CardioExchange Archive

Several Cardiology Fellows who are attending this week’s AHA meeting are blogging together on CardioExchange. The Fellows include Susan Cheng, Madhavi Reddy, John Ryan, and Amit Shah. Check back often to learn about the biggest buzz in Chicago this week — whether it’s a poster, a presentation, or the word in the hallways. You can read the preceding post here.

While I was sitting in on an AHA epidemiology session focused on ideal cardiovascular health status, based on what’s now called Life’s Simple 7, somebody mentioned using “principal components analysis.” This method was used to re-categorize a measure of healthy diet — basically because the vast majority of people (Americans) in the study would otherwise be categorized as eating unhealthily, which would render the diet measure useless. As somebody with a bit of formal training in biostatistics, I was familiar with most of the methods mentioned in the session. So I kind of know what principal components analysis is meant to do but only in a very general sense, and I would definitely need a statistician to help me understand how it was applied in this particular study.  I wondered how many other people in the room might also have felt stumped by this part of the methodology.

Having heard a lot of buzz about ROCKET-AF, I later ventured to drop by the plenary session where this trial was being presented. The post-presentation discussion was extremely interesting, but again involved statistical concepts that I didn’t feel completely familiar with, as somebody who isn’t active in clinical trials research. Following Ken Mahaffey’s very polished presentation of the results, Elaine Hylek presented her discussant opinion and focused on the potential pitfalls of non-inferiority trial design. By the time she was done, I was wishing that I could watch the trial presentation again so that I could better scrutinize the methods. Only after brushing up on the differences between non-inferiority and superiority trials (this site is helpful), did I feel that I could revisit the slides online to try to figure out the methodological nuances for myself.

So, I’m wondering if maybe there’s something missing at conferences like AHA. Cutting-edge research often involves not just ongoing advances in cardiovascular science but also ongoing developments in the statistical methods being used —  including risk prediction models (C-statistics, net reclassification index, etc.), genome-wide association analyses, non-inferiority trials, and adaptive trial designs. Could there be a way to help the average conference attendee make better sense of methods in order to better make sense of the results?  If conferences like AHA are to serve as a form of CME, perhaps they should have a greater emphasis on keeping us all up-to-date on how to critically appraise the latest research.  Perhaps more statistics primers scheduled at the beginning of the conference, or each day of the conference, would help?  Or journal-club-like sessions at the end of each conference day?  Or maybe just an online resource that reviews some general concepts and some more advanced ones in a relatively accessible format?

Then again, I could be the only person at AHA hungering for more stats knowledge while wandering through the convention halls.  If so, just let me know…