Abstract The Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) Cross-Trial Statistics Group gathered lessons learned from statisticians responsible for the design and analysis of the 11 ACTIV therapeutic master protocols to inform contemporary trial design as well as preparation for a future pandemic. The ACTIV master protocols were designed to rapidly assess what treatments might save lives, keep people out of the hospital, and help them feel better faster. Study teams initially worked without knowledge of the natural history of disease and thus without key information for design decisions. Moreover, the science of platform trial design was in its infancy. Here, we discuss the statistical design choices made and the adaptations forced by the changing pandemic context. Lessons around critical aspects of trial design are summarized, and recommendations are made for the organization of master protocols in the future. 

The statistical design and analysis of pandemic platform trials: Implications for the future

It is necessary to harmonize and standardize data variables used in case report forms (CRFs) of clinical studies to facilitate the merging and sharing of the collected patient data across several clinical studies. This is particularly true for clinical studies that focus on infectious diseases. Public health may be highly dependent on the findings of such studies. Hence, there is an elevated urgency to generate meaningful, reliable insights, ideally based on a high sample number and quality data. The implementation of core data elements and the incorporation of interoperability standards can facilitate the creation of harmonized clinical data sets. 

pdf/creation-of-standardized-common-data-elements-for-diagnostic-53igilokor.pdf

Creation of Standardized Common Data Elements for Diagnostic Tests in Infectious Disease Studies: Semantic and Syntactic Mapping

<sec> <title>BACKGROUND</title> It is necessary to harmonize and standardize data variables used in case report forms (CRFs) of clinical studies to facilitate the merging and sharing of the collected patient data across several clinical studies. This is particularly true for clinical studies that focus on infectious diseases. Public health may be highly dependent on the findings of such studies. Hence, there is an elevated urgency to generate meaningful, reliable insights, ideally based on a high sample number and quality data. The implementation of core data elements and the incorporation of interoperability standards can facilitate the creation of harmonized clinical data sets. </sec> <sec> <title>OBJECTIVE</title> This study’s objective was to compare, harmonize, and standardize variables focused on diagnostic tests used as part of CRFs in 6 international clinical studies of infectious diseases in order to, ultimately, then make available the panstudy common data elements (CDEs) for ongoing and future studies to foster interoperability and comparability of collected data across trials. </sec> <sec> <title>METHODS</title> We reviewed and compared the metadata that comprised the CRFs used for data collection in and across all 6 infectious disease studies under consideration in order to identify CDEs. We examined the availability of international semantic standard codes within the Systemized Nomenclature of Medicine - Clinical Terms, the National Cancer Institute Thesaurus, and the Logical Observation Identifiers Names and Codes system for the unambiguous representation of diagnostic testing information that makes up the CDEs. We then proposed 2 data models that incorporate semantic and syntactic standards for the identified CDEs. </sec> <sec> <title>RESULTS</title> Of 216 variables that were considered in the scope of the analysis, we identified 11 CDEs to describe diagnostic tests (in particular, serology and sequencing) for infectious diseases: viral lineage/clade; test date, type, performer, and manufacturer; target gene; quantitative and qualitative results; and specimen identifier, type, and collection date. </sec> <sec> <title>CONCLUSIONS</title> The identification of CDEs for infectious diseases is the first step in facilitating the exchange and possible merging of a subset of data across clinical studies (and with that, large research projects) for possible shared analysis to increase the power of findings. The path to harmonization and standardization of clinical study data in the interest of interoperability can be paved in 2 ways. First, a map to standard terminologies ensures that each data element’s (variable’s) definition is unambiguous and that it has a single, unique interpretation across studies. Second, the exchange of these data is assisted by “wrapping” them in a standard exchange format, such as Fast Health care Interoperability Resources or the Clinical Data Interchange Standards Consortium’s Clinical Data Acquisition Standards Harmonization Model. </sec> 

Creation of Standardized Common Data Elements for Diagnostic Tests in Infectious Disease Studies: Semantic and Syntactic Mapping (Preprint)

In response to the Covid-19 pandemic, the National Heart, Lung, and Blood Institute launched five multisite clinical trials testing candidate host tissue–directed medical interventions to hasten recovery, improve function, and reduce morbidity and mortality. Speed, flexibility, and collaboration were essential. This article from the Steering and Executive committees describes the Collaborating Network of Networks for Evaluating Covid-19 and Therapeutic Strategies (CONNECTS) research program that enrolled 6690 participants and evaluated 18 intervention strategies using 10 molecular agents across the care continuum (outpatient, inpatient, and post discharge), and reports lessons learned from this initiative. Successes include rapid trial execution through collaboration and adaptive platform designs. Challenges that impeded efficiency included time required to execute subcontracts, constraints on clinical research workforce, and limited research infrastructure in nonacademic settings. 

Lessons Learned from National Heart, Lung, and Blood Institute Covid-19 Clinical Trials

The NIH RADx Data Hub serves as an access platform for COVID-19 research studies funded by the U.S. National Institutes of Health (NIH). The RADx Data Hub contains a range of patient-related information, including diagnostic test data, demographic details, and clinical records. Controlled access to data safeguards patient privacy, but also presents challenges for users unfamiliar with the platform, developers seeking to test processes, and curators aiming to showcase its features. Meanwhile, using actual patient data raises confidentiality concerns and timing challenges, such as needing to get requisite approval. To address these limitations, synthetic datasets support generation of simulated patients, enabling effective learning and testing of the system with data closely resembling real study data. This paper describes a dynamic synthetic data generator for COVID-19 research data based on RADx codebooks and external sources. The generator empowers RADx Data Hub users to model diverse patient cohorts, generate hypotheses, and attain a better grasp of existing COVID-19 data that they may access through the RADx Data Hub.

Enhancing Data Accessibility in COVID Research - A Synthetic Data Generator for the RADx Data Hub

Background: Coronavirus Disease 2019 (COVID-19) instigated a flurry of clinical research activity. The unprecedented pace with which trials were launched left an early void in data standardization, limiting the potential for subsequent data pooling. To facilitate data standardization across emerging studies, the National Heart, Lung, and Blood Institute (NHLBI) charged two groups with harmonizing data collection, and these groups collaborated to create a concise set of COVID-19 Common Data Elements (CDEs) for clinical research. Methods: Our iterative approach followed three guiding principles: 1) draw from existing multi-center COVID-19 clinical trials as precedents, 2) incorporate existing data elements and data standards whenever possible, and 3) alignment to data standards that facilitate data sharing and regulatory submission. We also supported rapid implementation of the CDEs in NHLBI-funded studies and iteratively refined the CDEs based on feedback from those study teams Results: The NHLBI COVID-19 CDEs are publicly available and being used for current COVID-19 clinical trials. CDEs are organized into domains, and each data element is classified within a three-tiered prioritization system. The CDE manual is hosted publicly at https://nhlbi-connects.org/common_data_elements with an accompanying data dictionary and implementation guidance. Conclusions: The NHLBI COVID-19 CDEs are designed to aid data harmonization across studies to achieve the benefits of pooled analyses. We found that organizing CDE development around our three guiding principles focused our efforts and allowed us to adapt as COVID-19 knowledge advanced. As these CDEs continue to evolve, they could be generalized for use in other acute respiratory illnesses.

/pdf/development-and-implementation-of-the-national-heart-lung-3ul9mt5k.pdf

Development and implementation of the National Heart, Lung, and Blood Institute COVID-19 common data elements

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Development and implementation of the National Heart, Lung, and Blood Institute COVID-19 common data elements