Efficient Adaptive Designs for Clinical Trials of Interventions for COVID-19

A multi-arm multi-stage platform design that allows preplanned addition of arms while still controlling the family-wise error

There is growing interest in platform trials that allow for adding of new treatment arms as the trial progresses as well as being able to stop treatments part way through the trial for either lack of benefit/futility or for superiority. In some situations, platform trials need to guarantee that error rates are controlled. This paper presents a multi-stage design, that allows additional arms to be added in a platform trial in a preplanned fashion, while still controlling the family-wise error rate, under the assumption of known number and timing of treatments to be added, and no time trends. A method is given to compute the sample size required to achieve a desired level of power and we show how the distribution of the sample size and the expected sample size can be found. We focus on power under the least favorable configuration which is the power of finding the treatment with a clinically relevant effect out of a set of treatments while the rest have an uninteresting treatment effect. A motivating trial is presented which focuses on two settings, with the first being a set number of stages per active treatment arm and the second being a set total number of stages, with treatments that are added later getting fewer stages. Compared to Bonferroni, the savings in the total maximum sample size are modest in a trial with three arms, <1% of the total sample size. However, the savings are more substantial in trials with more arms.

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.

Bayesian sequential monitoring strategies for trials of digestive cancer therapeutics

BACKGROUND

New therapeutics in oncology have presented challenges to existing paradigms and trial designs in all phases of drug development. As a motivating example, we considered an ongoing phase II trial planned to evaluate the combination of a MET inhibitor and an anti-PD-L1 immunotherapy to treat advanced oesogastric carcinoma. The objective of the paper was to exemplify the planning of an adaptive phase II trial with novel anti-cancer agents, including prolonged observation windows and joint sequential evaluation of efficacy and toxicity.

METHODS

We considered various candidate designs and computed decision rules assuming correlations between efficacy and toxicity. Simulations were conducted to evaluate the operating characteristics of all designs.

RESULTS

Design approaches allowing continuous accrual, such as the time-to-event Bayesian Optimal Phase II design (TOP), showed good operating characteristics while ensuring a reduced trial duration. All designs were sensitive to the specification of the correlation between efficacy and toxicity during planning, but TOP can take that correlation into account more easily.

CONCLUSIONS

While specifying design working hypotheses requires caution, Bayesian approaches such as the TOP design had desirable operating characteristics and allowed incorporating concomittant information, such as toxicity data from concomitant observations in another relevant patient population (e.g., defined by mutational status).

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.

A brief review on the lessons learned from COVID-19 on drug discovery and research

COVID-19 pandemic has taught many lessons regarding drug discovery and development. This review covers these aspects of drug discovery and research for COVID-19 which might be used as a tool for future. It summarizes the positives such as progresses in antiviral drug discovery, drug repurposing, adaptations of clinical trial and its regulations, as well as the negative points such as the need to develop more collaboration among stakeholders and future directions. It also discusses the benefits and limitations of finding new indications for existing drugs, and the lessons learned regarding rigorous and robust clinical trials, pharmacokinetic/pharmacodynamic modelling, as well as combination therapy. The pandemic has also revealed some gaps regarding global collaboration and coordination, data sharing and transparency and equitable distribution. Finally, the review enumerates the future directions and implications of drug discovery and research for COVID-19 and other infectious diseases such as preparedness and resilience, interdisciplinary and integrative approaches, diversity and inclusion, and personalized and precision medicine.

COVID-19 therapeutics

SUMMARYSince the emergence of COVID-19 in 2020, an unprecedented range of therapeutic options has been studied and deployed. Healthcare providers have multiple treatment approaches to choose from, but efficacy of those approaches often remains controversial or compromised by viral evolution. Uncertainties still persist regarding the best therapies for high-risk patients, and the drug pipeline is suffering fatigue and shortage of funding. In this article, we review the antiviral activity, mechanism of action, pharmacokinetics, and safety of COVID-19 antiviral therapies. Additionally, we summarize the evidence from randomized controlled trials on efficacy and safety of the various COVID-19 antivirals and discuss unmet needs which should be addressed.

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.

Adaptive clinical trials in surgery: A scoping review of methodological and reporting quality

IMPORTANCE

Adaptive surgical trials are scarce, but adopting these methods may help elevate the quality of surgical research when large-scale RCTs are impractical.

OBJECTIVE

Randomized-controlled trials (RCTs) are the gold standard for evidence-based healthcare. Despite an increase in the number of RCTs, the number of surgical trials remains unchanged. Adaptive clinical trials can streamline trial design and time to trial reporting. The advantages identified for ACTs may help to improve the quality of future surgical trials. We present a scoping review of the methodological and reporting quality of adaptive surgical trials.

EVIDENCE REVIEW

We performed a search of Ovid, Web of Science, and Cochrane Collaboration for all adaptive surgical RCTs performed from database inception to October 12, 2023. We included any published trials that had at least one surgical arm. All review and abstraction were performed in duplicate. Risk of bias (RoB) was assessed using the RoB 2.0 instrument and reporting quality was evaluated using CONSORT ACE 2020. All results were analyzed using descriptive methods.

FINDINGS

Of the 1338 studies identified, six trials met inclusion criteria. Trials were performed in cardiothoracic, oral, orthopedic, and urological surgery. The most common type of adaptive trial was group sequential design with pre-specified interim analyses planned for efficacy, futility, and/or sample size re-estimation. Two trials did use statistical simulations. Our risk of bias evaluation identified a high risk of bias in 50% of included trials. Reporting quality was heterogeneous regarding trial design and outcome assessment and details in relation to randomization and blinding concealment.

CONCLUSION AND RELEVANCE

Surgical trialists should consider implementing adaptive components to help improve patient recruitment and reduce trial duration. Reporting of future adaptive trials must adhere to existing CONSORT ACE 2020 guidelines. Future research is needed to optimize standardization of adaptive methods across medicine and surgery.

Changing interim monitoring in response to internal clinical trial data

Designing clinical trials for emerging infectious diseases such as COVID-19 is challenging because information needed for proper planning may be lacking. Pre-specified adaptive designs can be attractive options, but what happens if a trial with no such design needs to be modified? For example, unexpectedly high efficacy (approximately 95%) in two COVID-19 vaccine trials might cause investigators in other COVID-19 vaccine trials to increase the number of interim analyses to allow earlier stopping for efficacy. If such a decision is based solely on external data, there are no issues, but what if internal trial data by arm are also examined? Fortunately, the conditional error principle of Müller and Schäfer (2004) can be used to ensure no inflation of the type 1 error rate, even if no interim analyses were planned. We study the properties, including limitations, of this method. We provide a shiny app to evaluate changes in timing of interim analyses in response to outcome data by arm in clinical trials.

The readiness of the Asian research ethics committees in responding to the COVID-19 pandemic: A multi-country survey

Background

COVID-19 is a highly challenging infectious disease. Research ethics committees (RECs) have challenges reviewing research on this new pandemic disease under a tight timeline and public pressure. This study aimed to assess RECs' responses and review during the outbreak in seven Asian countries where the Strategic Initiative for Developing Capacity in Ethical Review (SIDCER) networks are active.

Methods

The online survey was conducted in seven Asian countries from April to August 2021. Two sets of online questionnaires were developed, one set for the chairs/secretaries and another set for the REC members.The REC profiles obtained from the REC members are descriptive in nature. Data from the chairs/secretaries were compared between the RECs with external quality assessment (SIDCER-Recognized RECs, SR-RECs) and non-external quality assessment (Non-SIDCER-Recognized RECs, NSR-RECs) and analyzed using a Chi-squared test.

Results

A total of 688 REC members and 197 REC chairs/secretaries participated in the survey. Most RECs have standard operating procedures (SOPs), and have experience in reviewing all types of protocols, but 18.1% had no experience reviewing COVID-19 protocols. Most REC members need specific training on reviewing COVID-19 protocols (93%). In response to the outbreak, RECs used online reviews, increased meeting frequency and single/central REC. All SR-RECs had a member composition as required by the World Health Organisation ethics guidelines, while some NSR-RECs lacked non-affiliated and/or layperson members. SR-RECs reviewed more COVID-related product development protocols and indicated challenges in reviewing risk/benefit and vulnerability (0.010), informed consent form (0.002), and privacy and confidentiality (P = 0.020) than NSR-RECs.

Conclusions

Surveyed RECs had a general knowledge of REC operation and played a significant role in reviewing COVID-19-related product development protocols. Having active networks of RECs across regions to share updated information and resources could be one of the strategies to promote readiness for future public health emergencies.

Towards achieving transnational research partnership equity: lessons from implementing adaptive platform trials in low- and middle-income countries

Background

Use of adaptive clinical trials, particularly adaptive platform trials, has grown exponentially in response to the coronavirus disease (COVID-19) pandemic. Implementation of these trials in low- and middle-income countries (LMICs) has been fostered through the formation or modification of transnational research partnerships, typically between research groups from LMICs and high-income countries (HICs). While these partnerships are important to promote collaboration and overcome the structural and economic disadvantages faced by LMIC health researchers, it is critical to focus attention on the multiple dimensions of partnership equity.

Methods

Based on informal literature reviews and a meeting with leaders of one of the multinational COVID-19 adaptive platform trials, we describe some important considerations about research partnership equity in this context.

Results

We organize these considerations into eight thematic categories: 1) epistemic structures, 2) funding, 3) ethics oversight, 4) regulatory oversight, 5) leadership, 6) post-trial access to interventions, data, and specimens, 7) knowledge translation and dissemination, and 8) research capacity strengthening and maintenance. Within each category we review normative claims that support its relevance to research partnership equity followed by discussion of how adaptive platform trials highlight new dimensions, considerations, or challenges.

Conclusion

In aggregate, these observations provide insight into procedural and substantive equity-building measures within transnational global health research partnerships more broadly.

Electronic health records (EHRs) in clinical research and platform trials: Application of the innovative EHR-based methods developed by EU-PEARL

OBJECTIVE

Electronic Health Record (EHR) systems are digital platforms in clinical practice used to collect patients' clinical information related to their health status and represents a useful storage of real-world data. EHRs have a potential role in research studies, in particular, in platform trials. Platform trials are innovative trial designs including multiple trial arms (conducted simultaneously and/or sequentially) on different treatments under a single master protocol. However, the use of EHRs in research comes with important challenges such as incompleteness of records and the need to translate trial eligibility criteria into interoperable queries. In this paper, we aim to review and to describe our proposed innovative methods to tackle some of the most important challenges identified. This work is part of the Innovative Medicines Initiative (IMI) EU Patient-cEntric clinicAl tRial pLatforms (EU-PEARL) project's work package 3 (WP3), whose objective is to deliver tools and guidance for EHR-based protocol feasibility assessment, clinical site selection, and patient pre-screening in platform trials, investing in the building of a data-driven clinical network framework that can execute these complex innovative designs for which feasibility assessments are critically important.

METHODS

ISO standards and relevant references informed a readiness survey, producing 354 criteria with corresponding questions selected and harmonised through a 7-round scoring process (0-1) in stakeholder meetings, with 85% of consensus being the threshold of acceptance for a criterium/question. ATLAS cohort definition and Cohort Diagnostics were mainly used to create the trial feasibility eligibility (I/E) criteria as executable interoperable queries.

RESULTS

The WP3/EU-PEARL group developed a readiness survey (eSurvey) for an efficient selection of clinical sites with suitable EHRs, consisting of yes-or-no questions, and a set-up of interoperable proxy queries using physicians' defined trial criteria. Both actions facilitate recruiting trial participants and alignment between study costs/timelines and data-driven recruitment potential.

CONCLUSION

The eSurvey will help create an archive of clinical sites with mature EHR systems suitable to participate in clinical trials/platform trials, and the interoperable proxy queries of trial eligibility criteria will help identify the number of potential participants. Ultimately, these tools will contribute to the production of EHR-based protocol design.

Adaptive design clinical trials: current status by disease and trial phase in various perspectives

An adaptive design is a clinical trial design that allows for modification of a structured plan in a clinical trial based on data accumulated during pre-planned interim analyses. This flexible approach to clinical trial design improves the success rate of clinical trials while reducing time, cost, and sample size compared to conventional methods. The purpose of this study is to identify the current status of adaptive design and present key considerations for planning an appropriate adaptive design based on specific circumstances. We searched for clinical trials conducted between January 2006 to July 2021 in the Clinical Trials Registry (ClinicalTrials.gov) using keywords specified in the Food and Drug Administration Adaptive Design Clinical Trial Guidelines. In order to analyze the adaptive designs used in selected cases, we classified the results according to the phase of the clinical trial, type of indication, and the specific adaptation method employed. A total of 267 clinical trials were identified on ClinicalTrials.gov. Among them, 236 clinical trials actually applied adaptive designs and were classified according to phase, indication types, and adaptation methods. Adaptive designs were most frequently used in phase 2 clinical trials and oncology research. The most commonly used adaptation method was the adaptive treatment selection design. In the case of coronavirus disease 2019, the most frequently used designs were adaptive platform design and seamless design. Through this study, we expect to provide valuable insights and considerations for the implementation of adaptive design clinical trials in different diseases and stages.

The pandemic's unseen wounds: COVID-19's profound effects on mental health

Objective

This review aims to explore the impact of the COVID-19 pandemic on mental health, with a focus on the physiological and psychological consequences, including comorbidities. The goal is to understand the direct and indirect populations affected by mental distress and identify potential interventions.

Methodology

A comprehensive literature search was conducted using various databases, including Google Scholar, ResearchGate, ScienceDirect, PubMed, PLoS One, and Web of Science. The search utilized relevant keywords to investigate the direct and indirect impacts of COVID-19 on mental health. The selected articles were critically evaluated and analyzed to identify key findings and insights.

Main findings

Mental health, being an intrinsic component of overall well-being, plays a vital role in physiological functioning. The COVID-19 pandemic, caused by the emergence of the novel SARS-CoV-2 virus, has had a devastating global impact. Beyond the respiratory symptoms, individuals recovering from COVID-19 commonly experience additional ailments, such as arrhythmia, depression, anxiety, and fatigue. Healthcare professionals on the frontlines face an elevated risk of mental illness. However, it is crucial to recognize that the general population also grapples with comparable levels of mental distress.

Conclusion

The COVID-19 pandemic has underscored the significance of addressing mental health concerns. Various strategies can help mitigate the impact, including counselling, fostering open lines of communication, providing mental support, ensuring comprehensive patient care, and administering appropriate medications. In severe cases, treatment may involve the supplementation of essential vitamins and antidepressant therapy. By understanding the direct and indirect impacts of COVID-19 on mental health, healthcare providers and policymakers can develop targeted interventions to support individuals and communities affected by the pandemic. Continued research and collaborative efforts are essential to address this pervasive issue effectively.

Treating intrusive memories after trauma in healthcare workers: a Bayesian adaptive randomised trial developing an imagery-competing task intervention

Intensive care unit (ICU) staff continue to face recurrent work-related traumatic events throughout the COVID-19 pandemic. Intrusive memories (IMs) of such traumatic events comprise sensory image-based memories. Harnessing research on preventing IMs with a novel behavioural intervention on the day of trauma, here we take critical next steps in developing this approach as a treatment for ICU staff who are already experiencing IMs days, weeks, or months post-trauma. To address the urgent need to develop novel mental health interventions, we used Bayesian statistical approaches to optimise a brief imagery-competing task intervention to reduce the number of IMs. We evaluated a digitised version of the intervention for remote, scalable delivery. We conducted a two-arm, parallel-group, randomised, adaptive Bayesian optimisation trial. Eligible participants worked clinically in a UK NHS ICU during the pandemic, experienced at least one work-related traumatic event, and at least three IMs in the week prior to recruitment. Participants were randomised to receive immediate or delayed (after 4 weeks) access to the intervention. Primary outcome was the number of IMs of trauma during week 4, controlling for baseline week. Analyses were conducted on an intention-to-treat basis as a between-group comparison. Prior to final analysis, sequential Bayesian analyses were conducted (n = 20, 23, 29, 37, 41, 45) to inform early stopping of the trial prior to the planned maximum recruitment (n = 150). Final analysis (n = 75) showed strong evidence for a positive treatment effect (Bayes factor, BF = 1.25 × 106): the immediate arm reported fewer IMs (median = 1, IQR = 0-3) than the delayed arm (median = 10, IQR = 6-16.5). With further digital enhancements, the intervention (n = 28) also showed a positive treatment effect (BF = 7.31). Sequential Bayesian analyses provided evidence for reducing IMs of work-related trauma for healthcare workers. This methodology also allowed us to rule out negative effects early, reduced the planned maximum sample size, and allowed evaluation of enhancements. Trial Registration NCT04992390 ( www.clinicaltrials.gov ).

Point estimation for adaptive trial designs II: Practical considerations and guidance

In adaptive clinical trials, the conventional end-of-trial point estimate of a treatment effect is prone to bias, that is, a systematic tendency to deviate from its true value. As stated in recent FDA guidance on adaptive designs, it is desirable to report estimates of treatment effects that reduce or remove this bias. However, it may be unclear which of the available estimators are preferable, and their use remains rare in practice. This article is the second in a two-part series that studies the issue of bias in point estimation for adaptive trials. Part I provided a methodological review of approaches to remove or reduce the potential bias in point estimation for adaptive designs. In part II, we discuss how bias can affect standard estimators and assess the negative impact this can have. We review current practice for reporting point estimates and illustrate the computation of different estimators using a real adaptive trial example (including code), which we use as a basis for a simulation study. We show that while on average the values of these estimators can be similar, for a particular trial realization they can give noticeably different values for the estimated treatment effect. Finally, we propose guidelines for researchers around the choice of estimators and the reporting of estimates following an adaptive design. The issue of bias should be considered throughout the whole lifecycle of an adaptive design, with the estimation strategy prespecified in the statistical analysis plan. When available, unbiased or bias-reduced estimates are to be preferred.

Towards achieving transnational research partnership equity: lessons from implementing adaptive platform trials in low- and middle-income countries

Background: Use of adaptive clinical trials, particularly adaptive platform trials, has grown exponentially in response to the coronavirus disease (COVID-19) pandemic. Implementation of these trials in low- and middle-income countries (LMICs) has been fostered through the formation or modification of transnational research partnerships, typically between research groups from LMICs and high-income countries (HICs). While these partnerships are important to promote collaboration and overcome the structural and economic disadvantages faced by LMIC health researchers, it is critical to focus attention on the multiple dimensions of partnership equity. Methods: Based on informal literature reviews and meetings with leaders of one of the multinational COVID-19 adaptive platform trials, we describe what can be learned about research partnership equity from these experiences. Results: We organize these considerations into eight thematic categories: 1) epistemic structures, 2) funding, 3) ethics oversight, 4) regulatory oversight, 5) leadership, 6) post-trial access to interventions, data, and specimens, 7) knowledge translation, and 8) research capacity strengthening and maintenance. Within each category we review the normative claims that support its relevance to research partnership equity followed by discussion of how adaptive platform trials highlight new dimensions, considerations, or challenges. Conclusion: These observations provide insight into procedural and substantive equity-building measures within transnational global health research partnerships more broadly.

Point estimation for adaptive trial designs I: A methodological review

Recent FDA guidance on adaptive clinical trial designs defines bias as "a systematic tendency for the estimate of treatment effect to deviate from its true value," and states that it is desirable to obtain and report estimates of treatment effects that reduce or remove this bias. The conventional end-of-trial point estimates of the treatment effects are prone to bias in many adaptive designs, because they do not take into account the potential and realized trial adaptations. While much of the methodological developments on adaptive designs have tended to focus on control of type I error rates and power considerations, in contrast the question of biased estimation has received relatively less attention. This article is the first in a two-part series that studies the issue of potential bias in point estimation for adaptive trials. Part I provides a comprehensive review of the methods to remove or reduce the potential bias in point estimation of treatment effects for adaptive designs, while part II illustrates how to implement these in practice and proposes a set of guidelines for trial statisticians. The methods reviewed in this article can be broadly classified into unbiased and bias-reduced estimation, and we also provide a classification of estimators by the type of adaptive design. We compare the proposed methods, highlight available software and code, and discuss potential methodological gaps in the literature.

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.

Blood Purification Studies in the ICU: What Endpoints Should We Use?

The potential for treatment of the critically ill using blood purification techniques has been discussed for several decades. However, since the first attempts at applying extracorporeal techniques to patients with sepsis were described, there has been considerable hesitancy towards the widespread adoption of such methods, given the lack of mortality benefit observed and indeed the paucity of randomized controlled studies. However, this is not unique so far as studies on the critically ill are concerned where there is a dearth of studies providing a positive finding to influence clinical practice. Consequently, as well as targeted patient selection, it is perhaps time to consider endpoints other than mortality in studies on the critically ill, particularly in blood purification studies where, to-date, such heterogeneous groups of patients have been studied.

On the role of data, statistics and decisions in a pandemic

A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy and of effective dissemination and communication of findings.

Design aspects of COVID-19 treatment trials: Improving probability and time of favorable events

As a reaction to the pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a multitude of clinical trials for the treatment of SARS-CoV-2 or the resulting corona disease 2019 (COVID-19) are globally at various stages from planning to completion. Although some attempts were made to standardize study designs, this was hindered by the ferocity of the pandemic and the need to set up clinical trials quickly. We take the view that a successful treatment of COVID-19 patients (i) increases the probability of a recovery or improvement within a certain time interval, say 28 days; (ii) aims to expedite favorable events within this time frame; and (iii) does not increase mortality over this time period. On this background, we discuss the choice of endpoint and its analysis. Furthermore, we consider consequences of this choice for other design aspects including sample size and power and provide some guidance on the application of adaptive designs in this particular context.

Practical recommendations for implementing a Bayesian adaptive phase I design during a pandemic

BACKGROUND

Modern designs for dose-finding studies (e.g., model-based designs such as continual reassessment method) have been shown to substantially improve the ability to determine a suitable dose for efficacy testing when compared to traditional designs such as the 3 + 3 design. However, implementing such designs requires time and specialist knowledge.

METHODS

We present a practical approach to developing a model-based design to help support uptake of these methods; in particular, we lay out how to derive the necessary parameters and who should input, and when, to these decisions. Designing a model-based, dose-finding trial is demonstrated using a treatment within the AGILE platform trial, a phase I/II adaptive design for novel COVID-19 treatments.

RESULTS

We present discussion of the practical delivery of AGILE, covering what information was found to support principled decision making by the Safety Review Committee, and what could be contained within a statistical analysis plan. We also discuss additional challenges we encountered in the study and discuss more generally what (unplanned) adaptations may be acceptable (or not) in studies using model-based designs.

CONCLUSIONS

This example demonstrates both how to design and deliver an adaptive dose-finding trial in order to support uptake of these methods.

Quantifying Efficiency Gains of Innovative Designs of Two-Arm Vaccine Trials for COVID-19 Using an Epidemic Simulation Model

Clinical trials of a vaccine during an epidemic face particular challenges, such as the pressure to identify an effective vaccine quickly to control the epidemic, and the effect that time-space-varying infection incidence has on the power of a trial. We illustrate how the operating characteristics of different trial design elements maybe evaluated using a network epidemic and trial simulation model, based on COVID-19 and individually randomized two-arm trials with a binary outcome. We show that "ring" recruitment strategies, prioritizing participants at an imminent risk of infection, can result in substantial improvement in terms of power in the model we present. In addition, we introduce a novel method to make more efficient use of the data from the earliest cases of infection observed in the trial, whose infection may have been too early to be vaccine-preventable. Finally, we compare several methods of response-adaptive randomization (RAR), discussing their advantages and disadvantages in the context of our model and identifying particular adaptation strategies that preserve power and estimation properties, while slightly reducing the number of infections, given an effective vaccine.

Costs and staffing resource requirements for adaptive clinical trials: quantitative and qualitative results from the Costing Adaptive Trials project

BACKGROUND

Adaptive designs offer great promise in improving the efficiency and patient-benefit of clinical trials. An important barrier to further increased use is a lack of understanding about which additional resources are required to conduct a high-quality adaptive clinical trial, compared to a traditional fixed design. The Costing Adaptive Trials (CAT) project investigated which additional resources may be required to support adaptive trials.

METHODS

We conducted a mock costing exercise amongst seven Clinical Trials Units (CTUs) in the UK. Five scenarios were developed, derived from funded clinical trials, where a non-adaptive version and an adaptive version were described. Each scenario represented a different type of adaptive design. CTU staff were asked to provide the costs and staff time they estimated would be needed to support the trial, categorised into specified areas (e.g. statistics, data management, trial management). This was calculated separately for the non-adaptive and adaptive version of the trial, allowing paired comparisons. Interviews with 10 CTU staff who had completed the costing exercise were conducted by qualitative researchers to explore reasons for similarities and differences.

RESULTS

Estimated resources associated with conducting an adaptive trial were always (moderately) higher than for the non-adaptive equivalent. The median increase was between 2 and 4% for all scenarios, except for sample size re-estimation which was 26.5% (as the adaptive design could lead to a lengthened study period). The highest increase was for statistical staff, with lower increases for data management and trial management staff. The percentage increase in resources varied across different CTUs. The interviews identified possible explanations for differences, including (1) experience in adaptive trials, (2) the complexity of the non-adaptive and adaptive design, and (3) the extent of non-trial specific core infrastructure funding the CTU had.

CONCLUSIONS

This work sheds light on additional resources required to adequately support a high-quality adaptive trial. The percentage increase in costs for supporting an adaptive trial was generally modest and should not be a barrier to adaptive designs being cost-effective to use in practice. Informed by the results of this research, guidance for investigators and funders will be developed on appropriately resourcing adaptive trials.

Impact of COVID-19 on oncology practice and research in the United States

The COVID-19 outbreak has affected healthcare services in many ways, such as interruption of regular patient visit to clinical facilities and burdening of the already limited healthcare resources. Prior to the emergence of COVID-19, cancer has been one of the leading causes of deaths in the United States. While the regular care and access disruptions associated with the COVID-19 outbreak have received increasing attention in many areas, the range of gaps in oncology practice and research is not fully understood or well documented. Nonetheless, the global outbreak continues to unevenly affect the wealthiest and best-resourced country in the world and complicates response to other health issues. The aim of this study is to comment on the impact of the COVID-19 pandemic on oncology practice and research in the United States.

Planning for the Next Pandemic: Ethics and Innovation Today for Improved Clinical Trials Tomorrow

The coronavirus pandemic has brought public attention to the steps required to produce valid scientific clinical research in drug development. Traditional ethical principles that guide clinical research remain the guiding compass for physicians, patients, public health officials, investigators, drug developers and the public. Accelerating the process of delivering safe and effective treatments and vaccines against COVID-19 is a moral imperative. The apparent clash between the regulated system of phased randomized clinical trials and urgent public health need requires leveraging innovation with ethical scientific rigor. We reflect on the Belmont principles of autonomy, beneficence and justice as the pandemic unfolds, and illustrate the role of innovative clinical trial designs in alleviating pandemic challenges. Our discussion highlights selected types of innovative trial design and correlates them with ethical parameters and public health benefits. Details are provided for platform trials and other innovative designs such as basket and umbrella trials, designs leveraging external data sources, multi-stage seamless trials, preplanned control arm data sharing between larger trials, and higher order systems of linked trials coordinated more broadly between individual trials and phases of development, recently introduced conceptually as "PIPELINEs."

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.

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.

Clinical trial preparations for the next pandemic

This paper describes the need to prepare for the development of antiviral therapeutics for the next pandemic. Preparation would consist of a stockpiling of best practices for clinical trial design, analysis and operations during the current SARS-CoV-2 pandemic as well as continuous development of treatments and methodology between pandemics. This development would be facilitated by a global clinical trial pandemic reserve similar to the military reserves consisting of medical and quantitative methods professionals who would remain engaged between pandemics. Continuous identification of potential antiviral drugs and diagnostic methods would also be needed. Specific methodology addressed includes the importance of large simple trials, follow up time, efficacy endpoint, appropriate estimands, non-inferiority trials, more sophisticated patient accrual models and procedures for data sharing between clinical trials.

Causal inference methods for small non-randomized studies: Methods and an application in COVID-19

The usual development cycles are too slow for the development of vaccines, diagnostics and treatments in pandemics such as the ongoing SARS-CoV-2 pandemic. Given the pressure in such a situation, there is a risk that findings of early clinical trials are overinterpreted despite their limitations in terms of size and design. Motivated by a non-randomized open-label study investigating the efficacy of hydroxychloroquine in patients with COVID-19, we describe in a unified fashion various alternative approaches to the analysis of non-randomized studies. A widely used tool to reduce the impact of treatment-selection bias are so-called propensity score (PS) methods. Conditioning on the propensity score allows one to replicate the design of a randomized controlled trial, conditional on observed covariates. Extensions include the g-computation approach, which is less frequently applied, in particular in clinical studies. Moreover, doubly robust estimators provide additional advantages. Here, we investigate the properties of propensity score based methods including three variations of doubly robust estimators in small sample settings, typical for early trials, in a simulation study. R code for the simulations is provided.

Clinical Trials Impacted by the COVID-19 Pandemic: Adaptive Designs to the Rescue?

Very recently the new pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified and the coronavirus disease 2019 (COVID-19) declared a pandemic by the World Health Organization. The pandemic has a number of consequences for ongoing clinical trials in non-COVID-19 conditions. Motivated by four current clinical trials in a variety of disease areas we illustrate the challenges faced by the pandemic and sketch out possible solutions including adaptive designs. Guidance is provided on (i) where blinded adaptations can help; (ii) how to achieve Type I error rate control, if required; (iii) how to deal with potential treatment effect heterogeneity; (iv) how to use early read-outs; and (v) how to use Bayesian techniques. In more detail approaches to resizing a trial affected by the pandemic are developed including considerations to stop a trial early, the use of group-sequential designs or sample size adjustment. All methods considered are implemented in a freely available R shiny app. Furthermore, regulatory and operational issues including the role of data monitoring committees are discussed.

Machine learning for clinical trials in the era of COVID-19

The world is in the midst of a pandemic. We still know little about the disease COVID-19 or about the virus (SARS-CoV-2) that causes it. We do not have a vaccine or a treatment (aside from managing symptoms). We do not know if recovery from COVID-19 produces immunity, and if so for how long, hence we do not know if "herd immunity" will eventually reduce the risk or if a successful vaccine can be developed - and this knowledge may be a long time coming. In the meantime, the COVID-19 pandemic is presenting enormous challenges to medical research, and to clinical trials in particular. This paper identifies some of those challenges and suggests ways in which machine learning can help in response to those challenges. We identify three areas of challenge: ongoing clinical trials for non-COVID-19 drugs; clinical trials for repurposing drugs to treat COVID-19, and clinical trials for new drugs to treat COVID-19. Within each of these areas, we identify aspects for which we believe machine learning can provide invaluable assistance.