Current Statistical Considerations and Regulatory Perspectives on the Planning of Confirmatory Basket, Umbrella, and Platform Trials

The ESMO Tumour-Agnostic Classifier and Screener (ETAC-S): a tool for assessing tumour-agnostic potential of molecularly guided therapies and for steering drug development

BACKGROUND

Advances in precision oncology led to approval of tumour-agnostic molecularly guided treatment options (MGTOs). The minimum requirements for claiming tumour-agnostic potential remain elusive.

METHODS

The European Society for Medical Oncology (ESMO) Precision Medicine Working Group (PMWG) coordinated a project to optimise tumour-agnostic drug development. International experts examined and summarised the publicly available data used for regulatory assessment of the tumour-agnostic indications approved by the US Food and Drug Administration and/or the European Medicines Agency as of December 2023. Different scenarios of minimum objective response rate (ORR), number of tumour types investigated, and number of evaluable patients per tumour type were assessed for developing a screening tool for tumour-agnostic potential. This tool was tested using the tumour-agnostic indications approved during the first half of 2024. A taxonomy for MGTOs and a framework for tumour-agnostic drug development were conceptualised.

RESULTS

Each tumour-agnostic indication had data establishing objective response in at least one out of five patients (ORR ≥ 20%) in two-thirds (≥4) of the investigated tumour types, with at least five evaluable patients in each tumour type. These minimum requirements were met by tested indications and may serve as a screening tool for tumour-agnostic potential, requiring further validation. We propose a conceptual taxonomy classifying MGTOs based on the therapeutic effect obtained by targeting a driver molecular aberration across tumours and its modulation by tumour-specific biology: tumour-agnostic, tumour-modulated, or tumour-restricted. The presence of biology-informed mechanistic rationale, early regulatory advice, and adequate trial design demonstrating signs of biology-driven tumour-agnostic activity, followed by confirmatory evidence, should be the principles for tumour-agnostic drug development.

CONCLUSION

The ESMO Tumour-Agnostic Classifier (ETAC) focuses on the interplay of targeted driver molecular aberration and tumour-specific biology modulating the therapeutic effect of MGTOs. We propose minimum requirements to screen for tumour-agnostic potential (ETAC-S) as part of tumour-agnostic drug development. Definition of ETAC cut-offs is warranted.

Adding experimental treatment arms to multi-arm multi-stage platform trials in progress

Multi-arm multi-stage (MAMS) platform trials efficiently compare several treatments with a common control arm. Crucially MAMS designs allow for adjustment for multiplicity if required. If for example, the active treatment arms in a clinical trial relate to different dose levels or different routes of administration of a drug, the strict control of the family-wise error rate (FWER) is paramount. Suppose a further treatment becomes available, it is desirable to add this to the trial already in progress; to access both the practical and statistical benefits of the MAMS design. In any setting where control of the error rate is required, we must add corresponding hypotheses without compromising the validity of the testing procedure.To strongly control the FWER, MAMS designs use pre-planned decision rules that determine the recruitment of the next stage of the trial based on the available data. The addition of a treatment arm presents an unplanned change to the design that we must account for in the testing procedure. We demonstrate the use of the conditional error approach to add hypotheses to any testing procedure that strongly controls the FWER. We use this framework to add treatments to a MAMS trial in progress. Simulations illustrate the possible characteristics of such procedures.

Adaptive platform trials: the impact of common controls on type one error and power

Platform trials offer a framework to study multiple interventions in one trial with the opportunity of opening and closing arms. The use of common controls can increase efficiency as compared to individual controls. The need for multiplicity adjustment because of common controls is currently a debate among researchers, pharmaceutical companies, and regulators. The impact of common controls on the type one error in a fixed platform trial, i.e. when all treatments start and end recruitment at the same time, has been discussed in the literature before. We complement these findings by investigating the impact of a common control on the type one error and power in a flexible platform trial, i.e. when one arm joins the platform later. We derived the correlation of test statistics to assess the impact of the overlap and compared the results to a trial with individual controls. Furthermore, we evaluate the power, and the impact of multiplicity adjustment on the power in fixed and flexible platform trials. These methodological considerations are complemented by a regulatory guideline review. With multiple arms, the FWER is inflated when no multiplicity adjustment is applied. However, the FWER inflation is smaller with common controls than with individual controls. Even after multiplicity adjustment, a trial with common controls is often beneficial in terms of sample size and power. However, in some cases, the trial with common controls loses the efficiency gain and it might be advisable to run a separate trial rather than joining a platform trial.

Innovative approaches for vaccine trials as a key component of pandemic preparedness - a white paper

BACKGROUND

WHO postulates the application of adaptive design features in the global clinical trial ecosystem. However, the adaptive platform trial (APT) methodology has not been widely adopted in clinical research on vaccines.

METHODS

The VACCELERATE Consortium organized a two-day workshop to discuss the applicability of APT methodology in vaccine trials under non-pandemic as well as pandemic conditions. Core aspects of the discussions are summarized in this article.

RESULTS

An "ever-warm" APT appears ideally suited to improve efficiency and speed of vaccine research. Continuous learning based on accumulating APT trial data allows for pre-planned adaptations during its course. Given the relative design complexity, alignment of all stakeholders at all stages of an APT is central. Vaccine trial modelling is crucial, both before and in a pandemic emergency. Various inferential paradigms are possible (frequentist, likelihood, or Bayesian). The focus in the interpandemic interval may be on research gaps left by industry trials. For activation in emergency, template Disease X protocols of syndromal design for pathogens yet unknown need to be stockpiled and updated regularly. Governance of a vaccine APT should be fully integrated into supranational pandemic response mechanisms.

DISCUSSION

A broad range of adaptive features can be applied in platform trials on vaccines. Faster knowledge generation comes with increased complexity of trial design. Design complexity should not preclude simple execution at trial sites. Continuously generated evidence represents a return on investment that will garner societal support for sustainable funding. Adaptive design features will naturally find their way into platform trials on vaccines.

Regulatory Issues of Platform Trials: Learnings from EU-PEARL

Although platform trials have many benefits, the complexity of these designs may result not only in increased methodological but also regulatory and ethical challenges. These aspects were addressed as part of the IMI project EU Patient-Centric Clinical Trial Platforms (EU-PEARL). We reviewed the available guidelines on platform trials in the European Union and the United States. This is supported and complemented by feedback received from regulatory interactions with the European Medicines Agency and the US Food and Drug Administration. Throughout the project we collected the needs of all relevant stakeholders including ethics committees, regulators, and health technology assessment bodies through active dialog and dedicated stakeholder workshops. Furthermore, we focused on methodological aspects and where applicable identified the corresponding guidance. Learnings from the guideline review, regulatory interactions, and workshops are provided. Based on these, a master protocol template was developed. Issues that still need harmonization or clarification in guidelines or where further methodological research is needed are also presented. These include questions around clinical trial submissions in Europe, the need for multiplicity control across the whole master protocol, the use of non-concurrent controls, and the impact of different randomization schemes. Master protocols are an efficient and patient-centered clinical trial design that can expedite drug development. However, they can also introduce additional operational and regulatory complexities. It is important to understand the different requirements of stakeholders upfront and address them in the trial. While relevant guidance is increasing, early dialog with relevant stakeholders can help to further support such designs.

Advantages and Challenges of Platform Trials for Disease Modifying Therapies in Parkinson's Disease

Traditional drug development in Parkinson's disease (PD) faces significant challenges because of its protracted timeline and high costs. In response, innovative master protocols are emerging and designed to address multiple research questions within a single overarching protocol. These trials may offer advantages such as increased efficiency, agility in adding new treatment arms, and potential cost savings. However, they also present organizational, methodological, funding, regulatory, and sponsorship challenges. We review the potential of master protocols, focusing on platform trials, for disease modifying therapies in PD. These trials share a common control group and allow for the termination or addition of treatment arms during a trial with non-predetermined end. Specific issues exist for a platform trial in the PD field considering the heterogeneity of patients in terms of phenotype, genotype and staging, the confounding effects of symptomatic treatments, and the choice of outcome measures with no consensus on a non-clinical biomarker to serve as a surrogate and the slowness of PD progression. We illustrate these aspects using the examples of the main PD platform trials currently in development with each one targeting distinct goals, populations, and outcomes. Overall, platform trials hold promise in expediting the evaluation of potential therapies for PD. However, it remains to be proven whether these theoretical benefits will translate into increased production of high-quality trial data. Success also depends on the willingness of pharmaceutical companies to engage in such trials and whether this approach will ultimately hasten the identification and licensing of effective disease-modifying drugs. © 2024 International Parkinson and Movement Disorder Society.

Biomarker-driven basket trial designs: origins and new methodological developments

Due to increased use of gene sequencing techniques, understanding of cancer on a molecular level has evolved, in terms of both diagnosis and evaluation in response to initial therapies. In parallel, clinical trials meant to evaluate molecularly-driven interventions through assessment of both treatment effects and putative predictive biomarker effects are being employed to advance the goals of precision medicine. Basket trials investigate one or more biomarker-targeted therapies across multiple cancer types in a tumor location agnostic fashion. The review article offers an overview of the traditional forms of such designs, the practical challenges facing each type of design, and then review novel adaptations proposed in the last few years, categorized into Bayesian and Classical Frequentist perspectives. The review article concludes by summarizing potential advantages and limitations of the new trial design solutions.

How Much More Efficient Are Adaptive Platform Trials Than Multiple Stand-Alone Trials? A Comprehensive Simulation Study for Streamlining Drug Development During a Pandemic

With the coronavirus disease 2019 (COVID-19) pandemic, there is growing interest in utilizing adaptive platform clinical trials (APTs), in which multiple drugs are compared with a single common control group, such as a placebo or standard-of-care group. APTs evaluate several drugs for one disease and accept additions or exclusions of drugs as the trials progress; however, little is known about the efficiency of APTs over multiple stand-alone trials. In this study, we simulated the total development period, total sample size, and statistical operating characteristics of APTs and multiple stand-alone trials in drug development settings for hospitalized patients with COVID-19. Simulation studies using selected scenarios reconfirmed several findings regarding the efficiency of APTs. The APTs without staggered addition of drugs showed a shorter total development period than stand-alone trials, but the difference rapidly diminished if patient's enrollment was accelerated during the trials owing to the spread of infection. APTs with staggered addition of drugs still have the possibility of reducing the total development period compared with multiple stand-alone trials in some cases. Our study demonstrated that APTs could improve efficiency relative to multiple stand-alone trials regarding the total development period and total sample size without undermining statistical validity; however, this improvement varies depending on the speed of patient enrollment, sample size, presence/absence of family-wise error rate adjustment, allocation ratio between drug and placebo groups, and interval of staggered addition of drugs. Given the complexity of planning and implementing APT, the decision to implement APT during a pandemic must be made carefully.

Bayesian Methods for Information Borrowing in Basket Trials: An Overview

Basket trials allow simultaneous evaluation of a single therapy across multiple cancer types or subtypes of the same cancer. Since the same treatment is tested across all baskets, it may be desirable to borrow information across them to improve the statistical precision and power in estimating and detecting the treatment effects in different baskets. We review recent developments in Bayesian methods for the design and analysis of basket trials, focusing on the mechanism of information borrowing. We explain the common components of these methods, such as a prior model for the treatment effects that embodies an assumption of exchangeability. We also discuss the distinct features of these methods that lead to different degrees of borrowing. Through simulation studies, we demonstrate the impact of information borrowing on the operating characteristics of these methods and discuss its broader implications for drug development. Examples of basket trials are presented in both phase I and phase II settings.

Drawing causal inferences from randomized controlled trials in psychotherapy research

OBJECTIVE

Randomized control trials (RCTs) have played a critical role in psychotherapy research. This article discusses RCTs in the context of the criteria for drawing causal inferences in psychotherapy and intervention research more generally. The article also highlights underused variations of RCTs and how they not only establish causal relations but also address critical questions that can improve our intervention portfolio and patient care.

CONCLUSION

Random assignment is discussed in terms of what it can and cannot do in relation to drawing conclusions about the effects of interventions. Finally, RCTs are examined in the context of multiple questions that can guide therapy research, improve patient care, and develop treatments that reach people in need of psychological services.

Precision medicine in regulatory decision making: Biomarkers used for patient selection in European Public Assessment Reports from 2018 to 2020

Biomarkers can guide precision medicine in clinical trials and practice. They can increase clinical trials' efficiency through selection of study populations more likely to benefit from treatment, thus increasing statistical power and reducing sample size requirements or study duration. We performed a narrative synthesis to explore biomarker utilization for patient selection to guide precision medicine trials in marketing authorization dossiers of centrally approved medicines in Europe between 2018 and 2020 and analyzed in-depth those that eventually included biomarkers in the medicines' indications. From 119 eligible products, 26 included a biomarker in the indication, of which most were oncology products (n = 15). Included biomarkers were often known from literature or from previously approved products in the European Union or the United States. Additionally, 52 dossiers mentioned one or more biomarkers for patient selection in their clinical efficacy and safety information. Although these were not always included in the medicines' indication, they were often implicitly embedded in condition definitions adopted from clinical guidelines or practice. In 15 out of the 26 medicines with a biomarker-guided indication, only biomarker-positive populations were included in the main clinical studies supporting the marketing authorization. These studies were mostly randomized controlled trials or single-arm trials; only two products were studied for multiple indications in an innovative basket trial. Definitions of biomarkers could be subject of debate and needed adaptation after post hoc analyses requested by the assessment committee in four cases, stressing the importance of thorough justification of these definitions to include the right population for an optimal benefit-risk balance, enabling precise medicine.

Modified study designs to expand treatment options in personalised oncology: a multistakeholder view

Personalised oncology, whereby patients are given therapies based on their molecular tumour profile, is rapidly becoming an essential part of optimal clinical care, at least partly facilitated by recent advances in next-generation sequencing-based technology using liquid- and tissue-based biopsies. Consequently, clinical trials have shifted in approach, from traditional studies evaluating cytotoxic chemotherapy in largely histology-based populations to modified, biomarker-driven studies (e.g. basket, umbrella, platform) of molecularly guided therapies and cancer immunotherapies in selected patient subsets. Such modified study designs may assess, within the same trial structure, multiple cancer types and treatments, and should incorporate a multistakeholder perspective. This is key to generating complementary, fit-for-purpose and timely evidence for molecularly guided therapies that can be used as proof-of-concept to inform further study designs, lead to approval by regulatory authorities and be used as confirmation of clinical benefit for health technology assessment bodies. In general, the future of cancer clinical trials requires a framework for the application of innovative technologies and dynamic design methodologies, in order to efficiently transform scientific discoveries into clinical utility. Next-generation, modified studies that involve the joint efforts of all key stakeholders will offer individualised strategies that ultimately contribute to globalised knowledge and collective learning. In this review, we outline the background and purpose of such modified study designs and detail key aspects from a multistakeholder perspective. We also provide methodological considerations for designing the studies and highlight how insights from already-ongoing studies may address current challenges and opportunities in the era of personalised oncology.

Inflammation across tissues: can shared cell biology help design smarter trials?

Immune-mediated inflammatory diseases (IMIDs) are responsible for substantial global disease burden and associated health-care costs. Traditional models of research and service delivery silo their management within organ-based medical disciplines. Very often patients with disease in one organ have comorbid involvement in another, suggesting shared pathogenic pathways. Moreover, different IMIDs are often treated with the same drugs (including glucocorticoids, immunoregulators and biologics). Unlocking the cellular basis of these diseases remains a major challenge, leading us to ask why, if these diseases have so much in common, they are not investigated in a common manner. A tissue-based, cellular understanding of inflammation might pave the way for cross-disease, cross-discipline basket trials (testing one drug across two or more diseases) to reduce the risk of failure of early-phase drug development in IMIDs. This new approach will enable rapid assessment of the efficacy of new therapeutic agents in cross-disease translational research in humans.

Pooling Different Placebos as a Control Group in a Randomized Platform Trial: Benefits and Challenges From Experience in the ACTIV-2 COVID-19 Trial

Adaptive platform trials were implemented during the coronavirus disease 2019 (COVID-19) pandemic to rapidly evaluate therapeutics, including the placebo-controlled phase 2/3 ACTIV-2 trial, which studied 7 investigational agents with diverse routes of administration. For each agent, safety and efficacy outcomes were compared to a pooled placebo control group, which included participants who received a placebo for that agent or for other agents in concurrent evaluation. A 2-step randomization framework was implemented to facilitate this. Over the study duration, the pooled placebo design achieved a reduction in sample size of 6% versus a trial involving distinct placebo control groups for evaluating each agent. However, a 26% reduction was achieved during the period when multiple agents were in parallel phase 2 evaluation. We discuss some of the complexities implementing the pooled placebo design versus a design involving nonoverlapping control groups, with the aim of informing the design of future platform trials. Clinical Trials Registration. NCT04518410.

Online error rate control for platform trials

Platform trials evaluate multiple experimental treatments under a single master protocol, where new treatment arms are added to the trial over time. Given the multiple treatment comparisons, there is the potential for inflation of the overall type I error rate, which is complicated by the fact that the hypotheses are tested at different times and are not necessarily pre-specified. Online error rate control methodology provides a possible solution to the problem of multiplicity for platform trials where a relatively large number of hypotheses are expected to be tested over time. In the online multiple hypothesis testing framework, hypotheses are tested one-by-one over time, where at each time-step an analyst decides whether to reject the current null hypothesis without knowledge of future tests but based solely on past decisions. Methodology has recently been developed for online control of the false discovery rate as well as the familywise error rate (FWER). In this article, we describe how to apply online error rate control to the platform trial setting, present extensive simulation results, and give some recommendations for the use of this new methodology in practice. We show that the algorithms for online error rate control can have a substantially lower FWER than uncorrected testing, while still achieving noticeable gains in power when compared with the use of a Bonferroni correction. We also illustrate how online error rate control would have impacted a currently ongoing platform trial.

The Trial within Cohorts (TwiCs) study design in oncology: experience and methodological reflections

A Trial within Cohorts (TwiCs) study design is a trial design that uses the infrastructure of an observational cohort study to initiate a randomized trial. Upon cohort enrollment, the participants provide consent for being randomized in future studies without being informed. Once a new treatment is available, eligible cohort participants are randomly assigned to the treatment or standard of care. Patients randomized to the treatment arm are offered the new treatment, which they can choose to refuse. Patients who refuse will receive standard of care instead. Patients randomized to the standard of care arm receive no information about the trial and continue receiving standard of care as part of the cohort study. Standard cohort measures are used for outcome comparisons. The TwiCs study design aims to overcome some issues encountered in standard Randomized Controlled Trials (RCTs). An example of an issue in standard RCTs is the slow patient accrual. A TwiCs study aims to improve this by selecting patients using a cohort and only offering the intervention to patients in the intervention arm. In oncology, the TwiCs study design has gained increasing interest during the last decade. Despite its potential advantages over RCTs, the TwiCs study design has several methodological challenges that need careful consideration when planning a TwiCs study. In this article, we focus on these challenges and reflect on them using experiences from TwiCs studies initiated in oncology. Important methodological challenges that are discussed are the timing of randomization, the issue of non-compliance (refusal) after randomization in the intervention arm, and the definition of the intention-to-treat effect in a TwiCs study and how this effect is related to its counterpart in standard RCTs.

Practical basket design for binary outcomes with control of family-wise error rate

BACKGROUND

A basket trial is a type of clinical trial in which eligibility is based on the presence of specific molecular characteristics across subpopulations with different cancer types. The existing basket designs with Bayesian hierarchical models often improve the efficiency of evaluating therapeutic effects; however, these models calibrate the type I error rate based on the results of simulation studies under various selected scenarios. The theoretical control of family-wise error rate (FWER) is important for decision-making regarding drug approval.

METHODS

In this study, we propose a new Bayesian two-stage design with one interim analysis for controlling FWER at the target level, along with the formulations of type I and II error rates. Since the difficulty lies in the complexity of the theoretical formulation of the type I error rate, we devised the simulation-based method to approximate the type I error rate.

RESULTS

The proposed design enabled adjustment of the cutoff value to control the FWER at the target value in the final analysis. The simulation studies demonstrated that the proposed design can be used to control the well-approximated FWER below the target value even in situations where the number of enrolled patients differed among subpopulations.

CONCLUSIONS

The accrual number of patients is sometimes unable to reach the pre-defined value; therefore, existing basket designs may not ensure defined operating characteristics before beginning the trial. The proposed design that enables adjustment of the cutoff value to control FWER at the target value based on the results in the final analysis would be a better alternative.

The population-wise error rate for clinical trials with overlapping populations

We introduce a new multiple type I error criterion for clinical trials with multiple, overlapping populations. Such trials are of interest in precision medicine where the goal is to develop treatments that are targeted to specific sub-populations defined by genetic and/or clinical biomarkers. The new criterion is based on the observation that not all type I errors are relevant to all patients in the overall population. If disjoint sub-populations are considered, no multiplicity adjustment appears necessary, since a claim in one sub-population does not affect patients in the other ones. For intersecting sub-populations we suggest to control the average multiple type I error rate, i.e. the probability that a randomly selected patient will be exposed to an inefficient treatment. We call this the population-wise error rate, exemplify it by a number of examples and illustrate how to control it with an adjustment of critical boundaries or adjusted p -values. We furthermore define corresponding simultaneous confidence intervals. We finally illustrate the power gain achieved by passing from family-wise to population-wise error rate control with two simple examples and a recently suggested multiple-testing approach for umbrella trials.

Statistical analysis plan for the motor neuron disease systematic multi-arm adaptive randomised trial (MND-SMART)

BACKGROUND

MND-SMART is a platform, multi-arm, multi-stage, multi-centre, randomised controlled trial recruiting people with motor neuron disease. Initially, the treatments memantine and trazodone will each be compared against placebo, but other investigational treatments will be introduced into the trial later. The co-primary outcomes are the Amyotrophic Lateral Sclerosis Functional Rating Scale Revised (ALS-FRS-R) functional outcome, which is assessed longitudinally, and overall survival.

METHODS

Initially in MND-SMART, participants are randomised 1:1:1 via a minimisation algorithm to receive placebo or one of the two investigational treatments with up to 531 to be randomised in total. The comparisons between each research arm and placebo will be conducted in four stages, with the opportunity to cease further randomisations to poorly performing research arms at the end of stages 1 or 2. The final ALS-FRS-R analysis will be at the end of stage 3 and final survival analysis at the end of stage 4. The estimands for the co-primary outcomes are described in detail. The primary analysis of ALS-FRS-R at the end of stages 1 to 3 will involve fitting a normal linear mixed model to the data to calculate a mean difference in rate of ALS-FRS-R change between each research treatment and placebo. The pairwise type 1 error rate will be controlled, because each treatment comparison will generate its own distinct and separate interpretation. This publication is based on a formal statistical analysis plan document that was finalised and signed on 18 May 2022.

DISCUSSION

In developing the statistical analysis plan, we had to carefully consider several issues such as multiple testing, estimand specification, interim analyses, and statistical analysis of the repeated measurements of ALS-FRS-R. This analysis plan attempts to balance multiple factors, including minimisation of bias, maximising power and precision, and deriving clinically interpretable summaries of treatment effects.

TRIAL REGISTRATION

EudraCT Number, 2019-000099-41. Registered 2 October 2019, https://www.clinicaltrialsregister.eu/ctr-search/search?query=mnd-smart ClinicalTrials.gov, NCT04302870 . Registered 10 March 2020.

Les essais plateformes☆

Les essais plateformes connaissent depuis quelques années un essor important, amplifié récemment par la pandémie de coronavirus disease 2019 (COVID-19). La mise en œuvre d’un essai plateforme s’avère particulièrement utile dans certaines pathologies, notamment lorsqu’il y a un nombre important de candidats médicaments à évaluer, une évolution rapide du traitement de référence ou dans les situations de besoin urgent d’évaluation, au cours desquelles la mutualisation des protocoles et des infrastructures permet d’optimiser le nombre de patients à inclure, les coûts et les délais de réalisation de l’investigation. Toutefois, la spécificité des essais plateformes soulève des problématiques méthodologiques, éthiques et règlementaires, qui ont fait l’objet de la table ronde et qui sont exposées dans cet article. La table ronde a également été l’occasion d’aborder la complexité de la promotion et de la gestion des données liée à la multiplicité des partenaires, le financement et la gouvernance de ces essais, et le niveau d’acceptabilité de leurs résultats par les autorités compétentes.

Platform trials

For the past few years, platform trials have experienced a significant increase, recently amplified by the COVID-19 pandemic. The implementation of a platform trial is particularly useful in certain pathologies, particularly when there is a significant number of drug candidates to be assessed, a rapid evolution of the standard of care or in situations of urgent need for evaluation, during which the pooling of protocols and infrastructure optimizes the number of patients to be enrolled, the costs, and the deadlines for carrying out the investigation. However, the specificity of platform trials raises methodological, ethical, and regulatory issues, which have been the subject of the round table and which are presented in this article. The round table was also an opportunity to discuss the complexity of sponsorship and data management related to the multiplicity of partners, funding, and governance of these trials, and the level of acceptability of their findings by the competent authorities.

Online control of the False Discovery Rate in group-sequential platform trials

When testing multiple hypotheses, a suitable error rate should be controlled even in exploratory trials. Conventional methods to control the False Discovery Rate assume that all p-values are available at the time point of test decision. In platform trials, however, treatment arms enter and leave the trial at different times during its conduct. Therefore, the actual number of treatments and hypothesis tests is not fixed in advance and hypotheses are not tested at once, but sequentially. Recently, for such a setting the concept of online control of the False Discovery Rate was introduced. We propose several heuristic variations of the LOND procedure (significance Levels based On Number of Discoveries) that incorporate interim analyses for platform trials, and study their online False Discovery Rate via simulations. To adjust for the interim looks spending functions are applied with O'Brien-Fleming or Pocock type group-sequential boundaries. The power depends on the prior distribution of effect sizes, for example, whether true alternatives are uniformly distributed over time or not. We consider the choice of design parameters for the LOND procedure to maximize the overall power and investigate the impact on the False Discovery Rate by including both concurrent and non-concurrent control data.

Estimands and Complex Innovative Designs

Since the release of the ICH E9(R1) (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use Addendum on Estimands and Sensitivity Analysis in Clinical Trials to the Guideline on Statistical Principles for Clinical Trials) document in 2019, the estimand framework has become a fundamental part of clinical trial protocols. In parallel, complex innovative designs have gained increased popularity in drug development, in particular in early development phases or in difficult experimental situations. While the estimand framework is relevant to any study in which a treatment effect is estimated, experience is lacking as regards its application to these designs. In a basket trial for example, should a different estimand be specified for each subpopulation of interest, defined, for example, by cancer site? Or can a single estimand focusing on the general population (defined, for example, by the positivity to a certain biomarker) be used? In the case of platform trials, should a different estimand be proposed for each drug investigated? In this work we discuss possible ways of implementing the estimand framework for different types of complex innovative designs. We consider trials that allow adding or selecting experimental treatment arms, modifying the control arm or the standard of care, and selecting or pooling populations. We also address the potentially data-driven, adaptive selection of estimands in an ongoing trial and disentangle certain statistical issues that pertain to estimation rather than to estimands, such as the borrowing of nonconcurrent information. We hope this discussion will facilitate the implementation of the estimand framework and its description in the study protocol when the objectives of the trial require complex innovative designs.

Innovations in Clinical Development in Rare Diseases of Children and Adults: Small Populations and/or Small Patients

Many of the afflictions of children are rare diseases. This creates numerous drug development challenges related to small populations, including limited information about the disease state, enrollment challenges, and diminished incentives for pediatric development of novel therapies by pharmaceutical and biotechnology sponsors. We review selected innovations in clinical development that may partially mitigate some of these difficulties, starting with the concept of development efficiency for individual clinical trials, clinical programs (involving multiple trials for a single drug), and clinical portfolios of multiple drugs, and decision analysis as a tool to optimize efficiency. Development efficiency is defined as the ability to reach equally rigorous or more rigorous conclusions in less time, with fewer trial participants, or with fewer resources. We go on to discuss efficient methods for matching targeted therapies to biomarker-defined subgroups, methods for eliminating or reducing the need for natural history data to guide rare disease development, the use of basket trials to enhance efficiency by grouping multiple similar disease applications in a single clinical trial, and the use of alternative data sources including historical controls to augment or replace concurrent controls in clinical studies. Greater understanding and broader application of these methods could lead to improved therapies and/or more widespread and rapid access to novel therapies for rare diseases in both children and adults.

Design and analysis of umbrella trials: Where do we stand?

Background

The efficiencies that master protocol designs can bring to modern drug development have seen their increased utilization in oncology. Growing interest has also resulted in their consideration in non-oncology settings. Umbrella trials are one class of master protocol design that evaluates multiple targeted therapies in a single disease setting. Despite the existence of several reviews of master protocols, the statistical considerations of umbrella trials have received more limited attention.

Methods

We conduct a systematic review of the literature on umbrella trials, examining both the statistical methods that are available for their design and analysis, and also their use in practice. We pay particular attention to considerations for umbrella designs applied outside of oncology.

Findings

We identified 38 umbrella trials. To date, most umbrella trials have been conducted in early phase settings (73.7%, 28/38) and in oncology (92.1%, 35/38). The quality of statistical information available about conducted umbrella trials to date is poor; for example, it was impossible to ascertain how sample size was determined in the majority of trials (55.3%, 21/38). The literature on statistical methods for umbrella trials is currently sparse.

Conclusions

Umbrella trials have potentially great utility to expedite drug development, including outside of oncology. However, to enable lessons to be effectively learned from early use of such designs, there is a need for higher-quality reporting of umbrella trials. Furthermore, if the potential of umbrella trials is to be realized, further methodological research is required.

The Randomised Controlled Trial at the Intersection of Research Ethics and Innovation

The randomised controlled trial (RCT) has been considered for a long time as the gold standard for evidence generation to support regulatory decision making for medicines. The randomisation procedure involves an ethical dilemma since it means leaving the treatment choice to chance. Although currently contested, the ethical justification for the RCT that has gained widespread acceptance is the notion of 'clinical equipoise'. This state exists when "there is no consensus within the expert clinical community about the comparative merits of the alternatives to be tested"; it is argued that this confers the ethical grounds for the conduct of an RCT. The prominent position of the RCT is being challenged by new therapeutic modalities for which this study design may be unsuitable. Moreover, alternative approaches to evidence generation represent another area where innovation may have implications for the relevance of the RCT. Against the backdrop of the debate around the equipoise principle and some recent therapeutic and data analytical innovations, the aim of this article is to explore the current standing of the RCT from a regulatory perspective.

Expanding the scope, reach, and impact of evidence-based psychological treatments

The development and evaluation of evidence-based treatments (EBTs) for mental disorders represent an enormous advance with continued progress designed to understand the techniques and increase their use in clinical practice. This article suggests ways of expanding research along several fronts including the extension of the types of randomized controlled trials that are conducted, the use of more diverse samples to encompass different cultures and countries, the expansion of assessments to better reflect client functioning in everyday life, consideration of the impact of treatments for mental disorders on physical health, the careful evaluation of exceptional responders, the use of mixed-methods research, and the development of versions of EBTs that can be scaled. EBTs have been studied in well-controlled settings and extended to clinical settings, albeit less often. The least attention has been accorded their evaluation on a large scale to reach a greater portion of people in need of services but who do not receive any treatment.

The Role of Master Protocols in Pediatric Drug Development

Master protocols are innovative clinical trial designs that enable new approaches to analytics and operations, creating value for patients and drug developers. To date, the use of master protocols in pediatric drug development has been limited, focused primarily on pediatric oncology with limited experience in rare and ultra-rare pediatric diseases. This article explores the application of master protocols to pediatric programs required by FDA and EMA based on adult developmental programs. These required programs involve multiple assets developed in limited pediatric populations for registrational purposes. However, these required programs include the possibility for extrapolation of efficacy and safety from the adult population. The use of master protocols is a potential solution to the challenge of conducting clinical trials in small pediatric populations provided that such use would improve enrollment or reduce the required sample size. Toward that end, Janssen and Lilly have been working on a collaborative cross-company pediatric platform trial in pediatric Crohn's disease using an innovative Bayesian analysis. We describe how two competing companies can work together to design and execute the proposed platform, focusing on selected aspects-the usefulness of a single infrastructure, the regulatory submission process, the choice of control group, and the use of pediatric extrapolation. Master protocols offer the potential for great benefit in pediatrics by streamlining clinical development, with the goal of reducing the delay in pediatric marketing approvals when compared to adults so that children have timelier access to safe and effective medications.

The Bayesian Time Machine: Accounting for temporal drift in multi-arm platform trials

BACKGROUND

Multi-arm platform trials investigate multiple agents simultaneously, typically with staggered entry and exit of experimental treatment arms versus a shared control arm. In such settings, there is considerable debate whether to limit analyses for a treatment arm to concurrent randomized control subjects or to allow comparisons to both concurrent and non-concurrent (pooled) control subjects. The potential bias from temporal drift over time is at the core of this debate.

METHODS

We propose time-adjusted analyses, including a "Bayesian Time Machine," to model potential temporal drift in the entire study population, such that primary analyses can incorporate all randomized control subjects from the platform trial. We conduct a simulation study to assess performance relative to utilizing concurrent or pooled controls.

RESULTS

In multi-arm platform trials with staggered entry, analyses adjusting for temporal drift (either Bayesian or frequentist) have superior estimation of treatment effects and favorable testing properties compared to analyses using either concurrent or pooled controls. The Bayesian Time Machine generally provides estimates with greater precision and smaller mean square error than alternative approaches, at the risk of small bias and small Type I error inflation.

CONCLUSIONS

The Bayesian Time Machine provides a compromise between bias and precision by smoothing estimates across time and leveraging all available data for the estimation of treatment effects. Prior distributions controlling the behavior of dynamic smoothing across time must be pre-specified and carefully calibrated to the unique context of each trial, appropriately accounting for the population, disease, and endpoints.

Advancing innovative clinical trials to efficiently deliver medicines to patients

Complex innovative designs in clinical trials have the potential to increase efficiency and lower the cost of drug development, improving patient access to therapies. This article highlights designs and approaches based on a meeting linked to an ongoing FDA pilot program in the field.

COVID-19 drug research and the cohort multiple randomised controlled trial design

We read with interest the comment of R. Dal-Ré on the CORIMUNO-19 trials design [1–5]. The key argument is that the cohort multiple randomised controlled trial (cmRCT) design implies an alteration of the informed process that can only be justified if three requirements are fulfilled: the research has important social value, it poses no more than minimal risks to participants, and it would be impracticable to carry out without consent process modification [6]. R. Dal-Ré acknowledges that the first two requirements were fulfilled for CORIMUNO-19 trials, but argues that, given the huge number of patients hospitalised with severe or critical coronavirus disease 2019 (COVID-19) in March 2020, trials would have been feasible without consent modification.

The use of cohort multiple randomised controlled trials and epidemic crises https://bit.ly/39Obd4N

Risk and benefit for umbrella trials in oncology: a systematic review and meta-analysis

BACKGROUND

Umbrella clinical trials in precision oncology are designed to tailor therapies to the specific genetic changes within a tumor. Little is known about the risk/benefit ratio for umbrella clinical trials. The aim of our systematic review with meta-analysis was to evaluate the efficacy and safety profiles in cancer umbrella trials testing targeted drugs or a combination of targeted therapy with chemotherapy.

METHODS

Our study was prospectively registered in PROSPERO (CRD42020171494). We searched Embase and PubMed for cancer umbrella trials testing targeted agents or a combination of targeted therapies with chemotherapy. We included solid tumor studies published between 1 January 2006 and 7 October 2019. We measured the risk using drug-related grade 3 or higher adverse events (AEs), and the benefit by objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). When possible, data were meta-analyzed.

RESULTS

Of the 6207 records identified, we included 31 sub-trials or arms of nine umbrella trials (N = 1637). The pooled overall ORR was 17.7% (95% confidence interval [CI] 9.5-25.9). The ORR for targeted therapies in the experimental arms was significantly lower than the ORR for a combination of targeted therapy drugs with chemotherapy: 13.3% vs 39.0%; p = 0.005. The median PFS was 2.4 months (95% CI 1.9-2.9), and the median OS was 7.1 months (95% CI 6.1-8.4). The overall drug-related death rate (drug-related grade 5 AEs rate) was 0.8% (95% CI 0.3-1.4), and the average drug-related grade 3/4 AE rate per person was 0.45 (95% CI 0.40-0.50).

CONCLUSIONS

Our findings suggest that, on average, one in five cancer patients in umbrella trials published between 1 January 2006 and 7 October 2019 responded to a given therapy, while one in 125 died due to drug toxicity. Our findings do not support the expectation of increased patient benefit in cancer umbrella trials. Further studies should investigate whether umbrella trial design and the precision oncology approach improve patient outcomes.

Decision rules for identifying combination therapies in open-entry, randomized controlled platform trials

Platform trials have become increasingly popular for drug development programs, attracting interest from statisticians, clinicians and regulatory agencies. Many statistical questions related to designing platform trials-such as the impact of decision rules, sharing of information across cohorts, and allocation ratios on operating characteristics and error rates-remain unanswered. In many platform trials, the definition of error rates is not straightforward as classical error rate concepts are not applicable. For an open-entry, exploratory platform trial design comparing combination therapies to the respective monotherapies and standard-of-care, we define a set of error rates and operating characteristics and then use these to compare a set of design parameters under a range of simulation assumptions. When setting up the simulations, we aimed for realistic trial trajectories, such that for example, a priori we do not know the exact number of treatments that will be included over time in a specific simulation run as this follows a stochastic mechanism. Our results indicate that the method of data sharing, exact specification of decision rules and a priori assumptions regarding the treatment efficacy all strongly contribute to the operating characteristics of the platform trial. Furthermore, different operating characteristics might be of importance to different stakeholders. Together with the potential flexibility and complexity of a platform trial, which also impact the achieved operating characteristics via, for example, the degree of efficiency of data sharing this implies that utmost care needs to be given to evaluation of different assumptions and design parameters at the design stage.

Efficiency of a randomized confirmatory basket trial design constrained to control the family wise error rate by indication

Basket trials pool histologic indications sharing molecular pathophysiology, improving development efficiency. Currently, basket trials have been confirmatory only for exceptional therapies. Our previous randomized basket design may be generally suitable in the resource-intensive confirmatory phase, maintains high power even with modest effect sizes, and provides nearly k-fold increased efficiency for k indications, but controls false positives for the pooled result only. Since family wise error rate by indications may sometimes be required, we now simulate a variant of this basket design controlling family wise error rate at 0.025 k, the total family wise error rate of k separate randomized trials. We simulated this modified design under numerous scenarios varying design parameters. Only designs controlling family wise error rate and minimizing estimation bias were allowable. Optimal performance results when [Formula: see text]. We report efficiency (expected # true positives/expected sample size) relative to k parallel studies, at 90% power (“uncorrected”) or at the power achieved in the basket trial (“corrected,” because conventional designs could also increase efficiency by sacrificing power). Efficiency and power (percentage active indications identified) improve with a higher percentage of initial indications active. Up to 92% uncorrected and 38% corrected efficiency improvement is possible. Even under family wise error rate control, randomized confirmatory basket trials substantially improve development efficiency. Initial indication selection is critical.

Uptake of the multi-arm multi-stage (MAMS) adaptive platform approach: a trial-registry review of late-phase randomised clinical trials

BACKGROUND

For medical conditions with numerous interventions worthy of investigation, there are many advantages of a multi-arm multi-stage (MAMS) platform trial approach. However, there is currently limited knowledge on uptake of the MAMS design, especially in the late-phase setting. We sought to examine uptake and characteristics of late-phase MAMS platform trials, to enable better planning for teams considering future use of this approach.

DESIGN

We examined uptake of registered, late-phase MAMS platforms in the EU clinical trials register, Australian New Zealand Clinical Trials Registry, International Standard Randomised Controlled Trial Number registry, Pan African Clinical Trials Registry, WHO International Clinical Trial Registry Platform and databases: PubMed, Medline, Cochrane Library, Global Health Library and EMBASE. Searching was performed and review data frozen on 1 April 2021. MAMS platforms were defined as requiring two or more comparison arms, with two or more trial stages, with an interim analysis allowing for stopping of recruitment to arms and typically the ability to add new intervention arms.

RESULTS

62 late-phase clinical trials using an MAMS approach were included. Overall, the number of late-phase trials using the MAMS design has been increasing since 2001 and been accelerated by COVID-19. The majority of current MAMS platforms were either targeting infectious diseases (52%) or cancers (29%) and all identified trials were for treatment interventions. 89% (55/62) of MAMS platforms were evaluating medications, with 45% (28/62) of the MAMS platforms having at least one or more repurposed medication as a comparison arm.

CONCLUSIONS

Historically, late-phase trials have adhered to long-established standard (two-arm) designs. However, the number of late-phase MAMS platform trials is increasing, across a range of different disease areas. This study highlights the potential scope of MAMS platform trials and may assist research teams considering use of this approach in the late-phase randomised clinical trial setting.

PROSPERO REGISTRATION NUMBER

CRD42019153910.

Challenges for Clinical Drug Development in Pulmonary Fibrosis

Pulmonary fibrosis is a pathologic process associated with scarring of the lung interstitium. Interstitial lung diseases (ILDs) encompass a large and heterogenous group of disorders, a number of which are characterized by progressive pulmonary fibrosis that leads to respiratory failure and death. Idiopathic pulmonary fibrosis (IPF) has been described as an archetype of progressive fibrosing ILD, and the development of pirfenidone and nintedanib has been a major breakthrough in the treatment of patients with this deadly disease. Both drugs principally target scar-forming fibroblasts and have been shown to significantly slow down the accelerated decline of lung function by approximately 50%. In addition, nintedanib has been approved for patients with other progressive fibrosing ILDs and systemic sclerosis-associated ILD. However, there is still no cure for pulmonary fibrosis and no meaningful improvement of symptoms or quality of life has been shown. Advancement in research, such as the advent of single cell sequencing technology, has identified additional pathologic cell populations beyond the fibroblast which could be targeted for therapeutic purposes. The preclinical and clinical development of novel drug candidates is hampered by profound challenges such as a lack of sensitive clinical outcomes or suitable biomarkers that would provide an early indication of patient benefit. With the availability of these anti-fibrotic treatments, it has become even more difficult to demonstrate added efficacy, in particular in short-term clinical studies. Patient heterogeneity and the paucity of biomarkers of disease activity further complicate clinical development. It is conceivable that future treatment of pulmonary fibrosis will need to embrace more precision in treating the right patient at the right time, explore novel measures of efficacy, and likely combine treatment options.

Multiplicity adjustments in parallel-group multi-arm trials sharing a control group: Clear guidance is needed

Multi-arm, parallel-group clinical trials are an efficient way of testing several new treatments, treatment regimens or doses. However, guidance on the requirement for statistical adjustment to control for multiple comparisons (type I error) using a shared control group is unclear. We argue, based on current evidence, that adjustment is not always necessary in such situations. We propose that adjustment should not be a requirement in multi-arm, parallel-group trials testing distinct treatments and sharing a control group, and we call for clearer guidance from stakeholders, such as regulators and scientific journals, on the appropriate settings for adjustment of multiplicity.

Basket clinical trial design for targeted therapies for cancer: a French National Authority for Health statement for health technology assessment

During the past decade, health technology assessment bodies have faced new challenges in establishing the benefits of new drugs for individuals and health-care systems. A topic of increasing importance to the field of oncology is the so-called agnostic regulatory approval of targeted therapies for cancer (independent of tumour location and histology) granted on the basis of basket trials. Basket trials in oncology offer the advantage of simultaneously evaluating treatments for multiple tumours, even rare cancers, in a single clinical trial. To address the novel challenges introduced by these trials, an interdisciplinary panel was convened on behalf of the Transparency Committee of the French National Authority for Health to clarify an approach designed to guarantee a transparent, reproducible, and fair assessment of histology-agnostic treatments for reimbursement by the French National Health Insurance Fund. The requirements of this approach include the need for randomisation, clinically relevant endpoints, appropriate correction for multiple significance testing, characterisation of subgroup heterogeneity, and validation of underlying biomarker assays. A prospectively designated external control is encouraged when the implementation of a direct comparison is deemed infeasible. We also underline the importance of recording outcomes from basket trials in a registry for use as future external controls.

Statistical considerations of phase 3 umbrella trials allowing adding one treatment arm mid-trial

Master protocols, in particular umbrella trials and platform trials, when evaluating multiple experimental treatments with a common control, could save patient resource, increase trial efficiency, and reduce drug development cost. Compared to the phase 3 platform trials that allow unlimited number of experimental arms to be added, it is more practical for individual companies to evaluate two experimental arms with a common control in an umbrella trial and allow the second experimental arm to be added at a later time. There have been limited research done in this type of trials in terms of statistical properties and guidance. In this article, we present statistical considerations of a phase 3 three-arm umbrella design including Type I error control and power, as well as the optimal allocation ratio. We intend to not only complement the existing literature, but more importantly to provide practical guidance to pave the way for its implementation by individual companies.

Practical Considerations and Recommendations for Master Protocol Framework: Basket, Umbrella and Platform Trials

Master protocol, categorized as basket trial, umbrella trial or platform trial, is an innovative clinical trial framework that aims to expedite clinical drug development, enhance trial efficiency, and eventually bring medicines to patients faster. Despite a clear uptake on the advantages in the concepts and designs, master protocols are still yet to be widely used. Part of that may be due to the fact that the master protocol framework comes with the need for new statistical designs and considerations for analyses and operational challenges. In this article, we provide an overview of the master protocol framework, unify the definitions with some examples, review the statistical methods for the designs and analyses, and focus our discussions on some practical considerations and recommendations of master protocols to help practitioners better design and implement such studies.

Current trends in the clinical trial landscape for amyotrophic lateral sclerosis

PURPOSE OF REVIEW

To review the current developments in the design and conduct of clinical trials for amyotrophic lateral sclerosis (ALS), illustrated by a critical appraisal of ClinicalTrials.gov.

RECENT FINDINGS

In total, 63 clinical trials were included in the analysis, of which 13 phase 1, 35 phase 2 and 15 phase 3. Virtually all phase 3 clinical trials can be classified as randomized, placebo controlled, whereas this is only true for 57% of the phase 2 clinical trials. There are promising developments in the routes of drug administration, eligibility criteria, efficacy endpoints and overall trial design. Some of these innovative approaches may, however, not fulfil clinical trial guidelines or regulatory requirements. This could delay the development of effective therapy or hamper our ability to determine whether a treatment is truly (in)effective. The initiation of trial consortia comprising patient organizations, academia, industry and funding bodies may significantly strengthen the future clinical trial landscape for ALS.

SUMMARY

The ALS clinical trial landscape is currently highly active with several promising innovative developments and therapeutic options. By further refinement of evidence-based guidelines, and alignment of our current endeavours, we may soon be able to positively impact the lives of people living with ALS.

Categories, components, and techniques in a modular construction of basket trials for application and further research

Basket trials have become a virulent topic in medical and statistical research during the last decade. The core idea of them is to treat patients, who express the same genetic predisposition-either personally or their disease-with the same treatment irrespective of the location of the disease. The location of the disease defines each basket and the pathway of the treatment uses the common genetic predisposition among the baskets. This opens the opportunity to share information among baskets, which can consequently increase the information of the basket-wise response with respect to the investigated treatment. This further allows dynamic decisions regarding futility and efficacy of individual baskets during the ongoing trial. Several statistical designs have been proposed on how a basket trial can be conducted and this has left an unclear situation with many options. The different designs propose different mathematical and statistical techniques, different decision rules, and also different trial purposes. This paper presents a broad overview of existing designs, categorizes them, and elaborates their similarities and differences. A uniform and consistent notation facilitates the first contact, introduction, and understanding of the statistical methodologies and techniques used in basket trials. Finally, this paper presents a modular approach for the construction of basket trials in applied medical science and forms a base for further research of basket trial designs and their techniques.

The use of external controls: To what extent can it currently be recommended?

With more and better clinical data being captured outside of clinical studies and greater data sharing of clinical studies, external controls may become a more attractive alternative to randomized clinical trials (RCTs). Both industry and regulators recognize that in situations where a randomized study cannot be performed, external controls can provide the needed contextualization to allow a better interpretation of studies without a randomized control. It is also agreed that external controls will not fully replace RCTs as the gold standard for formal proof of efficacy in drug development and the yardstick of clinical research. However, it remains unclear in which situations conclusions about efficacy and a positive benefit/risk can reliably be based on the use of an external control. This paper will provide an overview on types of external control, their applications and the different sources of bias their use may incur, and discuss potential mitigation steps. It will also give recommendations on how the use of external controls can be justified.

Opportunity for efficiency in clinical development: An overview of adaptive clinical trial designs and innovative machine learning tools, with examples from the cardiovascular field

Modern data analysis tools and statistical modeling techniques are increasingly used in clinical research to improve diagnosis, estimate disease progression and predict treatment outcomes. What seems less emphasized is the importance of the study design, which can have a serious impact on the study cost, time and statistical efficiency. This paper provides an overview of different types of adaptive designs in clinical trials and their applications to cardiovascular trials. We highlight recent proliferation of work on adaptive designs over the past two decades, including some recent regulatory guidelines on complex trial designs and master protocols. We also describe the increasing role of machine learning and use of metaheuristics to construct increasingly complex adaptive designs or to identify interesting features for improved predictions and classifications.

Collaborative Platform Trials to Fight COVID-19: Methodological and Regulatory Considerations for a Better Societal Outcome

For the development of coronavirus disease 2019 (COVID‐19) drugs during the ongoing pandemic, speed is of essence whereas quality of evidence is of paramount importance. Although thousands of COVID‐19 trials were rapidly started, many are unlikely to provide robust statistical evidence and meet regulatory standards (e.g., because of lack of randomization or insufficient power). This has led to an inefficient use of time and resources. With more coordination, the sheer number of patients in these trials might have generated convincing data for several investigational treatments. Collaborative platform trials, comparing several drugs to a shared control arm, are an attractive solution. Those trials can utilize a variety of adaptive design features in order to accelerate the finding of life‐saving treatments. In this paper, we discuss several possible designs, illustrate them via simulations, and also discuss challenges, such as the heterogeneity of the target population, time‐varying standard of care, and the potentially high number of false hypothesis rejections in phase II and phase III trials. We provide corresponding regulatory perspectives on approval and reimbursement, and note that the optimal design of a platform trial will differ with our societal objective and by stakeholder. Hasty approvals may delay the development of better alternatives, whereas searching relentlessly for the single most efficacious treatment may indirectly diminish the number of lives saved as time is lost. We point out the need for incentivizing developers to participate in collaborative evidence‐generation initiatives when a positive return on investment is not met.

Systematic review of available software for multi-arm multi-stage and platform clinical trial design

BACKGROUND

In recent years, the popularity of multi-arm multi-stage, seamless adaptive, and platform trials has increased. However, many design-related questions and questions regarding which operating characteristics should be evaluated to determine the potential performance of a specific trial design remain and are often further complicated by the complexity of such trial designs.

METHODS

A systematic search was conducted to review existing software for the design of platform trials, whereby multi-arm multi-stage trials were also included. The results of this search are reported both on the literature level and the software level, highlighting the software judged to be particularly useful.

RESULTS

In recent years, many highly specialized software packages targeting single design elements on platform studies have been released. Only a few of the developed software packages provide extensive design flexibility, at the cost of limited access due to being commercial or not being usable as out-of-the-box solutions.

CONCLUSIONS

We believe that both an open-source modular software similar to OCTOPUS and a collaborative effort will be necessary to create software that takes advantage of and investigates the impact of all the flexibility that platform trials potentially provide.

Paediatric pharmacotherapy and drug regulation: Moving past the therapeutic orphan

The development of specific drug therapy for children was a paradigm-changing event that transformed paediatric medical practice. However, a series of tragedies involving drug treatment for children resulted in a gap developing between drug regulation and practice, with the majority of drugs used in child healthcare being used off-label, rendering children therapeutic orphans. Over the past two decades changes in drug regulation led by the US Food and Drug Administration and followed by the European Union's European Medicines Agency have led to substantial changes in how new drugs with potential use in children are studied and labelled. While these changes have substantially improved labelling for new drugs, there has been much less progress with older drugs. Although the unique challenges of conducting clinical research in children have been addressed by novel clinical trial designs, many of these innovations have not been translated into approaches accepted for the drug approval process. The regulations applying to the need for paediatric studies currently are only applicable in the United States and the European Union, and there is less impetus for paediatric labelling in other jurisdictions. This impacts on a number of issues beyond labelling, including the availability of child-friendly formulations. Finally, the impact of Brexit on paediatric drug studies in the UK remains unclear and is subject to ongoing negotiations between the UK government and the European Union.

Implementing Historical Controls in Oncology Trials

Drug development in oncology has broadened from mainly considering randomized clinical trials to also including single-arm trials tailored for very specific subtypes of cancer. They often use historical controls, and this article discusses benefits and risks of this paradigm and provide various regulatory and statistical considerations. While leveraging the information brought by historical controls could potentially shorten development time and reduce the number of patients enrolled, a careful selection of the past studies, a prespecified statistical analysis accounting for the heterogeneity between studies, and early engagement with regulators will be key to success. Although both the European Medicines Agency and the U.S. Food and Drug Administration have already approved medicines based on nonrandomized experiments, the evidentiary package can be perceived as less comprehensive than randomized experiments. Use of historical controls, therefore, is better suited for cases of high unmet clinical need, where the disease course is well characterized and the primary endpoint is objective. IMPLICATIONS FOR PRACTICE: Incorporating historical data in single-arm oncology trials has the potential to accelerate drug development and to reduce the number of patients enrolled, compared with standard randomized controlled clinical trials. Given the lack of blinding and randomization, such an approach is better suited for cases of high unmet clinical need and/or difficult experimental situations, in which the trajectory of the disease is well characterized and the endpoint can be measured objectively. Careful pre-specification and selection of the historical data, matching of the patient characteristics with the concurrent trial data, and innovative statistical methodologies accounting for between-study variation will be needed. Early engagement with regulators (e.g., via Scientific Advice) is highly recommended.