Nawar Shara, PhD
Nawar Shara

Nawar Shara, PhD

Chief, Research Data Science, MedStar Health Research Institute
Co-Director, Center of Biostatistics, Informatics and Data Science (CBIDS)

Nawar M. Shara, PhD, is chief, research data science and the co-director of the Center of Biostatistics, Informatics and Data Science (CBIDS) at the MedStar Health Research Institute. Dr. Shara is an associate professor of medicine at Georgetown University School of medicine and director of the Biostatistics, Epidemiology and Research Design (BERD) Core and co-director of the Biomedical Informatics core at the Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS). Dr. Shara is a seasoned biostatistician and established, NIH-funded clinical investigator with more than 18 years’ experience overseeing statistical and data management activities for large, multi-center clinical trials.

As director of the CBIDS, she leads a multi-disciplinary team whose mission is to engage and support research by providing infrastructure services such as study design, statistical consulting, data management, cohort discovery and innovative data solutions. As director of BERD and co-director of Biomedical Informatics for GHUCCTS, she oversees a wide range of projects stemming from multi-disciplinary collaborations spanning several institutions across the CTSA consortium, she develops courses and workshops, and mentors junior faculty and research scholars.

Dr. Shara’s research interests focus on collaborative and team approaches for big data solutions, predictive analytics, data mining, artificial intelligence (AI) and machine learning (ML data solutions such as voice assisted devices to improve patient outcomes, patient-provider communication and reduce healthcare utilization.

Dr. Shara is experienced in the design and development of biostatistical curriculum, modular courses and workshops with a focus on applications of AI/ML in health sciences. She has decades of serving as scientific reviewer on study sections for the Veterans Affairs, Department of Defense, and National Institutes of Health (NIH), and have published over 100 peer reviewed papers. Dr. Shara is site lead for the AIM-AHEAD NIH funded consortium for the Training Core.

Dr. Shara received her undergraduate degree in Economics from Damascus University, and received her master’s degree and PhD in applied statistics from American University in Washington DC.

Research Interests

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.

Selected Research

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).

View Dr. Shara's publications on PubMed

Research Areas


  • Biostatistics/Bioinformatics
    Data Science