3 ms·

> Fella has read the article Then not closely enough? The study adjusts for age, which is the core of OP’s criticism.

by afavour 2mo ago

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.

> Fella has read the article Then not closely enough? The study adjusts for age, which is the core of OP’s criticism.

by 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.

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.