DISQOVER for determining relevant clinical data sets

Data re-use best practices

With its powerful data-modeling and formatting capabilities, DISQOVER allows study designers and other users to ‘re-use’ data that was generated for other primary purposes, e.g. from unsuccessful trials. In some cases, the combined and re-usable data from previous studies can generate new hypotheses or insights, without the need to set up a new clinical trial.

• In cohort selection, for example, real-world data can be re-used in powerful ways to design a study with as little variability as possible.
• Non-clinical data can provide important insights into how the trial should be designed to avoid adverse effects – for example, through more precise identification of the patient population subset where the treatment would have the most beneficial impact.
• Preliminary providing potential partner sites with these inclusion/exclusion criteria can significantly optimize the recruitment process.
• By integrating existing internal and external clinical data and real-world data, clinical scientists can easily identify relevant datasets to support their hypothesis.

Capturing meta data and making it searchable

Clinical trials result in complex and multidimensional data sets. By capturing the generated meta data, DISQOVER allows users to

• narrow down on specific perspectives or criteria;
• perform downstream analysis;
• create new subsets according to various criteria.

By enabling the re-use of data in this way, DISQOVER also helps organizations to create ‘data catalogs’.  Such a catalog can significantly improve data management and knowledge discovery within the organization, and helps lay the groundwork for future successful clinical trial design.

More DISQOVER application fields for clinical trials