Abstract
Background/Aims Clinical research trials often have similar fundamental tracking needs, despite being quite variable in their specific logic and activities. A model tracking database that can be quickly adapted by a variety of studies has the potential to achieve significant efficiencies in database development and maintenance.
Methods Over the course of several different clinical trials, we have developed a database model that is highly adaptable to a variety of projects. Rather than hard-coding each specific event that might occur in a trial, along with its logical consequences, this model considers each event and its parameters to be a data record in its own right. Each event may have related variables (metadata) describing its prerequisites, subsequent events due, associated mailings, or events that it overrides. The metadata for each event is stored in the same record with the event name. When changes are made to the study protocol, no structural changes to the database are needed. One has only to add or edit events and their metadata. Changes in the event metadata automatically determine any related logic changes. In addition to streamlining application code, this model simplifies communication between the programmer and other team members. Database requirements can be phrased as changes to the underlying data, rather than to the application code. The project team can review a single report of events and metadata and easily see where changes might be needed. In addition to benefitting from streamlined code, the front end database application can also implement useful standard features such as automated mail merges and to do lists.
Results The event-based data model has proven itself to be robust, adaptable and user-friendly in a variety of study contexts. We have chosen to implement it as a SQL Server back end and distributed Access front end. Interested readers may request a copy of the Access front end and scripts for creating the back end database.
Discussion An event-based database with a consistent, robust set of features has the potential to significantly reduce development time and maintenance expense for clinical trial tracking databases.
