The Department of Population Health Sciences at the Medical College of Georgia at Augusta University was established in 2017 to strengthen the public medical school’s impact on the health of Georgia.
The broader mission of the department is to understand, preserve and improve the health human populations through research, training and community engagement, especially focusing on the health of Georgians. It will enable scientists and educators to pursue and share a more comprehensive perspective on who are getting sick, what they have and why, and which targeted interventions and treatments are most effective for them.
The mission of our biostatistics program is to advance knowledge in the fields of biostatistics, and to provide leadership and scholarship in research, teaching and mentoring, for the advancement of biomedical science and improvement of human health.
We study the distributions and determinants of diseases and health conditions in populations.
The HEM division was created in response to the growing need among medical professionals, health care researchers and students for support in health economic evaluations and health care modeling.
The goal of the workshop is to educate and empower graduate students, postdoctoral fellows and early-career researchers and faculty members with emerging statistical methods to address the complex data arising from various fields, including from biomedical and cyber sciences.
"We're so excited to be partnering with AU to really make this robust and to serve our community and to serve our people and keep them safe and healthy."
Being unable to get time off from work and being “nervous” about seeing a health care provider were among many cited reasons for not always getting timely survivorship care, investigators at the Medical College of Georgia and Georgia Cancer Center report.
The new Master of Science in Data Science at MCG will combine theory with application and will be the first program of its kind in the Augusta area.
Assistance in design of clinical trials, experimental design, power and sample size estimation, randomization, data management, modeling, data analysis, interpretation and manuscript preparation.
