West Virginia University is celebrating its seventh annual Research Week, April 7-11. This week is an opportunity to showcase the research efforts of our faculty and students. One faculty being recognized is the School of Pharmacy's Dr. Sabina Nduaguba, an assistant professor in the Department of Pharmaceutical Outcomes and Policy. Dr. Nduaguba also holds a joint faculty position with the WVU Cancer Institute.
Lori Acciavatti, communications specialist at the WVU Cancer Institute, recently talked to Dr. Nduaguba about her cancer research.
Lori: Why did you choose to become a doctor/researcher/expert in your field? Was there a specific "a-ha" kind of moment?
Dr. Nduaguba: I chose to pursue a career in this field due to a combination of my dual interest in data science and cancer research, which led me to pursue a minor in statistical modeling and explore cancer research in graduate school. My position as a faculty member in both the School of Pharmacy's Pharmaceutical System and Policy Department and the School of Medicine's Cancer Prevention and Control Department gives me the opportunity for interdisciplinary work, and it suits my desire to make a meaningful impact on patient care through data-driven research and interventions.
Lori: What's the best thing about doing what you do?
Dr.Nduaguba: Mentoring students. I mentor students at both the PharmD level and graduate level. I am also an instructor in the Cancer Cell Biology program. I believe that for my students to excel, interest is key.
Lori: What's new with your role at WVU?
Dr. Nduaguba: I was recently awarded an NIH R03-funded project that aims to adapt and test the feasibility of a multi-level intervention to reduce treatment delays for lung cancer patients. This work builds on previous work funded by internal grants and subawards.
Lori: What's unique about you?
Dr. Nduaguba: I will say that my research program at WVU HSC is unique – using data science to identify gaps in cancer care and inform the design of interventions to address the gaps, in other words, data-to-bedside form of translational research.
