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 the National Institute of Neurological Disorders and Stroke (NINDS) have identified a need for a network of stroke clinical trials centers in order to prioritize and efficiently design and conduct exploratory (Phase II) clinical trials and confirmatory (Phase III) clinical trials to identify and advance stroke treatments. The NIH StrokeNet is a multidisciplinary stroke research infrastructure that includes a National Clinical Coordinating Center, a National Data Management Center (NDMC), 25 Regional Coordinating Centers and the NINDS. Its mission is to develop and test new therapies for stroke treatment, recovery, and prevention that can decrease the global burden of stroke. 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 end, the Data Coordination Unit (DCU) at the Medical University of South Carolina has developed a web-based comprehensive integrated electronic data capture and clinical trials management system, WebDCU?, that enables data entry from the participating clinical sites with extensive data quality control and that provides the necessary tools to efficiently manage operational activities for concurrent multiple trials, while ensuring compliance with the NINDS Common Data Elements and FDA guidance and regulations. Using the WebDCU? system, we have developed, implemented and maintained 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? incorporates trial management information system that provides full support for all study operational activities in the StrokeNet. The neuroimaging repository at the NDMC houses protocol designated imaging that requires central review for all StrokeNet trials. In addition, in collaboration with the study Principal Investigator and other parties of the StrokeNet, the NDMC biostatisticians: 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; disseminate study results via presentations and publications; and create public use data sets for data sharing when a study is completed.

Public Health Relevance

The NIH StrokeNet is a research network infrastructure to efficiently conduct clinical trials to identify intervention for: (1) acute treatment of stroke; (2) prevention of stroke recurrence; and (3) recovery and rehabilitation after stroke. The National Data Management Center (NDMC) of the NIH StrokeNet offers economies of scale through its centralized clinical management system to conduct multiple stroke clinical trials simultaneously and efficiently. This is expected to have an impact on the public health by potentially reducing stroke occurrences and the burden of stroke to the individuals as well as to the society.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Project--Cooperative Agreements (U01)
Project #
Application #
Study Section
Special Emphasis Panel (ZNS1)
Program Officer
Vivalda, Joanna
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Medical University of South Carolina
Public Health & Prev Medicine
Schools of Medicine
United States
Zip Code
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

Showing the most recent 10 out of 15 publications