Stroke is a disabling and often fatal disease that affects all ages, but mostly in the elderly. Stroke incidence is expected to rise in the US as its population ages; therefore, its impact on public health and health resources utilization will continue to be significant. The Stroke Progress Review Group and NINDS have identified a need for a network of stroke clinical trials centers in order to prioritize and efficiently design nd conduct translational and exploratory early Phase I/II clinical trials and large Phase III clinical trials to identify and advance stroke treatments. The NINDS Stroke Trials Network is a multidisciplinary stroke research infrastructure that consists of a National Clinical Coordinating Center, a National Data Management Center (NDMC), and 25 Regional Coordinating Centers. The NDMC's role is to establish a collaborative relationship with all parties involved in the Network and provide efficient and standardized central data management that yields high quality data and statistical support in the planning and execution of the stroke trials. To this en, the Data Coordination Unit (DCU) at the Medical University of South Carolina has developed a web-based comprehensive integrated data and project management system, WebDCU, that enables distributed data entry from the participating clinical sites with extensive data quality control and also provides the necessary tools to efficiently manage operational activities for multiple trials, while ensuring compliance with the NINDS Common Data Elements and FDA regulations. Using the WebDCU? system, we will develop, implement and maintain a central database that streamlines and maximizes efficiency in the management of data collection, processing, and monitoring of clinical, biomarker and neuroimaging data. In addition, the WebDCU will incorporate trial management information system that will provide full support for all study operational activities in the Stroke Trials Network. The Neuroimaging Core of the NDMC will create neuroimaging repository for the stroke trials. In collaboration with the study Principal Investigator and the other parties of the Stroke Trials Network, the DCU biostatistics faculty members will: contribute to the innovative and efficient protocol development (including study design and case report forms development); statistically monitor study progress; generate reports for the DSMB and the study teams; conduct interim and final analysis and dissemination of study results via presentations and publications; and create public use datasets for data sharing. Finally, as the Statistics and Data Management Center for the Neurological Emergencies Treatment Trials (NETT) Network, the DCU offers unique advantage of seamless collaboration between the NETT and Stroke Trials Networks.

Public Health Relevance

The NINDS Stroke Trials Network will establish the infrastructure to conduct clinical trials to identify treatments to minimize death and disability ater stroke; to prevent stroke recurrence; and to recover functionality after stroke. The National Data Management Center (NDMC) of the Network offers economies of scale that will enable the Network to conduct multiple clinical trials simultaneously and efficiently. This in turn will have n impact on the public health by potentially reducing stroke occurrences and the burden of stroke to the individuals as well as to the society.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01NS087748-04
Application #
9322342
Study Section
Special Emphasis Panel (ZNS1)
Program Officer
Vivalda, Joanna
Project Start
2014-04-01
Project End
2018-07-31
Budget Start
2017-08-01
Budget End
2018-07-31
Support Year
4
Fiscal Year
2017
Total Cost
Indirect Cost
Name
Medical University of South Carolina
Department
Public Health & Prev Medicine
Type
Schools of Medicine
DUNS #
183710748
City
Charleston
State
SC
Country
United States
Zip Code
29403
Broderick, Joseph P; Adeoye, Opeolu; Elm, Jordan (2017) Evolution of the Modified Rankin Scale and Its Use in Future Stroke Trials. Stroke 48:2007-2012
Zhao, Wenle; Everett, Colin C; Weng, Yanqiu et al. (2017) Guessing strategies for treatment prediction under restricted randomization with unequal allocation. Contemp Clin Trials 59:118-120
Meinzer, Caitlyn; Martin, Renee; Suarez, Jose I (2017) Bayesian dose selection design for a binary outcome using restricted response adaptive randomization. Trials 18:420
Jiang, Yunyun; Zhao, Wenle; Durkalski-Mauldin, Valerie (2017) Impact of adaptation algorithm, timing, and stopping boundaries on the performance of Bayesian response adaptive randomization in confirmative trials with a binary endpoint. Contemp Clin Trials 62:114-120
Cramer, Steven C; Wolf, Steven L; Adams Jr, Harold P et al. (2017) Stroke Recovery and Rehabilitation Research: Issues, Opportunities, and the National Institutes of Health StrokeNet. Stroke 48:813-819
Zhao, Wenle; Pauls, Keith (2016) Architecture design of a generic centralized adjudication module integrated in a web-based clinical trial management system. Clin Trials 13:223-33
Broderick, Joseph P; Palesch, Yuko Y; Janis, L Scott et al. (2016) The National Institutes of Health StrokeNet: A User's Guide. Stroke 47:301-3
Zhao, Wenle; Ramakrishnan, Viswanathan (2016) Generalization of Wei's urn design to unequal allocations in sequential clinical trials. Contemp Clin Trials Commun 2:75-79
Lansberg, Maarten G; Bhat, Ninad S; Yeatts, Sharon D et al. (2016) Power of an Adaptive Trial Design for Endovascular Stroke Studies: Simulations Using IMS (Interventional Management of Stroke) III Data. Stroke 47:2931-2937
Zhao, Wenle; Mu, Yunming; Tayama, Darren et al. (2015) Comparison of statistical and operational properties of subject randomization procedures for large multicenter clinical trial treating medical emergencies. Contemp Clin Trials 41:211-8

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