All statisticians in academia are constantly confronted with the question of where to publish their papers. Sometimes it’s obvious: A theoretical paper might go to the Annals of Statistics or_JASA Theory & Methods_ or Biometrika. A more “methods-y” paper might go to JASA or JRSS-B or_Biometrics_ or maybe even Biostatistics (where all three of us are or have been associate editors).
But where should the applied papers go? I think this is an increasingly large category of papers being produced by statisticians. These are papers that do not necessarily develop a brand new method or uncover any new theory, but apply statistical methods to an interesting dataset in a not-so-obvious way. Some papers might combine a set of existing methods that have never been combined before in order to solve an important scientific problem.
Well, there are some official applied statistics journals: JASA Applications & Case Studies or JRSS-C or Annals of Applied Statistics. At least they have the word “application” or “applied” in their title. But the question we should be asking is if a paper is published in one of those journals, will it reach the right audience?
What is the audience for an applied stat paper? Perhaps it depends on the subject matter. If the application is biology, then maybe biologists. If it’s an air pollution and health application, maybe environmental epidemiologists. My point is that the key audience is probably not a bunch of other statisticians.
The fundamental conundrum of applied stat papers comes down to this question:If your application of statistical methods is truly addressing an important scientific question, then shouldn’t the scientists in the relevant field want to hear about it? If the answer is yes, then we have two options: Force other scientists to read our applied stat journals, or publish our papers in their journals. There doesn’t seem to be much momentum for the former, but the latter is already being done rather frequently.
Across a variety of fields we see statisticians making direct contributions to science by publishing in non-statistics journals. Some examples are this recent paper in Nature Genetics or a paper I published a few years ago in the Journal of the American Medical Association. I think there are two key features that these papers (and many others like them) have in common:
There was an important scientific question addressed. The first paper investigates variability of methylated regions of the genome and its relation to cancer tissue and the second paper addresses the problem of whether ambient coarse particles have an acute health effect. In both cases, scientists in the respective substantive areas were interested in the problem and so it was natural to publish the “answer” in their journals. The problem was well-suited to be addressed by statisticians. Both papers involved large and complex datasets for which training in data analysis and statistics was important. In the analysis of coarse particles and hospitalizations, we used a national database of air pollution concentrations and obtained health status data from Medicare. Linking these two databases together and conducting the analysis required enormous computational effort and statistical sophistication. While I doubt we were the only people who could have done that analysis, we were very well-positioned to do so.
So when statisticians are confronted by a scientific problems that are both (1) important and (2) well-suited for statisticians, what should we do? My feeling is we should skip the applied statistics journals and bring the message straight to the people who want/need to hear it.
There are two problems that come to mind immediately. First, sometimes the paper ends up being so statistically technical that a scientific journal won’t accept it. And of course, in academia, there is the sticky problem of how do you get promoted in a statistics department when your CV is filled with papers in non-statistics journals. This entry is already long enough so I’ll address these issues in a future post.