Nawar M. Shara, PhD, is director, Department of Biostatistics and Bioinformatics, MedStar Health Research Institute (MHRI); director, Biostatistics Core, Georgetown-Howard Universities Center for Clinical and Translational Science; and associate professor of Medicine, Division of Endocrinology, Georgetown University. Dr. Shara is an established, NIH-funded, clinical investigator with many years of experience overseeing statistical and data management activities for large, multi-center clinical trials. Within MHRI, she manages a department of PhD- and master's-level associates.
Dr. Shara has served as principal investigator, sub-investigator, and lead biostatistician on numerous studies and clinical trials. Her expertise is in the design and analysis of large-scale epidemiological trials, specifically in the development of novel statistical methodologies, such as adaptive dosage-finding techniques, imputation methods and predictive analytics for big data. In addition, she has been developing tools to extract electronic health records data for use in research to improve patient outcomes.
Her research has focused on developing novel statistical methodologies to investigate correlates of kidney and cardiovascular disease and to identify non-traditional predictors of these diseases. She has authored or co-authored nearly 50 publications in peer-reviewed journals, such as the American Journal of Kidney Disease, Plos One, and the International Journal of Stroke. She is a member of the American Statistical Association, the Royal Statistical Society, the American Society of Nephrology, and the Women in Statistics Caucus. She serves as chair of the Science and Research Committee for the National Arab American Medical Society.
Dr. Shara obtained undergraduate and graduate degrees at Damascus University, Damascus, Syria, and a master's degree and PhD from American University in Washington, DC.
Dr. Shara’s research interests include
- Adaptive designs
- Optimization schemes in clinical trials
- Design of experiments and development of novel statistical methodologies, such as data imputation and risk prediction models
- Big data
- Predictive analytics
- Data extraction from electronic medical records.
Randomly and Non-Randomly Missing Renal Function Data in the Strong Heart Study: A Comparison of Imputation Methods
Kidney and cardiovascular disease commonly occur in populations that have a high prevalence of diabetes. It can be challenging to study these two conditions simultaneously in longitudinal studies, because large amounts of data are often missing. In this study, Dr. Shara and colleagues found that determining whether data are missing at random or not can help in choosing a method to impute the data to reach the most accurate results. The results of this work have been published in PLoS One (2015;10:e0138923. doi: 10.1371/journal.pone.0138923).
Dietary Intake of Fiber, Fruit and Vegetables Decreases Risk of Kidney Stones: the Women's Health Initiative
Dr. Shara and colleagues evaluated the relationship between dietary intake of fiber, fruit, and vegetables and risk of kidney stones in postmenopausal women participating in the Women’s Health Initiative. The authors found that fiber, fruits and vegetables were associated with reduced risk of kidney stones. This work was published in The Journal of Urology (2014;192:1694-9. doi: 10.1016/j.juro.2014.05.086).
- Research Areas