COVID-19 Therapeutic Trial Common Data Elements
With the multitude of COVID-19 research being conducted, a common set of data elements is essential for efficiency in the study design process, increased power for discovery through aggregated data, and improved accountability for generalizability and reproducibility. The NHLBI-CONNECTS set of common data elements (CDE) are being developed in collaboration with the NHLBI-CONNECTS Study Design Core and NHLBI-funded research networks such as SIREN and PETAL. Trials funded through NHLBI-CONNECTS will implement these CDEs and make their data available through NHLBI’s BioData Catalyst data access and compute environment.
Materials for each release of the Common Data Elements are available below. Please refer to the Release Notes for a summary of changes for each new version, as well as a preview of upcoming changes.
Why are COVID-19 Trial Common Data Elements Needed?
The COVID-19 pandemic presents a new challenge to clinical researchers. Many therapeutic agents need to be studied and trials need to be launched rapidly to address the ongoing crisis. A lack of data standardization across trials will make comparisons between therapeutic agents difficult and could hinder the identification of optimal treatments.
CDEs address these challenges in the following ways:Effective study design
- Allows researchers to choose appropriate therapeutics and controls based on data from previous studies.
- Enables more accurate estimates of priors in new studies.
- Allows for reuse of case report forms across studies, lessening work required for trials to launch.
- Simplifies comprehension through familiarity for regulators, decreasing DSMB and IRB review time.
- Promotes the inclusion of variables that may otherwise be dropped for expediency, but which are known to be important in disease progression.
- Facilitates standardized processes for data and specimen collection and the sharing of best practices across centers.
- Does not restrict collection of other informative data. Instead, because the core data elements are already well established, it allows researchers to focus on selecting additional data that have maximum information content.
- Obviates the need to try and draw inferences from studies that have different outcomes measured in different ways even if treatments are similar.
- Enables the combination of smaller trials to produce meaningful findings.
- Ensures key covariates are measured similarly so merging and comparison of studies can occur.
- Improves data integration with other NIH COVID-19 studies such as Accelerating Covid-19 Therapeutic Interventions and Vaccines (ACTIV) trials and National COVID Cohort Collaborative (N3C).
- Allows for control groups in studies of different therapies yet common outcomes to be shared to boost understanding of effect sizes.
- Enables comparison of generalizability of trial populations and outcomes.
- Improves the interpretability of a body of literature.
Suggestions or Questions?
The Common Data Elements materials are working documents that will be updated on a routine basis. We welcome any feedback at firstname.lastname@example.org