A centralized informatics infrastructure for the National Institute on Drug Abuse Clinical Trials Network

Clinical trial networks (CTNs) were created to provide a sustaining infrastructure for the conduct of multisite clinical trials. As such, they must withstand changes in membership. Centralization of infrastructure including knowledge management, portfolio management, information management, process automation, work policies, and procedures in clinical research networks facilitates consistency and ultimately research.

In 2005, the National Institute on Drug Abuse (NIDA) CTN transitioned from a distributed data management model to a centralized informatics infrastructure to support the network's trial activities and administration. We describe the centralized informatics infrastructure and discuss our challenges to inform others considering such an endeavor.

During the migration of a clinical trial network from a decentralized to a centralized data center model, descriptive data were captured and are presented here to assess the impact of centralization.

We present the framework for the informatics infrastructure and evaluative metrics. The network has decreased the time from last patient-last visit to database lock from an average of 7.6 months to 2.8 months. The average database error rate decreased from 0.8% to 0.2%, with a corresponding decrease in the interquartile range from 0.04%-1.0% before centralization to 0.01-0.27% after centralization. Centralization has provided the CTN with integrated trial status reporting and the first standards-based public data share. A preliminary cost-benefit analysis showed a 50% reduction in data management cost per study participant over the life of a trial.

A single clinical trial network comprising addiction researchers and community treatment programs was assessed. The findings may not be applicable to other research settings.

The identified informatics components provide the information and infrastructure needed for our clinical trial network. Post centralization data management operations are more efficient and less costly, with higher data quality.