Why biostatistics at Brown was the best thing for me

Each January and February, hundreds of future statistics and biostatistics PhD students are pawns in what can be a nasty game of application roulette, which roughly consists of the following steps

1) Apply to a dozen schools, and shell out close to $1000 to do so. You don’t get this money back.

2) Wait. Mostly do this.

3) Interview at a recruitment event, which consists of several mini interviews with faculty, most of whom you’ve never heard of and who talk you about research you’ve mostly never heard of. Meanwhile, they ask you about your research plans that you’re mostly making up. No matter what the school or the circumstance, these days and meetings will undoubtedly be awkward.

4) Wait. Continue to do this:

5) Receive some offer. This first offer is almost never the one you want it to be.

6) Wait some more, while fielding phone calls from the first places that offered you, because they want to know if you’ll fill one of their spots.

7) Repeat steps (4), (5) and (6).

8) Pick a school. Hope for the best.

The above 8 steps led be to Brown’s Department of Biostatistics. Here, I’m going to tell you why this was a great decision.

First, it’s a small program. Like two, three, or four PhD students a year small. The downside of a small program is seemingly a restricted selection of classes. Brown does its best to overcome this, and offers more than a dozen different courses on a rotating basis. Further, I never felt isolated, because the school’s other departments, like Applied Math of Computer Science, offer so many great classes that biostatistics students can enroll in.

The benefits of a small program, however, are huge. I know every biostatistics faculty member, what they research, and, in many cases, why they chose to come to Brown and why they love statistics. They each present their research to the students during each calendar year, which helps young students (Masters or PhD) pick research topics. This is critical, and it motivates me to do strong work.

Second, the department is young, and growing. When I chose Brown, a well-respected faculty member with ties to local statistics programs cited the school for having a “critical mass” behind its research. I didn’t know what that meant at the time, but I do now.

Dr’s Gutman, Liu, and Luo were all first-author writers for JASA, the top journal in our field, just last year. Dr. Bauer received a teaching fellowship from the Sheridan Center, and Dr. Schmid was chair of the Health Policy Statistics Section of the American Statistics Association. In my unofficial run-through of the top causal inference paper of the 2000’s, Dr. Hogan‘s paper on drop-out in longitudinal studies was among the most cited causal papers I could find.  I’m leaving several people out here – not intentionally – so feel free to explore the faculty listing here, too.

Since Brown’s a small program, those names aren’t just figureheads behind a desk or on the cover of a book – they’re your teachers, advisers, and, if you are lucky enough, your recommendation writers. They’ll lead your working groups, and tell you how to frame your talks and your papers. Eventually, they’ll ask for your advice.

Third, the program is part of the official School of Public Health. I love that my research is motivated by Public Health data, and that this data is relevant to policy makers and doctors. When I was purely a statistics student, I knew the data would motivate the method, but I didn’t realize it would motivate the person devising the method as well. It’s been pretty cool to work with cancer data, HIV data, and nutrition data, all within the last two years. Even better? All of those projects turned into papers which have, or will be, published.

Further, as part of the Public Health program, we’re constantly interacting with researchers in Epidemiology and Health Policy. This isn’t a choice – its required, and it happens daily in classes, working groups, or at lunch. Not that it means much, but our building (121 S. Main St.) is pretty great, too, and the computing and library resources of a school like Brown are top notch, too.

I think I’ve rambled on long enough, but hopefully this helps at least one future statistics or biostatistics student with their decisions. If anyone who comes across this has any questions about Brown’s programs, either the PhD or Masters, please feel free to email me and I can tell you more.

I remember my application process from four years ago like it was yesterday. And, worse yet, I’m probably still paying off all of those application fees!


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