Someone who wishes to remain anonymous points us to a recent article in the Lancet, “Risk thresholds for alcohol consumption: combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies,” by Angela Wood et al., that’s received a lot of press coverage; for example:
Here’s the key graph from the research paper:
According to one of the news articles, 100 grams of alcohol per week, which according to the graph above is approximately the maximum safe dose, is equivalent to “five standard 175ml glasses of wine or five pints [of beer] a week.”
The press coverage of this study was uncritical and included this summary from our friend David Spiegelhalter who described it as “massive and very impressive”:
The paper estimates a 40-year-old drinking four units a day above the guidelines [the equivalent of drinking three glasses of wine in a night] has roughly two years’ lower life expectancy, which is around a 20th of their remaining life. This works out at about an hour per day. So it’s as if each unit above guidelines is taking, on average, about 15 minutes of life, about the same as a cigarette.
And the statistics
On one hand, I’m always suspicious of headline-grabbing studies. On the other, I respect Spiegelhalter.
I took a look at the research article, and . . . it’s complicated. They need to do a lot with their data. Most obviously, they need to adjust for pre-treatment differences in the groups, differences of age, sex, smoking status, and other variables. They adjust for whether people are current smokers or not, but they don’t seem to adjust for the level of smoking or past smoking status; maybe these data are not available? The researchers also have a complicated series of decisions to make regarding missing data, inclusion of nonlinear terms and interactions in their models, and other statistical details.
It was not clear to me exactly how they got the above graph, and how much the pattern could be distorted by systematic differences between the groups, not caused by alcohol consumption.
That said, you have to do something. And, from a casual look at the paper, the analyses seem serious and the results seem plausible.
The logical next step is for the data and analysis to be shared. Immediately. Put all the data on a spreadsheet on Github so that anyone can do their own analyses. Maybe the data are already publicly available and easily accessible? I don’t know. There are various questionable steps in the published analysis—that’s fine, no analysis is perfect!—and the topic is important enough that it’s time to let a thousand reanalyses bloom.