Experiences of the Data Monitoring Committee for the RECOVERY trial, a large-scale adaptive platform randomised trial of treatments for patients hospitalised with COVID-19.

AIM: To inform the oversight of future clinical trials during a pandemic, we summarise the experiences of the Data Monitoring Committee (DMC) for the Randomised Evaluation of COVID therapy trial (RECOVERY), a large-scale randomised adaptive platform clinical trial of treatments for hospitalised patients with COVID-19. METHODS AND FINDINGS: During the first 24 months of the trial (March 2020 to February 2022), the DMC oversaw accumulating data for 14 treatments in adults (plus 10 in children) involving > 45,000 randomised patients. Five trial aspects key for the DMC in performing its role were: a large committee of members, including some with extensive DMC experience and others who had broad clinical expertise; clear strategic planning, communication, and responsiveness by the trial principal investigators; data collection and analysis systems able to cope with phases of very rapid recruitment and link to electronic health records; an ability to work constructively with regulators (and other DMCs) to address emerging concerns without the need to release unblinded mortality results; and the use of videoconferencing systems that enabled national and international members to meet at short notice and from home during the pandemic when physical meetings were impossible. Challenges included that the first four treatments introduced were effectively 'competing' for patients (increasing pressure to make rapid decisions on each one); balancing the global health imperative to report on findings with the need to maintain confidentiality until the results were sufficiently certain to appropriately inform treatment decisions; and reliably assessing safety, especially for newer agents introduced after the initial wave and in the small numbers of pregnant women and children included. We present a series of case vignettes to illustrate some of the issues and the DMC decision-making related to hydroxychloroquine, dexamethasone, casirivimab + imdevimab, and tocilizumab. CONCLUSIONS: RECOVERY's streamlined adaptive platform design, linked to hospital-level and population-level health data, enabled the rapid and reliable assessment of multiple treatments for hospitalised patients with COVID-19. The later introduction of factorial assessments increased the trial's efficiency, without compromising the DMC's ability to assess safety and efficacy. Requests for the release of unblinded primary outcome data to regulators at points when data were not mature required significant efforts in communication with the regulators by the DMC to avoid inappropriate early trial termination.

Evaluating group-sequential non-inferiority clinical trials following interim stopping: The HIV Prevention Trials Network 083 trial.

BACKGROUND/AIMS: The HIV Prevention Trials Network 083 trial was a group-sequential non-inferiority trial designed to compare HIV incidence under a novel experimental regimen for HIV prevention, long-acting injectable cabotegravir, with an active-control regimen of daily oral tenofovir disoproxil fumarate/emtricitabine (brand name Truvada). In March of 2020, just as the trial had completed enrollment, the COVID-19 pandemic threatened to prevent trial participants from attending study visits and obtaining study medication, motivating the study team to update the interim monitoring plan. The Data and Safety Monitoring Board subsequently stopped the trial at the first interim review due to strong early evidence of efficacy. METHODS: Here we describe some unique aspects of the trial's design, monitoring, analysis, and interpretation. We illustrate the importance of computing point estimates, confidence intervals, and p values based on the sampling distribution induced by sequential monitoring. RESULTS: Accurate analysis, decision-making and interpretation of trial results rely on pre-specification of a stopping boundary, including the scale on which the stopping rule will be implemented, the specific test statistics to be calculated, and how the boundary will be adjusted if the available information fraction at interim review is different from planned. After appropriate adjustment for the sampling distribution and overrun, the HIV Prevention Trials Network 083 trial provided strong evidence that the experimental regimen was superior to the active control. CONCLUSIONS: For the HIV Prevention Trials Network 083 trial, the difference between corrected inferential statistics and naive results was quite small-as will often be the case-nevertheless, it is appropriate to report and publish the most accurate and unbiased statistical results.

The Essential Role of Data and Safety Monitoring Boards (DSMBs) in Ensuring the Ethics of Global Vaccine Trials to Address Coronavirus Disease 2019 (COVID-19O).

Coronavirus disease 2019 (COVID-19) vaccines are being developed and implemented with unprecedented speed. Accordingly, trials considered ethical at their inception may quickly become concerning. We provide recommendations for Data and Safety Monitoring Boards (DSMBs) on monitoring the ethical acceptability of COVID-19 vaccine trials, focusing on placebo-controlled trials in low- and middle-income countries.

Risk-based centralized data monitoring of clinical trials at the time of COVID-19 pandemic.

OBJECTIVES: COVID-19 pandemic caused several alarming challenges for clinical trials. On-site source data verification (SDV) in the multicenter clinical trial became difficult due to travel ban and social distancing. For multicenter clinical trials, centralized data monitoring is an efficient and cost-effective method of data monitoring. Centralized data monitoring reduces the risk of COVID-19 infections and provides additional capabilities compared to on-site monitoring. The key steps for on-site monitoring include identifying key risk factors and thresholds for the risk factors, developing a monitoring plan, following up the risk factors, and providing a management plan to mitigate the risk. METHODS: For analysis purposes, we simulated data similar to our clinical trial data. We classified the data monitoring process into two groups, such as the Supervised analysis process, to follow each patient remotely by creating a dashboard and an Unsupervised analysis process to identify data discrepancy, data error, or data fraud. We conducted several risk-based statistical analysis techniques to avoid on-site source data verification to reduce time and cost, followed up with each patient remotely to maintain social distancing, and created a centralized data monitoring dashboard to ensure patient safety and maintain the data quality. CONCLUSION: Data monitoring in clinical trials is a mandatory process. A risk-based centralized data review process is cost-effective and helpful to ignore on-site data monitoring at the time of the pandemic. We summarized how different statistical methods could be implemented and explained in SAS to identify various data error or fabrication issues in multicenter clinical trials.

Clinical trials in a COVID-19 pandemic: Shared infrastructure for continuous learning in a rapidly changing landscape.

BACKGROUND: Clinical trials, conducted efficiently and with the utmost integrity, are a key component in identifying effective vaccines, therapies, and other interventions urgently needed to solve the COVID-19 crisis. Yet launching and implementing trials with the rigor necessary to produce convincing results is a complicated and time-consuming process. Balancing rigor and efficiency involves relying on designs that employ flexible features to respond to a fast-changing landscape, measuring valid endpoints that result in translational actions and disseminating findings in a timely manner. We describe the challenges involved in creating infrastructure with potential utility for shared learning. METHODS: We have established a shared infrastructure that borrows strength across multiple trials. The infrastructure includes an endpoint registry to aid in selecting appropriate endpoints, a registry to facilitate establishing a Data & Safety Monitoring Board, common data collection instruments, a COVID-19 dedicated design and analysis team, and a pragmatic platform protocol, among other elements. RESULTS: The authors have relied on the shared infrastructure for six clinical trials for which they serve as the Data Coordinating Center and have a design and analysis team comprising 15 members who are dedicated to COVID-19. The authors established a pragmatic platform to simultaneously investigate multiple treatments for the outpatient with adaptive features to add or drop treatment arms. CONCLUSION: The shared infrastructure provides appealing opportunities to evaluate disease in a more robust manner with fewer resources and is especially valued during a pandemic where efficiency in time and resources is crucial. The most important element of the shared infrastructure is the pragmatic platform. While it may be the most challenging of the elements to establish, it may provide the greatest benefit to both patients and researchers.

Moving fast but going slow: coordination challenges for trials of COVID-19 post-exposure prophylaxis.

An unprecedented volume of research has been generated in response to the COVID-19 pandemic. However, there are risks of inefficient duplication and of important work being impeded if efforts are not synchronized. Excessive reliance on observational studies, which can be more rapidly conducted but are inevitably subject to measured and unmeasured confounders, can foil efforts to conduct rigorous randomized trials. These challenges are illustrated by recent global efforts to conduct clinical trials of post-exposure prophylaxis (PEP) as a strategy for preventing COVID-19. Innovative strategies are needed to help overcome these issues, including increasing communication between the Data Safety and Monitoring Committees (DSMCs) of similar trials. It is important to reinforce the primacy of high-quality trials in generating unbiased answers to pressing prevention and treatment questions about COVID-19.

Data monitoring committees for clinical trials evaluating treatments of COVID-19.

The first cases of coronavirus disease 2019 (COVID-19) were reported in December 2019 and the outbreak of SARS-CoV-2 was declared a pandemic in March 2020 by the World Health Organization. This sparked a plethora of investigations into diagnostics and vaccination for SARS-CoV-2, as well as treatments for COVID-19. Since COVID-19 is a severe disease associated with a high mortality, clinical trials in this disease should be monitored by a data monitoring committee (DMC), also known as data safety monitoring board (DSMB). DMCs in this indication face a number of challenges including fast recruitment requiring an unusually high frequency of safety reviews, more frequent use of complex designs and virtually no prior experience with the disease. In this paper, we provide a perspective on the work of DMCs for clinical trials of treatments for COVID-19. More specifically, we discuss organizational aspects of setting up and running DMCs for COVID-19 trials, in particular for trials with more complex designs such as platform trials or adaptive designs. Furthermore, statistical aspects of monitoring clinical trials of treatments for COVID-19 are considered. Some recommendations are made regarding the presentation of the data, stopping rules for safety monitoring and the use of external data. The proposed stopping boundaries are assessed in a simulation study motivated by clinical trials in COVID-19.

It is unprecedented: trial management during the COVID-19 pandemic and beyond.

The COVID-19 pandemic has presented unique challenges for the clinical trial community, both in the rapid establishment of COVID-19 clinical trials and many existing non-COVID-19 studies either being temporarily paused (whether that is a complete pause or pause in some activities) and/or adapting their processes. Trial managers have played a key role in decision-making, undertaking risk assessments and adapting trial processes, working closely with other members of the research team. This article presents some of the ways in which trial management processes have been altered and the key role that trial managers have played. It has been born out of discussions between trial managers in the UK who are members of the UK Trial Managers' Network (UKTMN), a national network of trial management professionals managing non-commercial trials.In these unprecedented times, clinical trials have faced many uncertainties and broad-ranging challenges encompassing a range of activities including prioritising patient safety amidst the pandemic, consenting and recruiting new participants into trials, data collection and management and intervention delivery. In many cases, recruitment has been paused whilst mitigations have been put in place to continue data collection. Innovative solutions have been implemented to ensure we continue, where possible, to deliver high-quality clinical trials. Technology has provided many solutions to these challenges, and trial managers have adapted to new ways of working whilst continuing to deliver their clinical trials. Trial management groups are now faced with new uncertainties around re-starting clinical trials, and it is unclear currently how this will go, though working together with sponsors, funders and site teams is clearly a priority.Clinical trial teams have worked together to ensure their trials have adapted quickly whilst ensuring participant safety is given utmost importance. There are clear examples where the trial community have come together to share experiences and expertise, and this should continue in the future to ensure the innovative practices developed become embedded in the design and conduct of clinical trials in the future.

[Data monitoring committees-state of the art and perspectives for the future]. / Das Data Monitoring Committee ­ State of the Art und Perspektiven für die Zukunft.

BACKGROUND: The classic randomized and controlled clinical trial is facing new challenges with complex study designs and disease interception concepts. For this reason, data monitoring committees (DMCs) can take on a central function if professionally integrated into the methodical procedure of clinical trials. On this basis, the responsible competent authority and the responsible ethics committee have to verify the substantial charter document in the implicit/explicit approval procedure reflecting the working process of the independent committee. OBJECTIVES: The frequencies and conditions under which DMCs are used in clinical trials was investigated. METHODS: The database of the Federal Institute for Drugs and Medical Devices (BfArM) was the basis for statistical analysis concerning the frequency of implementation of data monitoring committees with different criteria over an observation period of more than 15 years. RESULTS: In total, 4152 DMCs have been used in 14,135 clinical trials with drugs. The independent expert committee was mostly integrated by commercial sponsors in phase III of the clinical development. The ethics committees were involved with different absolute frequencies. DISCUSSION: Sponsors demonstrate an increasing willingness to integrate DMCs in the methodical conduct of clinical trials especially in the case of new study designs. DMCs could be an important scientific aid in order to assess the implications of coronavirus SARS-CoV­2 on clinical trials.

Achieving effective informed oversight by DMCs in COVID clinical trials.

Best practices of data monitoring committees (DMCs) in randomized clinical trials are well established. Independent oversight provided by DMCs is particularly important in trials conducted in public health emergencies, such as in HIV/AIDS or coronavirus epidemics. Special considerations are needed to enable DMCs to effectively address novel circumstances they face in such settings. In the COVID-19 pandemic, these include the remarkable speed in which data regarding benefits and risks of interventions are accumulated. DMCs must hold frequent virtual meetings, using state-of-the-art communication software that protects against risk for security breaches. Data capture and DMC reports should be focused on the most informative measures about benefits and risks. Because numerous clinical trials are being concurrently conducted in the COVID-19 setting, often addressing closely related scientific questions, structures for DMC oversight should be efficient and adequately informative. When these concurrently conducted trials are evaluating related regimens in related clinical settings, often individually underpowered for safety and having separate DMCs, processes should be implemented enabling these DMCs to share with each other emerging confidential evidence to better assess risks and benefits. Ideally a single DMC would monitor a portfolio of clinical trials or a trial with multiple arms, such as a platform trial.

Data Monitoring for the Chinese Clinical Trials of Remdesivir in Treating Patients with COVID-19 During the Pandemic Crisis.

Two phase-III, double-blind, randomized clinical trials of remdesivir plus SOC (standard of care) versus placebo plus SOC have been conducted in Wuhan hospitals by Chinese investigators during the urgent COVID-19 epidemic [ClincalTrials.gov NCT04257656 and NCT04252664]. These trials have been highly anticipated worldwide. We expect investigators of the trials will soon report the clinical and laboratory findings from the medical perspective. This manuscript provides documentary style information on the process of monitoring key data and making recommendations to the sponsor and investigators based on analytical insights when dealing with the emergent situation from the statistical viewpoint. Having monitored data sequentially from 237 patients, we comment on the strength and weakness of the study design and suggest the treatment effect of remdesivir on severe COVID-19 cases. Our experience with using the Dynamic Data Monitoring (DDM) tool has demonstrated its efficiency and reliability in supporting DSMB's instantaneous review of essential data during the emergent situation. DDM, when used properly by disciplined statisticians, has shown its capability of exploring the trial data flexibly and, in the meantime, protecting the trial's scientific integrity.