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Fella has read the article, has an argument and some stats. What more do you want from a comment? He isn't running a study. Besides, the point is a good one. T

• jstummbillig 2mo ago
  The comment insinuates that the study did not control for age. The study controlled for age. 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.
  • roenxi 2mo ago
    But after the study controls for age the starkness of the effect more or less disappears (eyeballing Fig 1, the 2nd graph). 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.
    • RIMR 2mo ago
      > 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 graphs and all that. Your choice to be dismissively anti-science helps to explain the bad point you're making.
      • bluGill 2mo ago
        > 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/ https://xkcd.com/882/ seems to better fix the data just as well.
        • fc417fc802 2mo ago
          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.
          • bluGill 2mo ago
            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.
            • fc417fc802 2mo ago
              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 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.
  • nylonstrung 2mo ago
    It's getting hard to find any study posted without a kneejerk "correlation does not equal causation" take as if that's not something researchers have to control for in order to even conduct the study in the first place, let alone publish
    • mapt 2mo ago
      The institutional media departments, journalists, and headline writers add back in the insinuated causation and effect size as a routine matter. That way we can't find fault with the researchers for their clickbait study.
    • jmull 2mo ago
      > kneejerk "correlation does not equal causation" 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.
      • Forgeties79 2mo ago
        On hackernews I really don't think it needs to be said. Call me arrogant/too generous to this community, but I think most of the users here know the phrase and its meaning. Seeing 8000 variants of it doesn't contribute, and since it costs nothing to say "I bet they made a mistake" while elevating your own intellect people default to it far too much.
        • jmull 2mo ago
          Read the comment I replied to. It claims the researches must have somehow controlled for correlation (whatever that’s supposed to mean). Which they haven’t. There are a bunch of other comments arguing there’s something beyond correlation shown here. But it hasn’t. The study just demonstrates correlation. 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.
• zarzavat 2mo ago
  A distinction is that Alzheimer's is in an uncommon category of mystery diseases. 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.
  • marcosdumay 2mo ago
    It's worth publication and awareness within people studying Alzheimer. It's absolutely not worth publication with clickbait title and distorted description to the general public.
• afavour 2mo ago
  > Fella has read the article Then not closely enough? The study adjusts for age, which is the core of OP’s criticism.
  • roenxi 2mo ago
    If we read OP's comment closely, it appears to be technically correct. I can imagine someone not upvoting it because it seems to be generic, but there isn't anything wrong or low effort about with it. And I don't see where the stats came from if he hasn't read the article.
  • FrustratedMonky 2mo ago
    "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. 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"
    • compass_copium 2mo ago
      As long as a researcher said "we controlled for confounding variables," there are plenty of people willing to take it at face value. 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.
    • mattkrause 2mo ago
      Statistically. 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.
• stymaar 2mo ago
  > Fella has read the article 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.