BACKGROUND: 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. PURPOSE: 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. METHODS: 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. RESULTS: 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. LIMITATIONS: A single clinical trial network comprising addiction researchers and community treatment programs was assessed. The findings may not be applicable to other research settings. CONCLUSIONS: 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.
BACKGROUND: 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. PURPOSE: 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. METHODS: 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. RESULTS: 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. LIMITATIONS: A single clinical trial network comprising addiction researchers and community treatment programs was assessed. The findings may not be applicable to other research settings. CONCLUSIONS: 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.
Authors: Diana M Escolar; Erik K Henricson; Livia Pasquali; Ksenija Gorni; Eric P Hoffman Journal: Neuromuscul Disord Date: 2002-10 Impact factor: 4.296
Authors: John S March; Susan G Silva; Scott Compton; Ginger Anthony; Joseph DeVeaugh-Geiss; Robert Califf; Ranga Krishnan Journal: J Am Acad Child Adolesc Psychiatry Date: 2004-05 Impact factor: 8.829
Authors: Rashmi Patel; Soon Nan Wee; Rajagopalan Ramaswamy; Simran Thadani; Jesisca Tandi; Ruchir Garg; Nathan Calvanese; Matthew Valko; A John Rush; Miguel E Rentería; Joydeep Sarkar; Scott H Kollins Journal: BMJ Open Date: 2022-04-22 Impact factor: 3.006
Authors: Eveline Hürlimann; Nadine Schur; Konstantina Boutsika; Anna-Sofie Stensgaard; Maiti Laserna de Himpsl; Kathrin Ziegelbauer; Nassor Laizer; Lukas Camenzind; Aurelio Di Pasquale; Uwem F Ekpo; Christopher Simoonga; Gabriel Mushinge; Christopher F L Saarnak; Jürg Utzinger; Thomas K Kristensen; Penelope Vounatsou Journal: PLoS Negl Trop Dis Date: 2011-12-13