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.

Master protocols and other innovative trial designs in inflammation and immunology to expedite clinical drug development.

Master protocol designs, such as umbrella and basket studies, allow multiple compounds or multiple target populations to be evaluated simultaneously within a single protocol, and have been widely adopted in oncology clinical trials. These novel designs can also be applied in other therapeutic areas, where they could have several benefits over conducting traditional randomized controlled trials. Here, we detail Pfizer's recent implementations of master protocol designs in inflammation and immunology clinical studies, focusing on the opportunities for cost and resource savings and how these designs can expedite the time required to bring new treatments to patients in need.

The effect of a multi-target protocol on cetacean detection and abundance estimation in aerial surveys.

A double-platform protocol was implemented in the Bay of Biscay and English Channel during the SCANS-III survey (2016). Two observation platforms using different protocols were operating on board a single aircraft: the reference platform (Scans), targeting cetaceans, and the 'Megafauna' platform, recording all the marine fauna visible at the sea surface (jellyfish to seabirds). We tested for a potential bias in small cetacean detection and density estimation when recording all marine fauna. At a small temporal scale (30 s, roughly 1.5 km), our results provided overall similar perception probabilities for both platforms. Small cetacean perception was higher following the detection of another cetacean within the previous 30 s in both platforms. The only prior target that decreased small cetacean perception during the subsequent 30 s was seabirds, in the Megafauna platform. However, at a larger scale (study area), this small-scale perception bias had no effect on the density estimates, which were similar for the two protocols. As a result, there was no evidence of lower performance regarding small cetacean population monitoring for the multi-target protocol in our study area. Because our study area was characterized by moderate cetacean densities and small spatial overlap of cetaceans and seabirds, any extrapolation to other areas or time requires caution. Nonetheless, by permitting the collection of cost-effective quantitative data for marine fauna, anthropogenic activities and marine litter at the sea surface, the multi-target protocol is valuable for optimizing logistical and financial resources to efficiently monitor biodiversity and study community ecology.

Changing platforms without stopping the train: experiences of data management and data management systems when adapting platform protocols by adding and closing comparisons.

BACKGROUND: There is limited research and literature on the data management challenges encountered in multi-arm, multi-stage platform and umbrella protocols. These trial designs allow both (1) seamless addition of new research comparisons and (2) early stopping of accrual to individual comparisons that do not show sufficient activity. FOCUS4 (colorectal cancer) and STAMPEDE (prostate cancer), run from the Medical Research Council Clinical Trials Unit (CTU) at UCL, are two leading UK examples of clinical trials implementing adaptive platform protocol designs. To date, STAMPEDE has added five new research comparisons, closed two research comparisons following pre-planned interim analysis (lack of benefit), adapted the control arm following results from STAMPEDE and other relevant trials, and completed recruitment to six research comparisons. FOCUS4 has closed one research comparison following pre-planned interim analysis (lack of benefit) and added one new research comparison, with a number of further comparisons in the pipeline. We share our experiences from the operational aspects of running these adaptive trials, focusing on data management. METHODS: We held discussion groups with STAMPEDE and FOCUS4 CTU data management staff to identify data management challenges specific to adaptive platform protocols. We collated data on a number of case report form (CRF) changes, database amendments and database growth since each trial began. DISCUSSION: We found similar adaptive protocol-specific challenges in both trials. Adding comparisons to and removing them from open trials provides extra layers of complexity to CRF and database development. At the start of an adaptive trial, CRFs and databases must be designed to be flexible and scalable in order to cope with the continuous changes, ensuring future data requirements are considered where possible. When adding or stopping a comparison, the challenge is to incorporate new data requirements while ensuring data collection within ongoing comparisons is unaffected. Some changes may apply to all comparisons; others may be comparison-specific or applicable only to patients recruited during a specific time period. We discuss the advantages and disadvantages of the different approaches to CRF and database design we implemented in these trials, particularly in relation to use and maintenance of generic versus comparison-specific CRFs and databases. The work required to add or remove a comparison, including the development and testing of changes, updating of documentation, and training of sites, must be undertaken alongside data management of ongoing comparisons. Adequate resource is required for these competing data management tasks, especially in trials with long follow-up. A plan is needed for regular and pre-analysis data cleaning for multiple comparisons that could recruit at different rates and periods of time. Data-cleaning activities may need to be split and prioritised, especially if analyses for different comparisons overlap in time. CONCLUSIONS: Adaptive trials offer an efficient model to run randomised controlled trials, but setting up and conducting the data management activities in these trials can be operationally challenging. Trialists and funders must plan for scalability in data collection and the resource required to cope with additional competing data management tasks.