Besides, the point is a good one. There are a lot of professions. If we glance at the underlying study [0] Figure 1 suggests that we'd probably expect to see an outlier or two in that mess just through chance, we're expecting a few p<0.01. Taxi drivers doing unusually well doesn't seem especially impressive in that light, especially since it is one of the professions that seem to die off rather young and we might expect one of those professions to do rather well on Alzheimers.
It's not terrible to not make a good point, but it's not a good point. The not good points should not be ranked highly.
It turns out that there is a profession with a lowest risk of getting Alzheimer's (duh, one had to exist) and it looks like a slight outlier (it's the lowest, we'd expect that one to look like a slight outlier). As outliers go it isn't that impressive, it looks like it is part of a fairly boring distribution. We just plotted 400 data points on the graph, the 1/400 chance weird data point is going to look a little weird. We're actually just reading a lot into the fact that the 2nd data point in ambulance drivers which isn't all that convincing.
Not to knock the study, I'm sure it is a great study. Lots of science done. They do good graphs and all that. But the study appears to be telling me that Alzheimer's is random with respect to occupation and ideally don't spend so much time on the road if you want to live to see old age.
Adjusting for age, Alzheimers deaths were 1/100 for taxi drivers, and 1/60 for the general public. That's extremely significant. However you feel about the graphs means nothing compared to the actual numbers presented here.
> I'm sure it is a great study. Lots of science done. They do good graphs and all that.
Your choice to be dismissively anti-science helps to explain the bad point you're making.
What makes you think that is significant at all? Anytime you take a lot of small subsets of a large random sample you expect to find outliers that look a lot like this.
That is https://xkcd.com/882/ seems to better fix the data just as well.
To be clear I'm not claiming in one direction or the other. I feel that if you're going to attempt to discredit a piece of work it's on you to do such a basic check.
Really the whole point of that xkcd was to viscerally illustrate the practical effect of binary misclassification, or alternatively to make the point that a p of 0.05 isn't necessarily as rigorous as you might expect. Neither of those things directly applies here.
In the study they seem put the P value of the taxi driver outcome at <0.01 (in Table 2, although I admit I haven't read the thing especially closely). For 400 data points, we expect there to be around 4 values with a P value less than <0.01.
[0] https://en.wikipedia.org/wiki/68%E2%80%9395%E2%80%9399.7_rul...
They aren't being anti-science and it's low-quality to suggest they are.
The article (and apparently the researchers too) advances the idea there's a causal link here, even though one is not shown by the research. They go so far as to generally prescribe spatial reasoning training as a way to reduce Alzheimers risk.
You might be tired of hearing "correlation does not equal causation", but it needs to be said here.
The misunderstanding here is widespread so it worth calling out.
It’s actually kind of funny that people are more sick of the mistake being pointed out over and over than of the mistake being made over and over.
If a study found people who swim regularly are are less likely to get heart disease, that wouldn't be very surprising because know a lot about why people get heart disease. Therefore it would be valid to insist on more rigour for accepting (swimming => less heart disease) because the only use of that information is for health advice.
However even weak statistical correlations can be a valid lead for further research into Alzheimer's disease if they suggest where to look. You don't need to prove that signposts are correct in order to use them.
It's absolutely not worth publication with clickbait title and distorted description to the general public.
Then not closely enough? The study adjusts for age, which is the core of OP’s criticism.
maybe it would help this thread to have someone explain exactly how they control for age, if everyone in the range they want to study, is already dead.
What is the actual process to control for age? This comes up a lot, and the only response is "oh, well, we controlled for that", well exactly how?
Seriously, I'm curious.
//lot of downvotes, not a lot of anybody actually able to explain it.
From Article, the age question does seem sketchy.
"Although proportional mortality analyses do not provide information about the population at risk, with careful selection of controls and risk adjustment for factors that may affect competing risks (eg, age, sex, and social class), these values can still serve as a useful indicator of variations in disease frequency across different occupations.16 17"
It's really difficult (maybe not possible) to do that in these large retrospective studies. Shingles vaccine preventing dementia, GLP-1 drugs preventing just about everything, etc, etc. There are plenty of good faith critiques of this kind of work from actual medical researchers making essentially the same point as the OP.
You build a model that describes how likely people are to get diagnosed with Alzhimer's as a function of sex: maybe 12 women out of 100 get it, while 8/100 men are diagnosed. You can do the same thing for age: almost nobody is diagnosed before 30, it's very rare before 40, and sadly common (~1 in 10) after 65 years of age. There are all sorts of mathematical tricks to include multiple variables, account for the fact that you can only be diagnosed once, or that data is "censored" (i.e., missing) at some ages because people have already died.
Based on that, you can then ask if the prevalence of Alzheimer's Disease among cab drivers is surprising, given their demographics. For example, if we know that they skew male and younger, we'd expect that number to be a bit lower than a naive estimate of 10%. In fact, we can calculate that number and then see if it's unexpected given the number we actually see. In practice, you'd actually do this by fitting two models: one containing job and one that doesn't, and see which one best describes the data and how, specifically, the job factor affects the outcome.
However, how well these adjustments work depends on the quality of your data and your modelling. Your model could be missing important factors or have the wrong structure: (e.g., you assume risk is directly proportional to age, but it actually increases more rapidly as you get older). Your data could have problems too: maybe women are more likely to go to the doctor (and thus get diagnosed), even if the actual prevalence is the same.
Their argument doesn't show that at all though, they make an argument against an age bias when the article explicitly says age is being controlled for.
> Figure 1 suggests that we'd probably expect to see an outlier or two in that mess just through chance, we're expecting a few p<0.01.
That's a valid point, and a much better criticism than the one I'm calling out. That doesn't redeem the original comment though.
I'm also a little concerned that the BMJ plot of risk adjusted mortality still shows a clear positive correlation with age at death, which I strongly suspect would turn out to be statistically significant. The adjustment does not appear to be correct, therefore, and while that might still preserve these odd findings, it's definitely not ideal.
The much better occupation, from the graph, is the third outlier -- mining and geological engineers. Life expectancy: 80.3, risk-adjusted mortality: 1.1%. Or, alternatively, economists are close too (80.6; 1.19%).
Aircraft pilots, btw, really were the opposite end of the scale (78.1; 2.3%), surprising enough to be noteworthy beyond a control (the BMJ article does discuss this a little, but seriously -- that's very surprising for a highly-educated profession).
Plausible scenario. Individuals predisposed to Alzheimer’s experience different mental sharpness from birth and this makes them enjoy taxi driving less (more intense than long-haul trucking), and so they pursue taxi driving as a career at lower rates. Under this scenario, driving has no effect, it just induces a selection bias.
Being mentally active (or physically active?) IIRC makes actual plaques both less likely, but also less detectable - a taxi driver can forget 90% of what they know and still not get diagnosed because they don't get lost on the way to the shops. Also they are often masters of small talk.
I would suspect this would eliminate a lot of the experiments participants, as only people who go for regular cognitive check-ups might be eligible?
I cannot imagine there would be many people left who'd be eligible. I can only think of 1 person that does it.. and he obviously does it for the pure satisfaction of acing those cognitive tests, and not because he is showing worrying amounts of decline in his mental faculties.
Every top-level thread is like a multiple choice armchair takedown.
Here's an interesting counterpoint. "Unthinkable" presented studies of navigation tasks in 2D/3D and testing/training. Hippocampus measurements correlated with skill and training. Interestingly, this skill and training and measurements all correlated with beneficial responses in a disaster (as in, not panicking). Weird right?
For example, the other day I wanted to figure out what’s the current state about whether gender equality causes measurable benefits for companies… and the studies are terrible. All of them. Most of them was about Norway, and almost all of them had a reference point in 2008, one of the largest economic crisis, and somehow most of them was even worse than this. They openly distorted statistics. Depending on what they wanted to achieve to one way or another. Even the most cited ones. It’s disgusting. The best ones could prove only that inequality is not inherent of economics, but social. But the agenda was different for them too, so they tried to lie something bigger, all the time, while this would be more than enough to support it.