Dr. George Howard has focused on being a statistician, working in the overlap of the development and application of statistical methods in the domains of clinical trials and epidemiological studies for several years. In the domain of clinical trials, he is currently the PI of the Statistical and Data Management Center for several trials including Carotid Revascularization for Primary Prevention of Stroke Trial (CREST-2). Since 1994 a major focus of his research has been on geographic and racial differences in stroke risk.
Job Title: Professor
The core of the Statistical and Data Coordinating Center (SDCC) at UAB School of Public Health is an established team that has successfully provided coordination for many clinical trials of primary or secondary stroke prevention that reached successful completion including such SPS3 and CRES. We are currently the SDCC for the CREST-2 funded by NIH-NINDS through an R01 with George Howard as PI. Dr. George Howard has a wealth of experience in biostatistics, data management, and the direction of coordinating centers of multicenter studies. The team also includes Dr. Virginia Howard who has been involved in the successful conduct and management of multicenter stroke clinical trials for over 30 years, most notably as co-investigator for ACAS, VISP, and most recently CREST and CREST-2. The entire Statistical and Data Coordinating Center team consists of investigators, systems developers, statisticians, project managers, clinical nurse manager, and support staff who have worked together in the successful design and conduct of these and other stroke studies. Dr. Russell Griffin, Department of Epidemiology, is the newest team member bringing expertise in linkages with medical records and complex data management processes.
Address:
1665 University Boulevard
Birmingham, AL 35294-0022
Phone: 205-934-4905
Email: ghoward@uab.edu
Job Title: Professor
After receiving a BS in Mathematics from Tennessee Wesleyan College and an MS in Mathematics from Tennessee Technological University, Dr. Long attended the University of North Carolina, Chapel Hill for her PhD in Biostatistics. Her doctoral research proposed the marginalized zero-inflated Poisson model, focusing on providing marginal inference for count data with excess zeroes.
She joined the UAB Department of Biostatistics in July 2016. Her research interests include categorical data analysis, particularly zero-inflated models, and meta-analysis for single case design data. She has collaborated across many disciplines, most recently in injury prevention and dental research.
Project Role: Senior Analyst of Advanced Enterprise Analytics
Since 2005, Dr. Griffin, worked as an injury epidemiologist with the Center for Injury Sciences (CIS) at the University of Alabama at Birmingham (UAB), and was appointed Co- Director of the Research Support Services Unit of the CIS in 2010. In 2012, Dr. Griffin was hired as an Assistant Professor in the Department of Epidemiology at UAB with a focus on injury epidemiology, and in 2014 was appointed Senior Analyst of Advanced Enterprise Analytics.
The studies which he has co-authored have examined a wide-range of injury-related topics, including, but not limited to, epidemiological surveillance of consumer product-related injuries, motor vehicle collision related injuries, and the process of care for hospitalized trauma patients. He has extensive experience in research involving the use of electronic medical record data on a wide variety of clinical topics such as examination of the effects of process of care changes on patient outcomes, blood transfusion utilization throughout the hospital by medical service, and examination of the incidence of and predictive factors of inpatient readmissions.
More recently, he has been utilizing my knowledge of injury epidemiology and analyzing electronic medical record data to examine nutrition's role in the care of a patient posttrauma or post-burn injury. Through working on these studies, I am well-versed with a variety of statistical models, including generalized linear models (including logistic regression), random effects mixed models, and time-to-event analyses such as proportional
hazards regression.