CUBE Program: A Student’s View of the World of Biostatistics

Kayla Williams

During the summer of 2022, I participated in the Collaborative Undergraduate Biostatistics Experience program at Virginia Tech. I enjoyed learning about the application of mathematical and statistical concepts to answer real-world questions and realized this is something I want to do as a career.

The CUBE program introduced me to the field of biostatistics. I was not just learning about introductory biostatistics concepts and topics; I was immersed in the work that an actual collaborative biostatistician would be doing. I helped form hypotheses for research questions, applied the appropriate statistical methods, summarized my results using various tables and figures, and presented those results to my peers and other researchers.

More About the CUBE Program
The CUBE program is an eight-week training program designed to give motivated, underrepresented undergraduate students in STEM the opportunity to engage in a full-time (~40 hours/week) collaborative data science experience with related professional development activities. CUBE is aimed at promoting diversity, equity, and inclusion in the STEM fields. The CUBE program is built on the following four pillars:

  • Training in introductory biostatistics
  • Training in R programming
  • Professional development
  • Collaborative research project addressing research questions in various disciplines

CUBE is accepting applications through March for the summer of 2024. Check out this and additional internships available in 2024.

A Glimpse into Biostatistics Careers

With my peers, I was given the opportunity to explore many aspects of collaborative biostatistics, from the beginning of the research cycle to the end. We utilized R programs to assess sociodemographic predictors of anterior cruciate ligament injuries diagnosed in the emergency department using data from the nationwide emergency department sample of the Healthcare Utilization Project. Completing the research cycle, we interpreted our results and communicated those results through oral and poster presentations at summer research symposiums.

The eight-week CUBE program gave me the experience and knowledge I needed to become aware of collaborative biostatistics as a potential career. My career goals are to eventually engage in collaborative research at a university as a statistician. Without the CUBE program, I may have never fully realized my passion for biostatistics, because it’s not a field that is easily understood with online research.

A Statistics Power Skill: Collaboration

I also learned how much collaboration there is in the field of biostatistics. As part of the CUBE program, we visited several research labs and centers, including an animal cancer center and a smoking and alcohol lab to study addictive behaviors. We interacted with those researchers and learned how biostatistics can be used to analyze and publish the data they collect.

For my collaborative group project, I worked closely with my CUBE peers and Charlotte Baker, an epidemiologist who studies the control and prevention of sports and recreation injuries among youth. Actively collaborating and learning about how biostatisticians can be engaged in different areas of research was something I liked about the CUBE program and the field of collaborative biostatistics.

Start Anywhere—Just Start

For students who want to pursue a career in collaborative biostatistics, my biggest piece of advice would be not to worry too much about your undergraduate major. There were so many collaborative biostatisticians I met who didn’t study mathematics or statistics as undergraduates and still found their way to the field.

I also think it is important to gain experience through internships or programs like the CUBE program to get a glimpse into a day in the life of a collaborative biostatistician and to connect with mentors and other people who care about your success and want to help you early on in your career.

Ask for Help and Get Involved

My biggest takeaway from this experience is to never be afraid to ask for help. I used to think if I didn’t know how to do everything before choosing a certain career path, I shouldn’t take that route. However, I’ve learned even the most experienced professionals ask for help when they don’t know how to approach a research question or know little about a specific method. Having the confidence to ask for help and believing there are no stupid questions when it comes to research was important for me because that’s something I have always struggled with.

Through this experience, I also learned to have more patience. For my collaborative group project, we would have to wait 30 minutes to an hour for our code to run in R because the data we were working with was so large. I learned that good research takes time and cannot be completed in a day.

Editor’s Note: This article originally appeared on ThisIsStatistics in May.