Who to test?
Sammie Jia draws my attention to a story in Nature News about drug-testing for atheletes. The story mentions the impressive performance of swimmer Ye Shiwen in the women’s 400m individual medley, and suggests that tests should focus on atheletes who have unusually good performances or strong improvements.
I’m surprised this isn’t already being done. There are at least three good arguments for it. Firstly, if doping works, the rate will be higher among athletes who have improved dramatically, so more cheats will be found with lower testing costs. Secondly, the successes of performance-enhancing drugs are the cases it’s most useful to catch, either if you think the point is deterrence in young atheletes or if you are doing it for some sort of abstract ideal of fair competition. And finally, athletes who make dramatic performance gains honestly deserve to have any doubts removed.
In contrast to many other approaches to targeting tests, this one is very hard to game. The whole point of doping is to get otherwise-impossible improvements in performance, so there isn’t any useful way to avoid attention. Targeting performance improvements would be even more efficient in circumstances where taking and storing samples is cheaper than analyzing them: it would be possible to store a larger collection of samples and retrospectively test performers who had done surprisingly well. That, more or less, is how outcome-dependent sampling is used in medical research: we take blood samples from everyone, but focus the expensive assays on people who, possibly decades later, stand out for good or bad health.
Thomas Lumley (@tslumley) is Professor of Biostatistics at the University of Auckland. His research interests include semiparametric models, survey sampling, statistical computing, foundations of statistics, and whatever methodological problems his medical collaborators come up with. He also blogs at Biased and Inefficient See all posts by Thomas Lumley »
Moreorless (http://www.bbc.co.uk/programmes/b006qshd, https://www.statschat.org.nz/2012/07/14/bbc-radio-equivalent-of-statschat/) this week looks at Ye Shiwen and finds no “statistical smoking gun.”
12 years ago
Good to know. The point of the Nature News piece doesn’t actually rely on anything about Ms Ye, but I’m not surprised if the data have been overinterpreted.
12 years ago