The Original Data Science

Tracing the history of biostatistics at Brown — an essential component of all branches of public health and medical research.

When Joseph Hogan, Carole and Lawrence Sirovich Professor of Public Health and chair of the Department of Biostatistics, recruits Ph.D. candidates to Brown, he often mentions that biostatistics is the original data science. “This usually gets a couple of laughs,” he said, “but there’s a real point to highlight here.”

Data science depends on mathematics, statistics, high-level computing and deep engagement with a particular field of application, Hogan said. This lines up with the basics of biostatistics, which uses these same tools to help with making informed decisions in health care, conducting research to improve the health of populations, and uncovering mechanisms underlying biological processes.

But what sets biostatistics apart is its commitment to addressing real-world, high-impact problems. “Most of us in biostatistics got into the field because we want to make a difference,” Hogan said, “because we wanted to have an impact on human health and clinical medicine, and the understanding  of human biology, because we were interested in something other than just math and statistics.” 
​​Biostatistics, as the original data science, is also an original component of the Brown University School of Public Health. It stands alongside Behavioral and Social Sciences, Epidemiology and Health Services, Policy, and Practice as a foundational department of the school. 

Origins and Growth

Established in 2011, Brown University's Department of Biostatistics is still young, but its origins trace back nearly 30 years. It began in 1995, with the formation of Brown’s Center for Statistical Sciences (CSS), led by Constantine Gatsonis, Henry Ledyard Goddard Professor of Biostatistics and CSS’s first and current director. Under Gatsonis’ leadership, the center quickly became a hub for biostatistical  research and training, covering vital areas like cancer, Alzheimer’s disease, HIV, biomarker research, behavioral medicine, cardiology and digital imaging.

“The department has undergone a significant evolution since its inception,” Gatsonis said. “Initially, we began with limited resources and adopted a pragmatic ‘Brown way’ approach, prioritizing project development and research. As we secured projects and developed research initiatives, we also ventured into establishing graduate programs. The department’s ability to thrive was substantially influenced by the availability of research opportunities, collaborations, and our interdisciplinary nature.”

An important moment for CSS came with the introduction of degree programs in biostatistics, beginning with undergraduate concentration in statistics in 1997. Biostatistics education at Brown expanded to doctoral programs in 1999, with masters of science and masters of arts degrees in biostatistics added in 2007.

In 2005, Brown University awarded its first doctoral degree in biostatistics to Mei-Hsiu Chen Ph.D.'05, who now serves as the director of statistical consulting in the Department of Mathematics and Statistics at Binghamton University. After completing a master’s degree in statistics, Chen began work as a staff statistician alongside Gatsonis at CSS. There, she worked on clinical trials, developing her skills in data analysis and results dissemination.

“I found math and statistics to be a bit abstract and theoretical,” Chen said. “Applying those concepts in improving people’s health noninvasively was like solving puzzles and that’s what piqued my interest. The research environment at the Center was very open and stimulating, and that helped me think more broadly. It was exciting to be part of the formative years of the department.” 

Educating Future Generations

In 2006, the Biostatistics Section of the Department of Community Health was formed as a transitional organization in the process of laying the foundations for the establishment of a Department of Biostatistics as an integral part of a School of Public Health. Officially formed in July 2011, the department began with six tenure-track and four research faculty members. 

“Our graduate programs and undergraduate concentration had already taken root by that point,” Gatsonis said. “Our focus on graduate programs, importantly, predates the formal establishment of the department. This progressive approach allowed us to expand the department and solidify our role as a structural part of the School of Public Health.”

Today, the Department continues to place great emphasis on its educational programs. Faculty are currently mentoring about 65 master’s and doctoral students , shaping the future generation of statisticians. They’re also broadening the undergraduate program and offering introductory courses in statistics to ensure that Brown students stay up-to-date with the evolving data revolution.

In two forward-looking moves, Hogan, in partnership with Associate Director of the Online MPH Program, Jen Nazareno, is seeking to expand the department’s master’s program by introducing online learning alongside traditional classes, granting students greater flexibility in their studies.
In collaboration with Dean Ashish Jha, Assistant Dean for Equity, Diversity and Inclusion, Jai-Me Potter-Rutledge, and Associate Professor of Biostatistics, Jon Steingrimmson, he also initiated the NextGen Scholars in Biostatistics program, recruiting graduates of historically Black colleges and universities to increase diversity in the field and ultimately at the decisions-making levels of public health leadership. Assistant Professor and Director of ScM in Biostatistics Stavroula Chrysanthopoulou has also been heavily engaged in facilitating these programs. 

Forecasting the Future

In the early 1990s, while pursuing graduate studies in statistics at the University of Southern California, Hogan developed an interest in biostatistics. Motivated by the human toll of HIV, he saw the potential for statistics to make a difference in saving lives. A major early contribution of biostatisticians to the fight against HIV was to design clinical trials that 'surrogate endpoints' like CD4 rather than mortality, which enabled faster discovery and approval of effective antiviral medications.

Today, biostatisticians at Brown are engaged in a range of life-saving work, from forecasting early onset Alzheimer’s disease and enhancing lung cancer detection to advancing digital imaging tomography and computational capabilities. They are also analyzing electronic health records data, developing methods to discover genetic signatures of disease, and engaging in global health collaborations related to HIV and other infectious diseases

These techniques are being developed in partnership with the Eastern Cooperative Oncology Group and the American College of Radiology ECOG-ACRIN, the Providence-Boston Center for AIDS Research, the Center for Computational Molecular Biology, the Carney Institute for Brain Sciences, the Data Science Institute, and Legoretta Cancer Center.

For this work to continue thriving, Hogan envisions expanding the Department with an eye on meeting the challenges posed by the exponential growth of data technology. He points out that the advent of supercomputers capable of processing massive datasets, data centers so large that they require vast cooling systems, advancements in data measurement techniques such as high-resolution imaging and the use of accelerometers in everyday products represent major milestones.

“The accelerometers we find in an Apple Watch continuously gather a stream of data about our physical activity, all stored within the watch itself,” Hogan said. “This illustrates how far technology has come. A decade ago, it would have been inconceivable for such a small device to have the storage capacity to manage all the data it collects.”

The growth of data technology has transformed the field of biostatistics, leading to methodological advancements and interdisciplinary collaborations. It has also increased the need for versatile skill sets to effectively analyze and derive insights from massive amounts of biological and health-related data, while still dealing with the enduring statistics-related issues of bias and uncertainty. 

“As we look to the future, we are diving into even more complex datasets that depict intricate networks, like brain or gene networks,” Hogan said. “These datasets present a unique set of challenges, given their network structures and inherent complexities. For instance, understanding the impact of social networks on preventive behaviors, such as alcohol intake, healthy eating, and exercise, necessitates taking into account the evolving nature of these networks over time.”

One of the Department’s goals is to expand in a manner that aligns with the escalating demands of the field. This involves staying in step with the fast-paced evolution of data science that includes methods like machine learning, data fusion and artificial intelligence. “We are acutely aware of the challenges this presents,” Hogan said, “but we have full confidence in our ability to confront them head-on.”