use real data on the monitoring of adverse events following the H1N1 vaccination. The numerical results are obtained using the R Sequential package.

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.

Optimizing a Bayesian hierarchical adaptive platform trial design for stroke patients

BACKGROUND

Platform trials are well-known for their ability to investigate multiple arms on heterogeneous patient populations and their flexibility to add/drop treatment arms due to efficacy/lack of efficacy. Because of their complexity, it is important to develop highly optimized, transparent, and rigorous designs that are cost-efficient, offer high statistical power, maximize patient benefit, and are robust to changes over time.

METHODS

To address these needs, we present a Bayesian platform trial design based on a beta-binomial model for binary outcomes that uses three key strategies: (1) hierarchical modeling of subgroups within treatment arms that allows for borrowing of information across subgroups, (2) utilization of response-adaptive randomization (RAR) schemes that seek a tradeoff between statistical power and patient benefit, and (3) adjustment for potential drift over time. Motivated by a proposed clinical trial that aims to find the appropriate treatment for different subgroup populations of ischemic stroke patients, extensive simulation studies were performed to validate the approach, compare different allocation rules, and study the model operating characteristics.

RESULTS AND CONCLUSIONS

Our proposed approach achieved high statistical power and good patient benefit and was also robust against population drift over time. Our design provided a good balance between the strengths of both the traditional RAR scheme and fixed 1:1 allocation and may be a promising choice for dichotomous outcomes trials investigating multiple subgroups.

An alternative metric for evaluating the potential patient benefit of response-adaptive randomization procedures

When planning a two-arm group sequential clinical trial with a binary primary outcome that has severe implications for quality of life (e.g., mortality), investigators may strive to find the design that maximizes in-trial patient benefit. In such cases, Bayesian response-adaptive randomization (BRAR) is often considered because it can alter the allocation ratio throughout the trial in favor of the treatment that is currently performing better. Although previous studies have recommended using fixed randomization over BRAR based on patient benefit metrics calculated from the realized trial sample size, these previous comparisons have been limited by failures to hold type I and II error rates constant across designs or consider the impacts on all individuals directly affected by the design choice. In this paper, we propose a metric for comparing designs with the same type I and II error rates that reflects expected outcomes among individuals who would participate in the trial if enrollment is open when they become eligible. We demonstrate how to use the proposed metric to guide the choice of design in the context of two recent trials in persons suffering out of hospital cardiac arrest. Using computer simulation, we demonstrate that various implementations of group sequential BRAR offer modest improvements with respect to the proposed metric relative to conventional group sequential monitoring alone. This article is protected by copyright. All rights reserved.

Time trends with response-adaptive randomization: The inevitability of inefficiency

Response-adaptive randomization, which changes the randomization ratio as a randomized clinical trial progresses, is inefficient as compared to a fixed 1:1 randomization ratio in terms of increased required sample size. It is also known that response-adaptive randomization leads to biased treatment effects if there are time trends in the accruing outcome data, for example, due to changes in the patient population being accrued, evaluation methods, or concomitant treatments. Response-adaptive-randomization analysis methods that account for potential time trends, such as time-block stratification or re-randomization, can eliminate this bias. However, as shown in this Commentary, these analysis methods cause a large additional inefficiency of response-adaptive randomization, regardless of whether a time trend actually exists.

Of some innovations in clinical trial design in hematology and oncology

The design of clinical trials, formalized in the immediate post-war period, has undergone major changes due to therapeutic innovations, particularly the arrival of targeted therapies in onco-hematology. The traditional phase I-II-III regimen is regularly questioned and multiple adaptations are proposed. This article proposes to expose some of these modifications and the issues they lead to.

A roadmap to using randomization in clinical trials

BACKGROUND

Randomization is the foundation of any clinical trial involving treatment comparison. It helps mitigate selection bias, promotes similarity of treatment groups with respect to important known and unknown confounders, and contributes to the validity of statistical tests. Various restricted randomization procedures with different probabilistic structures and different statistical properties are available. The goal of this paper is to present a systematic roadmap for the choice and application of a restricted randomization procedure in a clinical trial.

METHODS

We survey available restricted randomization procedures for sequential allocation of subjects in a randomized, comparative, parallel group clinical trial with equal (1:1) allocation. We explore statistical properties of these procedures, including balance/randomness tradeoff, type I error rate and power. We perform head-to-head comparisons of different procedures through simulation under various experimental scenarios, including cases when common model assumptions are violated. We also provide some real-life clinical trial examples to illustrate the thinking process for selecting a randomization procedure for implementation in practice.

RESULTS

Restricted randomization procedures targeting 1:1 allocation vary in the degree of balance/randomness they induce, and more importantly, they vary in terms of validity and efficiency of statistical inference when common model assumptions are violated (e.g. when outcomes are affected by a linear time trend; measurement error distribution is misspecified; or selection bias is introduced in the experiment). Some procedures are more robust than others. Covariate-adjusted analysis may be essential to ensure validity of the results. Special considerations are required when selecting a randomization procedure for a clinical trial with very small sample size.

CONCLUSIONS

The choice of randomization design, data analytic technique (parametric or nonparametric), and analysis strategy (randomization-based or population model-based) are all very important considerations. Randomization-based tests are robust and valid alternatives to likelihood-based tests and should be considered more frequently by clinical investigators.

Adaptive Trials in Cardiology: Some Considerations and Examples

Adaptive trials hold great promise to enhance the evidence base supporting medical interventions. In this review, we will describe the basic principles of an adaptive trial and the different types of adaptive trials, show examples of adaptive trials, and conclude with the advantages and challenges of different types of adaptive trials. While regulatory bodies have expressed a desire to see more adaptive trials, resistance in the community remains. We hope that this review helps to build greater acceptance of the concept of adaptive trial design.

COVID-19 and the generation of novel scientific knowledge: Research questions and study designs

RATIONALE, AIMS, AND OBJECTIVES

One of the sectors challenged by the COVID-19 pandemic is medical research. COVID-19 originates from a novel coronavirus (SARS-CoV-2) and the scientific community is faced with the daunting task of creating a novel model for this pandemic or, in other words, creating novel science. This paper is the first part of a series of two papers that explore the intricate relationship between the different challenges that have hindered biomedical research and the generation of scientific knowledge during the COVID-19 pandemic.

METHODS

During the early stages of the pandemic, research conducted on hydroxychloroquine (HCQ) was chaotic and sparked several heated debates with respect to the scientific methods used and the quality of knowledge generated. Research on HCQ is used as a case study in both papers. The authors explored biomedical databases, peer-reviewed journals, pre-print servers, and media articles to identify relevant literature on HCQ and COVID-19, and examined philosophical perspectives on medical research in the context of this pandemic and previous global health challenges.

RESULTS

This paper demonstrates that a lack of prioritization among research questions and therapeutics was responsible for the duplication of clinical trials and the dispersion of precious resources. Study designs, aimed at minimising biases and increasing objectivity, were, instead, the subject of fruitless oppositions. The duplication of research works, combined with poor-quality research, has greatly contributed to slowing down the creation of novel scientific knowledge.

CONCLUSIONS

The COVID-19 pandemic presented challenges in terms of (1) finding and prioritising relevant research questions and (2) choosing study designs that are appropriate for a time of emergency.

Adaptive randomization in a two-stage sequential multiple assignment randomized trial

Sequential multiple assignment randomized trials (SMARTs) are systematic and efficient media for comparing dynamic treatment regimes (DTRs), where each patient is involved in multiple stages of treatment with the randomization at each stage depending on the patient's previous treatment history and interim outcomes. Generally, patients enrolled in SMARTs are randomized equally to ethically acceptable treatment options regardless of how effective those treatments were during the previous stages, which results in some undesirable consequences in practice, such as low recruitment, less retention, and lower treatment adherence. In this article, we propose a response-adaptive SMART (RA-SMART) design where the allocation probabilities are imbalanced in favor of more promising treatments based on the accumulated information on treatment efficacy from previous patients and stages. The operating characteristics of the RA-SMART design relative to SMART design, including the consistency and efficiency of estimated response rate under each DTR, the power of identifying the optimal DTR, and the number of patients treated with the optimal and the worst DTRs, are assessed through extensive simulation studies. Some practical suggestions are discussed in the conclusion.

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.

Alternative models and randomization techniques for Bayesian response-adaptive randomization with binary outcomes

BACKGROUND

Bayesian response-adaptive designs, which data adaptively alter the allocation ratio in favor of the better performing treatment, are often criticized for engendering a non-trivial probability of a subject imbalance in favor of the inferior treatment, inflating type I error rate, and increasing sample size requirements. The implementation of these designs using the Thompson sampling methods has generally assumed a simple beta-binomial probability model in the literature; however, the effect of these choices on the resulting design operating characteristics relative to other reasonable alternatives has not been fully examined. Motivated by the Advanced R² Eperfusion STrategies for Refractory Cardiac Arrest trial, we posit that a logistic probability model coupled with an urn or permuted block randomization method will alleviate some of the practical limitations engendered by the conventional implementation of a two-arm Bayesian response-adaptive design with binary outcomes. In this article, we discuss up to what extent this solution works and when it does not.

METHODS

A computer simulation study was performed to evaluate the relative merits of a Bayesian response-adaptive design for the Advanced R² Eperfusion STrategies for Refractory Cardiac Arrest trial using the Thompson sampling methods based on a logistic regression probability model coupled with either an urn or permuted block randomization method that limits deviations from the evolving target allocation ratio. The different implementations of the response-adaptive design were evaluated for type I error rate control across various null response rates and power, among other performance metrics.

RESULTS

The logistic regression probability model engenders smaller average sample sizes with similar power, better control over type I error rate, and more favorable treatment arm sample size distributions than the conventional beta-binomial probability model, and designs using the alternative randomization methods have a negligible chance of a sample size imbalance in the wrong direction.

CONCLUSION

Pairing the logistic regression probability model with either of the alternative randomization methods results in a much improved response-adaptive design in regard to important operating characteristics, including type I error rate control and the risk of a sample size imbalance in favor of the inferior treatment.

A Bayesian adaptive randomized phase II multicenter trial of bevacizumab with or without vorinostat in adults with recurrent glioblastoma

BACKGROUND

Bevacizumab has promising activity against recurrent glioblastoma (GBM). However, acquired resistance to this agent results in tumor recurrence. We hypothesized that vorinostat, a histone deacetylase (HDAC) inhibitor with anti-angiogenic effects, would prevent acquired resistance to bevacizumab.

METHODS

This multicenter phase II trial used a Bayesian adaptive design to randomize patients with recurrent GBM to bevacizumab alone or bevacizumab plus vorinostat with the primary endpoint of progression-free survival (PFS) and secondary endpoints of overall survival (OS) and clinical outcomes assessment (MD Anderson Symptom Inventory Brain Tumor module [MDASI-BT]). Eligible patients were adults (≥18 y) with histologically confirmed GBM recurrent after prior radiation therapy, with adequate organ function, KPS ≥60, and no prior bevacizumab or HDAC inhibitors.

RESULTS

Ninety patients (bevacizumab + vorinostat: 49, bevacizumab: 41) were enrolled, of whom 74 were evaluable for PFS (bevacizumab + vorinostat: 44, bevacizumab: 30). Median PFS (3.7 vs 3.9 mo, P = 0.94, hazard ratio [HR] 0.63 [95% CI: 0.38, 1.06, P = 0.08]), median OS (7.8 vs 9.3 mo, P = 0.64, HR 0.93 [95% CI: 0.5, 1.6, P = 0.79]) and clinical benefit were similar between the 2 arms. Toxicity (grade ≥3) in 85 evaluable patients included hypertension (n = 37), neurological changes (n = 2), anorexia (n = 2), infections (n = 9), wound dehiscence (n = 2), deep vein thrombosis/pulmonary embolism (n = 2), and colonic perforation (n = 1).

CONCLUSIONS

Bevacizumab combined with vorinostat did not yield improvement in PFS or OS or clinical benefit compared with bevacizumab alone or a clinical benefit in adults with recurrent GBM. This trial is the first to test a Bayesian adaptive design with adaptive randomization and Bayesian continuous monitoring in patients with primary brain tumor and demonstrates the feasibility of using complex Bayesian adaptive design in a multicenter setting.

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.

An adaptive method for assigning clinical trials wait-times for controls

Wait-list control clinical trials are popular among psychologists and rehabilitation specialists partly because all participants receive the intervention. In 2 arm wait-list control trials, individuals randomized to the treatment group receive immediate treatment whereas individuals randomized to the control group wait a fixed amount of time before intervention is initiated. For interventions that have varying durations, careful consideration must be given to the period that participants in the control group have a delay until treatment begins, as incongruent wait times compared to the intervention durations of the treatment group may introduce confounding into the evaluation of the treatment differences. To alleviate this issue, we propose to adaptively assign wait times to individuals randomized to the control group based on the intervention duration of those in the treatment group. Simulations demonstrate the that our method not only results in similar timing distributions between participants in the treatment and control groups, but also allows participants in the control group to initiate treatment earlier than the traditional design. The latter characteristic may reduce dropout and result in more efficient study enrollment.

Adaptive Enrichment Designs in Clinical Trials

Adaptive enrichment designs for clinical trials may include rules that use interim data to identify treatment-sensitive patient subgroups, select or compare treatments, or change entry criteria. A common setting is a trial to compare a new biologically targeted agent to standard therapy. An enrichment design's structure depends on its goals, how it accounts for patient heterogeneity and treatment effects, and practical constraints. This article first covers basic concepts, including treatment-biomarker interaction, precision medicine, selection bias, and sequentially adaptive decision making, and briefly describes some different types of enrichment. Numerical illustrations are provided for qualitatively different cases involving treatment-biomarker interactions. Reviews are given of adaptive signature designs; a Bayesian design that uses a random partition to identify treatment-sensitive biomarker subgroups and assign treatments; and designs that enrich superior treatment sample sizes overall or within subgroups, make subgroup-specific decisions, or include outcome-adaptive randomization.

The Bayesian Design of Adaptive Clinical Trials

This paper presents a brief overview of the recent literature on adaptive design of clinical trials from a Bayesian perspective for statistically not so sophisticated readers. Adaptive designs are attracting a keen interest in several disciplines, from a theoretical viewpoint and also—potentially—from a practical one, and Bayesian adaptive designs, in particular, have raised high expectations in clinical trials. The main conceptual tools are highlighted here, with a mention of several trial designs proposed in the literature that use these methods, including some of the registered Bayesian adaptive trials to this date. This review aims at complementing the existing ones on this topic, pointing at further interesting reading material.

Infectious diseases epidemiology, quantitative methodology, and clinical research in the midst of the COVID-19 pandemic: Perspective from a European country

Starting from historic reflections, the current SARS-CoV-2 induced COVID-19 pandemic is examined from various perspectives, in terms of what it implies for the implementation of non-pharmaceutical interventions, the modeling and monitoring of the epidemic, the development of early-warning systems, the study of mortality, prevalence estimation, diagnostic and serological testing, vaccine development, and ultimately clinical trials. Emphasis is placed on how the pandemic had led to unprecedented speed in methodological and clinical development, the pitfalls thereof, but also the opportunities that it engenders for national and international collaboration, and how it has simplified and sped up procedures. We also study the impact of the pandemic on clinical trials in other indications. We note that it has placed biostatistics, epidemiology, virology, infectiology, and vaccinology, and related fields in the spotlight in an unprecedented way, implying great opportunities, but also the need to communicate effectively, often amidst controversy.

Statistical Considerations for Trials in Adjuvant Treatment of Colorectal Cancer

The design of the best possible clinical trials of adjuvant interventions in colorectal cancer will entail the use of both time-tested and novel methods that allow efficient, reliable and patient-relevant therapeutic development. The ultimate goal of this endeavor is to safely and expeditiously bring to clinical practice novel interventions that impact patient lives. In this paper, we discuss statistical aspects and provide suggestions to optimize trial design, data collection, study implementation, and the use of predictive biomarkers and endpoints in phase 3 trials of systemic adjuvant therapy. We also discuss the issues of collaboration and patient centricity, expecting that several novel agents with activity in the (neo)adjuvant therapy of colon and rectal cancers will become available in the near future.

[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.

Imbalanced randomization in clinical trials

Randomization is a common technique used in clinical trials to eliminate potential bias and confounders in a patient population. Equal allocation to treatment groups is the standard due to its optimal efficiency in many cases. However, in certain scenarios, unequal allocation can improve efficiency. In superiority trials with more than two groups, the optimal randomization is not always a balanced randomization. In noninferiority (NI) trials, additive margin with equal variance is the http://www.statlab.wisc.edu/shiny/SSNI/.

Reprioritizing Risk and Benefit: The Future of Study Design in Early-Phase Cancer Research

The scientific purpose of phase I trials is to determine the maximum tolerated dose and/or optimal biological dose of experimental agents. Yet most participants in phase I oncology trials enroll hoping for direct medical benefit. The most common phase I trial designs use low starting doses and escalate cautiously in a "risk-escalation" model focused on minimizing risk for each participant. This approach ensures that a proportion of subjects will likely not receive any benefit, even if the intervention proves to be successful at appropriate doses. In this article, we propose that trial designs should employ dosing strategies that increase chances of providing benefit if the investigational agent should prove to be successful while limiting risk to reasonable levels. We then describe how adaptive trial designs can facilitate refined dose optimization based on both therapeutic benefit and toxicity, which can simultaneously decrease the risk of harm while increasing the chances of benefit.

Master protocols in immuno-oncology: do novel drugs deserve novel designs?

The rapid rise to fame of immuno-oncology (IO) drugs has generated unprecedented interest in the industry, patients and doctors, and has had a major impact in the treatment of most cancers. An interesting aspect in the clinical development of many IO agents is the increasing reliance on nonconventional trial design, including the so-called 'master protocols' that incorporate various adaptive features and often heavily rely on biomarkers to select patient populations most likely to benefit. These novel designs promise to maximize the clinical benefit that can be reaped from clinical research, but are not without costs. Their acceptance as solid evidence basis for use outside of the research context requires profound cultural changes by multiple stakeholders, including regulatory bodies, decision-makers, statisticians, researchers, doctors and, most importantly, patients. Here we review characteristics of recent and ongoing trials testing IO drugs with unconventional design, and we highlight trends and critical aspects.

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.

Bias in retrospective analyses of biomarker effect using data from an outcome-adaptive randomized trial

BACKGROUND/AIMS

Biomarker-stratified outcome-adaptive randomization trials, in which randomization probabilities depend on both biomarker value and outcomes of previously treated patients, are receiving increased attention in oncology research. Data from these trials can also form the basis of investigation of additional biomarkers that may not have been incorporated into the original trial design. In this article, we investigate the validity of a standard analytical method that utilizes data from a biomarker-stratified outcome-adaptive randomization trial to assess the effect of a newly identified biomarker on patient outcomes.

METHODS

In the context of an ancillary biomarker study for a two-arm phase II trial with a response endpoint, we conduct analytic and simulation studies to investigate bias in estimated biomarker effects under outcome-adaptive randomization. Conditions under which bias arises and magnitude of the bias are examined in several settings. We then propose unbiased estimators of biomarker effects with appropriate variance estimators.

RESULTS

We demonstrate that use of biomarker-stratified outcome-adaptive randomization perturbs the patient population and treatment assignments. Consequently, application of standard analysis methods to data from an outcome-adaptive randomization trial either to estimate prognostic effect of a new biomarker in uniformly treated patients or to estimate effect of treatment in relation to the new biomarker can lead to substantially biased estimates. The proposed adjusted estimators are asymptotically unbiased, and the proposed variance estimators correctly reflect the sample variability in the estimators.

CONCLUSION

This article demonstrates existence of bias when standard, naïve statistical methods are utilized to assess biomarker effects using data from a biomarker-stratified outcome-adaptive randomization trial, and hence that results from naïve analyses must be interpreted with great caution. These findings highlight that, in an era where data and specimens are increasingly being shared for biomarker studies, care must be taken to document and understand implications of the study design under which specimens or data have been obtained.

Umbrella and basket trials in oncology: ethical challenges

BACKGROUND

Novel precision oncology trial designs, such as basket and umbrella trials, are designed to test new anticancer agents in more effective and affordable ways. However, they present some ethical concerns referred to scientific validity, risk-benefit balance and informed consent. Our aim is to discuss these issues in basket and umbrella trials, giving examples of two ongoing cancer trials: NCI-MATCH (National Cancer Institute - Molecular Analysis for Therapy Choice) and Lung-MAP (Lung Cancer Master Protocol) study.

MAIN BODY

We discuss three ethical requirements for clinical trials which may be challenged in basket and umbrella trial designs. Firstly, we consider scientific validity. Thanks to the new trial designs, patients with rare malignancies have the opportunity to be enrolled and benefit from the trial, but due to insufficient accrual, the trial may generate clinically insignificant findings. Inadequate sample size in study arms and the use of surrogate endpoints may result in a drug approval without confirmed efficacy. Moreover, complexity, limited quality and availability of tumor samples may not only introduce bias and result in unreliable and unrepresentative findings, but also can potentially harm patients and assign them to an inappropriate therapy arm. Secondly, we refer to benefits and risks. Novel clinical trials can gain important knowledge on the variety of tumors, which can be used in future trials to develop effective therapies. However, they offer limited direct benefits to patients. All potential participants must wait about 2 weeks for the results of the genetic screening, which may be stressful and produce anxiety. The enrollment of patients whose tumors harbor multiple mutations in treatments matching a single mutation may be controversial. As to informed consent - the third requirement we discuss, the excessive use of phrases like "personalized medicine", "tailored therapy" or "precision oncology" might be misleading and cause personal convictions that the study protocol is designed to fulfill the individual health-related needs of participants.

CONCLUSIONS

We suggest that further approaches should be implemented to enhance scientific validity, reduce misunderstandings and risks, thus maximizing the benefits to society and to trial participants.

When to keep it simple - adaptive designs are not always useful

BACKGROUND

Adaptive designs are a wide class of methods focused on improving the power, efficiency and participant benefit of clinical trials. They do this through allowing information gathered during the trial to be used to make changes in a statistically robust manner - the changes could include which treatment arms patients are enrolled to (e.g. dropping non-promising treatment arms), the allocation ratios, the target sample size or the enrolment criteria of the trial. Generally, we are enthusiastic about adaptive designs and advocate their use in many clinical situations. However, they are not always advantageous. In some situations, they provide little efficiency advantage or are even detrimental to the quality of information provided by the trial. In our experience, factors that reduce the efficiency of adaptive designs are routinely downplayed or ignored in methodological papers, which may lead researchers into believing they are more beneficial than they actually are.

MAIN TEXT

In this paper, we discuss situations where adaptive designs may not be as useful, including situations when the outcomes take a long time to observe, when dropping arms early may cause issues and when increased practical complexity eliminates theoretical efficiency gains.

CONCLUSION

Adaptive designs often provide notable efficiency benefits. However, it is important for investigators to be aware that they do not always provide an advantage. There should always be careful consideration of the potential benefits and disadvantages of an adaptive design.

Using Adaptive Designs to Avoid Selecting the Wrong Arms in Multiarm Comparative Effectiveness Trials

Limited resources are a challenge when planning comparative effectiveness studies of multiple promising treatments, often prompting study planners to reduce the sample size to meet the financial constraints. The practical solution is often to increase the efficiency of this sample size by selecting a pair of treatments among the pool of promising treatments before the clinical trial begins. The problem with this approach is that the investigator may inadvertently leave out the most beneficial treatment. This paper demonstrates a possible solution to this problem by using Bayesian adaptive designs. We use a planned comparative effectiveness clinical trial of treatments for sialorrhea in amyotrophic lateral sclerosis as an example of the approach. Rather than having to guess at the two best treatments to compare based on limited data, we suggest putting more arms in the trial and letting response adaptive randomization (RAR) determine better arms. To ground this study relative to previous literature we first compare RAR, adaptive equal randomization (ER), arm(s) dropping, and a fixed design. Given the goals of this trial we demonstrate that we may avoid 'type III errors' - inadvertently leaving out the best treatment - with little loss in power compared to a two-arm design, even when choosing the correct two arms for the two-armed design. There are appreciable gains in power when the two arms are prescreened at random.

Phase II Trials in Drug Development and Adaptive Trial Design

Phase II clinical studies represent a critical point in determining drug costs, and phase II is a poor predictor of drug success: >30% of drugs entering phase II studies fail to progress, and >58% of drugs go on to fail in phase III. Adaptive clinical trial design has been proposed as a way to reduce the costs of phase II testing by providing earlier determination of futility and prediction of phase III success, reducing overall phase II and III trial sizes, and shortening overall drug development time. This review examines issues in phase II testing and adaptive trial design.

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.

Bayesian selective response-adaptive design using the historical control

High quality historical control data, if incorporated, may reduce sample size, trial cost, and duration. A too optimistic use of the data, however, may result in bias under prior-data conflict. Motivated by well-publicized two-arm comparative trials in stroke, we propose a Bayesian design that both adaptively incorporates historical control data and selectively adapt the treatment allocation ratios within an ongoing trial responsively to the relative treatment effects. The proposed design differs from existing designs that borrow from historical controls. As opposed to reducing the number of subjects assigned to the control arm blindly, this design does so adaptively to the relative treatment effects only if evaluation of cumulated current trial data combined with the historical control suggests the superiority of the intervention arm. We used the effective historical sample size approach to quantify borrowed information on the control arm and modified the treatment allocation rules of the doubly adaptive biased coin design to incorporate the quantity. The modified allocation rules were then implemented under the Bayesian framework with commensurate priors addressing prior-data conflict. Trials were also more frequently concluded earlier in line with the underlying truth, reducing trial cost, and duration and yielded parameter estimates with smaller standard errors.

Considerations for adaptive design in pediatric clinical trials: study protocol for a systematic review, mixed-methods study, and integrated knowledge translation plan

Background

Although children have historically been excluded from clinical trials (CTs), many require medicines tested and approved in CTs, forcing health care providers to treat their pediatric patients based on extrapolated data. Unfortunately, traditional randomized CTs can be slow and resource-intensive, and they often require multi-center collaboration. However, an adaptive design (AD) framework for CTs could be used to increase the efficiency of pediatric CTs by incorporating prospectively planned modifications to CT methods without undermining the integrity or validity of the study. There are many possible adaptations, but each will have ethical, logistical, and statistical implications. It remains unclear which adaptations (or combinations thereof) will lead to real-world improvements in pediatric CT efficiency. This study will identify, evaluate, and synthesize the various regulatory, ethical, logistical, and statistical considerations and emerging issues of AD in CTs that could be used to evaluate the use of drugs in children.

Methods/design

Following the development of a peer-reviewed search strategy, a systematic review on AD in CTs will be conducted. Data on regulatory, ethical, logistic, and statistical considerations as well as population and trial design characteristics will be synthesized. A mixed-methods study including surveys and focus groups with regulators, research ethics board members, biostatisticians, clinicians, and scientists, as well as representatives from patient groups and the public will evaluate the opportunities and challenges in applying AD in trials enrolling children and propose recommendations on best practices.

Discussion

This study will deliver practical recommendations on the use of AD in pediatric CTs. Collaboration and consultation with national and global partners will ensure that our results meet the needs of researchers, regulators, and patients, both locally and globally, and that they remain current and relevant by engaging a wide variety of stakeholders. Overall, this research will enrich the knowledge base regarding if, how, and when AD can be used to answer research questions with fewer resources while still meeting the highest ethical standards and regulatory requirements for CTs. In turn, this will result in increased high-quality clinical research needed by health care providers so they have access to appropriate, population-specific evidence regarding the safe and effective use of medicines in children.

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

To give substance to the rhetoric of 'learning health systems', a variety of novel trial designs are being explored to more seamlessly integrate research with medical practice, reduce study duration and reduce the number of participants allocated to ineffective interventions. Many of these designs rely on response adaptive randomisation (RAR). However, critics charge that RAR is unethical on the grounds that it violates the principle of equipoise. In this paper, I reconstruct critiques of RAR as holding that it is inconsistent with five important ethical principles. I then argue that these criticisms rest on a faulty view of equipoise encouraged by the idea that a RAR study models the beliefs of a single rational agent about the relative merits of the interventions being studied. I outline a view in which RAR models an idealised health system in which diverse communities of fully informed experts shrink or grow as their constituent members update their expert opinions in light of reliable medical evidence. I show how a proper understanding of clinical equipoise can reconcile this conception of RAR with these five ethical principles. This analysis removes an in-principle objection to RAR and sheds important light on the relationship between clinical equipoise and transient diversity in the scientific community.