Frequently Asked Questions

N.B.: The funding for this project expired by June 1, 2022. The COVID-evidence database will continue to be automatically updated weekly until December 31, 2022 but manual screening has been stopped as of June 1, 2022. 
 

What is COVID-evidence BASIC?

It is a comprehensive dataset with basic information on all randomized clinical trials to treat or prevent COVID-19 included in the database.

What is the data flow?

The WHO ICTRP and ClinicalTrials.gov are searched and imported automatically whereas the L·OVE platform is searched manually. After conversion to a standard format, all entries are assigned to a  screening collection (i.e. dataset). The collections ensure transparency and reproducibility of each steps of the data flow.

All entries are screened for eligibility (automatically for the WHO ICTRP and ClinicalTrials.gov collections and manually for the L·OVE platform collection).

Following automatic de-duplication and data extraction, eligible entries are indexed in the Cove BASIC collection. The variables of entries retrieved from the WHO ICTRP and ClinicalTrials.gov are automatically updated on a weekly basis, when applicable.

Entries that are related to the same trial are linked and stored in the linked trials collection.

Entries included in COVID-evidence BASIC collection are further manually screened, verifying their eligibility. Manual extraction and data verification of automatic data extraction are done for “Expansion Modules” that go beyond the collection of basic information of trials.

In the COVID-evidence database, entries and all related information are made publicly available.

 

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How reliable are filters?

Filters provide a fast access related to users’ specific interests and in large majority are automatically populated based on information available in the database. The filters are designed to focus on trial characteristics and important clinical questions (topics). Filters on trial characteristics are based on automatic data extraction and processing. 

Filters on “Vaccine” and “Post-acute COVID” topics are based on keyword searches in title and intervention field of a trial entry. Trials retrieved by the topic “Preventive non-pharmaceutical interventions (NPI)” are manually identified in our data set and restricted to registry entries. Details on filters are provided in our protocol on Open Science Framework. 

What are “Expansion Modules”?

Expansion modules aim to address more detailed questions that go beyond the collection of basic information of trials.

The modular concept allows targeting emerging needs for example by extracting in-depth information for only a subset of trials (e.g. trials assessing hydroxychloroquine or convalescent plasma), or for a certain population (e.g. children only), or country of origin. International collaborations and close interactions with various trial teams are the cornerstone of our expansion modules aiming at providing rapid answers to important clinical questions.

Do you share your data?

All data can be downloaded for free on our database page. Our protocol and other relevant projects can be accessed on Open Science Framework. All codes related to the construct of the database is available on GitHub.

How accurate is the data?

We aim to provide data of highest standard through duplicate extractions and by keeping our methods transparent. We focus on automatic extractions with continuously evolving filters due to the unprecedented growth of accumulating trial information. We present the current status of the data in the “status of review” column, marking automatic extractions (“automatic”), ongoing manual extraction (“in manual extraction”), and fulfilled manual extraction (“manual extraction completed”. Specific information on topics covered by expansion modules is available via specific filters.

Do you work with other initiatives?

Yes, we are in contact with several initiatives whose aims overlap with ours. For example, we have partnered with the Living OVerview of Evidence (L·OVE) platform for COVID-19 that facilitates our tracking of publications and preprints of randomized controlled trials assessing an intervention to treat or prevent COVID-19.

What makes COVID-evidence different from other initiatives?

Examples of other efforts to summarize the literature on COVID-19 are presented here. Some overlap exists, mainly for COVID-evidence BASIC. We aim to make use of them in order to increase data reliability and work efficiency. We would like to emphasize the following COVID-evidence characteristics:

  • We apply highest-standard systematic review methods.
  • Beyond tracking trial registries, we integrate manual data extraction by experienced meta-researchers for specific fields with special importance.
  • We have a broad definition of trials included – not only drug treatments.
  • Trials are included all across their lifespan – from planned to published.
  • We integrate information from different sources, including fulltexts, and our reviewer team has native speakers of several languages (e.g., English, Chinese, French, German, and Dutch).
  • We have created strong international collaborations with trial teams.

Where do I find more details on COVID-evidence?

For more details on our work, please visit our full protocol on Open Science Framework (OSF; DOI 10.17605/OSF.IO/GEHFX). We aim to continuously upload our updated methods and processes.

Who is paying?

This is a non-profit initiative. The project is funded by the Swiss National Science Foundation (project 31CA30_196190). The members of the COVID-evidence core team are supported by our institutions at the University of Basel (DKF) and Stanford University. The Ninox database used in the first project stage was kindly provided for free by Ninox software Gmbh, Berlin.

How can I help?

If you are interested in contributing to this project, please send an email to lars.hemkens@usb.ch.