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.
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.
|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; 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|
|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|
|Ellerbe, Caitlyn (2015) What Information Will a Statistician Need to Help Me With a Sample Size Calculation? Stroke 46:e159-61|
|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|
|Zhao, Wenle (2015) Mass weighted urn design--A new randomization algorithm for unequal allocations. Contemp Clin Trials 43:209-16|
|Liebeskind, David S; Albers, Gregory W; Crawford, Karen et al. (2015) Imaging in StrokeNet: Realizing the Potential of Big Data. Stroke 46:2000-6|
|Zhao, Wenle (2014) A better alternative to stratified permuted block design for subject randomization in clinical trials. Stat Med 33:5239-48|
|Palesch, Yuko Y (2014) Some common misperceptions about P values. Stroke 45:e244-6|