Anupam “Bapu” Jena, the Ruth L. Newhouse Associate Professor of Health Care Policy at Harvard Medical School, analyzes health care issues ranging from physician behavior to prescription drug abuse to the economics of medical innovation—at times challenging what is thought to be “common knowledge.”
In a conversation with the Harvard Medical Labcast, Jena revealed what goes on in his head when he finds, investigates and sometimes abandons research questions. Read the excerpted Q&A below or listen to the full podcast here.
HARVARD MEDICAL LABCAST: How do you choose a question to investigate?
JENA: Well, I have a dartboard and I throw darts and I see what sticks.
No, there are a few criteria. The general rule of thumb I have for a question is not whether a physician or health policy researcher would be interested in knowing the answer—I want to answer questions that everybody would be interested in.
The second criterion is that there has to be detailed enough data that allow you to answer the question in a rigorous way. And the third is actually sometimes the most challenging: There has to be a credible way of trying to answer the question.
What I prefer to do, and what many economists try to do, is find some source of natural randomization or variation in the real world—the term that we sometimes use is “natural experiment”—to try to uncover some of these relationships that might be otherwise difficult to analyze.
HML: It must be tricky to figure out what is going on without being able to control all the variables. You’re making deductions and assumptions, but how do you then check whether those are correct?
JENA: Yeah, that’s tough. In many observational studies, the treatment and control groups look clearly different. Through various types of analyses, those differences are accounted for, but I would argue that they are rarely fully accounted for, and what really should be the gold standard in this type of research is the gold standard in randomized controlled trials: You have to show that there is evidence of randomization if you want to uncover real cause-and-effect relationships in the data as opposed to just associations.
HML: How did you gain the skills to do what you do?
JENA: One, I think a lot of it is luck. Two, my background and my ongoing experiences help a lot. I have a PhD in economics, which is extraordinarily helpful for thinking about these sorts of questions, because economics, among many other things, really prides itself on being able to look at study design very rigorously, and try to understand what causes what, as opposed to saying what is associated with something else.
And as important as that is, the fact that I’m able to work clinically helps a lot because it helps me come up with questions, improve study designs and understand what types of outcomes we should be looking at.
Then the third thing, which is something I’ve learned over the last couple of years, is that the process by which one comes up with ideas can be active.
HML: How do you mean?
JENA: When you take a class in college—let’s say it’s a math or physics class—your assignment may be what’s called a problem set—a set of problems that you have to turn in at the end of the week. And you can get very good at learning how to solve problems. That’s a different set of skills from learning how to come up with problems.
It seems to me that the same kind of systematic approach that one would use to learn material and to answer questions could be used to come up with questions. So one thing that I try to do with my graduate students and medical students is to say, “Okay, let’s come up with five ideas every three or four days,” and then, from among those that seem even remotely interesting, talk through how you’d actually carry out that project.
I think that that’s extraordinarily useful, because it forces you to learn how to think creatively.
HML: What’s the weirdest place you’ve picked up an idea?
JENA: I was at the University of Chicago, I was in my second year of my PhD, and like any good PhD student, I was on the internet. I used to look up articles on Yahoo News, because it just tells you what random people are thinking. There was some article that happened to be talking about Viagra and STDs but wasn’t linking the two. And I wondered, “Is there a question here?”
If you started to look at the CDC data, what you would see is that at that time, STD rates were going up among the elderly, and it was around the same time that drugs like Viagra had been introduced. And so you can put two and two together and understand why those two might be linked. We used a large insurance data set and looked at people who use Viagra, and found that among those people, the rates of STDs went up shortly after use.
And so that was the strangest place I saw something: Yahoo News.
HML: What’s the most challenging part of doing this kind of work?
JENA: I think it’s when you’re really optimistic about a question and it seems like there is promising initial evidence to support what you’re doing, but it doesn’t all fit—and ultimately the fragmentation in the findings is large enough that you can’t come up with a coherent story, and you just have to drop it at that point. That’s a difficult thing to do, not only because you like the idea and have become attached to it, but also because at some point you start to convince yourself that the things that are consistent with the story are more valid than all the things that are inconsistent.
And that’s a challenge I think any researcher has to deal with, because at the end of the day what gets published is the best of what you’ve done. One never sees all the analyses that didn’t work out. But I think the hardest part is letting go.
HML: Do you have any advice for people who want to think about things the way you think about them?
JENA: What I say to the residents—and I’m not that far from residency, so it sounds premature for me to say this, but I’ll say it anyway—is that you see these things happening around you all the time, and it just might not occur to you that there is an interesting research question there. And I would say: Look around you. These things are all there. You just have to train your mind to think like that, and obviously, be interested in those sorts of questions; not everybody is.
This interview was edited for length and clarity.
