Faculty Spotlight: Scott Zeger, PhD

Scott Zeger, PhD, MS, has been a part of the Bloomberg School of Public Health for 42 years. After receiving his undergraduate degree in mathematics and biology from University of Pennsylvania, he worked for four years as a field biologist on a boat in the Chesapeake and Delaware Bays, before receiving his PhD in statistics from Princeton University.

Scott is the John C. Malone Professor in the Department of Biostatistics. He was the inaugural director and is current co-director of Johns Hopkins inHealth, a Universitywide collaboration to use medical data more intelligently to improve health outcomes and lower costs.

Describe your work in one sentence.

I develop methods that quantify the evidence in data relevant to answering questions about human health.

What are your research interests?

I'm interested in how nature changes through time. I'm a specialist in methods for following people over time and coming to understand the mechanisms that give rise to their changing health. 

What are you focused on currently?

About 10 years ago I expanded my interests as a result of a personal experience with how the American medical system treats older people who are near the end of their lives. I observed discontinuity of care and the influence of how medicine is financed on what care is provided to people. 

Through that experience, I got interested in whether we can use evidence that comes from clinical research, or even from ordinary clinical care, to help doctors make better, less biased decisions about what care to provide older patients. This interest got me involved in a project called Johns Hopkins inHealth. At inHealth we are changing the kind of information that doctors have available to them when they speak with their patients to make decisions about care; especially important decisions that get made near end of life, or when a major disease needs to be managed.

What impact do you hope your research will have? 

My hope is to change how information is used in healthcare. When a patient and doctor make a critical decision, the data they reply upon is summarized and displayed in a way that improves the decision. The improvement will come from having a more valid assessment of the risks and benefits of the choices at hand and reducing the influence of financial incentives or the institution’s preferences.

The people make Hopkins special. 

What do you like best about working at Bloomberg? 

The people make Hopkins special. It’s not just that they're lovely people, but they're very collaborative and open to each other. We have lots of professional and social engagements, both among the faculty, students, and staff. The quality of the people and their willingness to collaborate together, in and out of work, has made the experience particularly rich.

I really knew nothing about public health when I joined the Biostatistics Department 42 years ago. I didn't know that it was in a school of public health, nor did I know what public health was, but I must say, the accident of arriving in a school of public health and making it a career has been one of the real delights of my life.

What is your role in the educational programs in the Department?

My role as an educator takes a few different forms. First, I have four PhD students and a few master's students that I mentor. Additionally, many of our students are working on clinics or research studies where I'm involved as a colleague, so I get the chance to help them learn though their work on those applied projects.

Over the years I have helped to design several of our PhD and master’s courses. While doing less classroom teaching, I continue to be involved with the courses that are about longitudinal data.

What would you say to a person considering a career in biostatistics?

I think biostatistics is an essential set of skills, maybe more important than it's ever been. We're in a transition time; we've digitized all the data that gets generated in public health and medicine, but we've not really figured out how to learn from it. 

There are two competing, and complementary, approaches at the moment: One is just to take all of that data and all of the notes and use a large language model and try to find what truth we can. The other is to design scientific studies that use the data to figure out how to optimally treat individual patients by learning which treatments are best for particular subsets of patients. Both approaches will require biostatistical expertise, so it's a very exciting time to get beyond just digitizing the information but actually learning from it and then turning what we learn back into better public health and medical practice.

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