281–290 of 302 posts
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A dense street network with complex intersections also means living in walkable urban cores vs car-centric suburbs.
idk have you ever driven in boston?
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> But after the study controls for age the starkness of the effect more or less disappears (eyeballing Fig 1, the 2nd graph). 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 gra…
> That's extremely significant. 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.
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> That's extremely significant. 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.
Outliers, sure. Exactly like this though? That depends. Since this is a population then assuming a gaussian (which could well be wrong for a variety of reasons I'm just guessing here) what's sigma? If 1/100 is within ~3 sigma then sure. Otherwise no.
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> Fella has read the article Then not closely enough? The study adjusts for age, which is the core of OP’s criticism.
"study adjusts for age" 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. Fro…
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.
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People jump the gun to show their perceived intellectual superiority. Typically same kind of people are last to admit of a mistake.
It's not necessarily about intellectualism. Science in modern times is plagued by publish or perish. Researchers just need to get things published to keep working, and negative results don't get published. So I think people often publish stuff without proper controls/missing confounding variables/inversion of causality/etc not out of ignorance, but because they know the conclusion is dubious - but if they don't publi…
I doubt OP's jaded response was alluding to your insight, but still thanks for that, it's genuinely good to know.
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Outliers, sure. Exactly like this though? That depends. Since this is a population then assuming a gaussian (which could well be wrong for a variety of reasons I'm just guessing here) what's sigma? If 1/100 is within ~3 sigma then sure. Otherwise no.
It needs more statistical analysis than I have the background to do. However all signs look to another variation of the replication crisis, which is more an more my default assumption when I see an observational study.
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.
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Suction cup compasses are ~ $10 on amazon if it bothers you. Won't be in the mirror though.
You'd also need to mount them facing up, pretty inconvenient when driving
Here's an example https://www.amazon.com/example/dp/B0FZSFWJQR that I'm not endorsing.
Taxi Drivers: The mean age at death is approximately 67.8 years. General Population: in the same dataset, life expectancy averaged 74 years. The average age at which patients are typically diagnosed with Alzheimer's disease is 79. People in these jobs don't live long enough on average to get diagnosed, at the same rate. The same effect will happen in any job that lowers your life expectancy.
It pains me to see such a low-effort contrarian comment at the top: as stated in TFA taxi drivers are below other profession when controlling for age, and there are multiple professions with lower life expectancy that still have significantly higher Alzheimer rate than taxi drivers.
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It needs more statistical analysis than I have the background to do. However all signs look to another variation of the replication crisis, which is more an more my default assumption when I see an observational study.
You're handwaving. The xkcd point only applies if you're within ~3 sigma. This isn't a complicated statistical analysis it's just asking how far out the outlier is versus the standard deviation and size of the dataset. 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…
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Suction cup compasses are ~ $10 on amazon if it bothers you. Won't be in the mirror though.
They have been reality for many years. A compass built into the car didn't exist in the 1990s (it probably did in luxury cars - I never drove them), then moved into mainstream cars, and now the fad is dieing.
My parents had the same model van, but an earlier year; we didn't have the fancy digital compass mirror, just a suction cup compass ball.