4 ms·

How do they separate cases to get the research? I mean, isn't it more likely that overweight people consume more zero-calorie sweetener... How do they conclude

How do they separate cases to get the research? I mean, isn't it more likely that overweight people consume more zero-calorie sweetener... How do they conclude the increased clot production isn't just a variable of the study groups population? Like, how do they know that weight or lifestyle of the population studied is a lurking variable?

notafraudster 4y ago
The design appears to be based on the notion that most of the confounders are washing out in the propensity to seek cardiac treatment, which was a precursor for recruitment into the study. I agree this doesn't mitigate all confounding concerns. Given the design where patients are measured at baseline and the endline event measure is measured later, I'd like to see a balance table at the baseline measures. One possible pitch: I'm also not sure whether the circulating blood levels of this chemical are actually connected to consumption of the sweetener. You could imagine a world where two individuals who have identical consumption of the sweetener but different free-circulating levels of the chemical, the higher levels of the chemical could be an indication of other confounding health issues causing malabsorption. I am not a biologist or doctor and don't know anything about sweeteners or any of the mechanisms that may or may not play a role here, just commenting on design.
- fartsucker69 4y ago
  >The design appears to be based on the notion that most of the confounders are washing out in the propensity to seek cardiac treatment, which was a precursor for recruitment into the study What a bad assumption for a design. It should be obvious that there is still a relationship between lifestyle health and things like artificial sweetener intake even among that filtered group. Nutritional studies are just all kinds of worthless when you try to use it for making actual decisions. They should be used to guide future and more detailled/well funded research, and that is literally the only thing they should be used for.
hombre_fatal 4y ago
Because multiple colinear variables like that would make the confidence intervals blow up because not every person in the study is obese much less the same degree of obese, and adjusting for colinearity is a basic part of data modeling. Just like how smokers may partake in worse health practices in general, but not every smoker does, and not every smoker is unhealthy to the same degree, thus you can find an effect across all smokers, like O2 deficiency, even a health nut smoker.
zackees 4y ago
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notafraudster 4y ago

The design appears to be based on the notion that most of the confounders are washing out in the propensity to seek cardiac treatment, which was a precursor for recruitment into the study. I agree this doesn't mitigate all confounding concerns. Given the design where patients are measured at baseline and the endline event measure is measured later, I'd like to see a balance table at the baseline measures. One possible pitch: I'm also not sure whether the circulating blood levels of this chemical are actually connected to consumption of the sweetener. You could imagine a world where two individuals who have identical consumption of the sweetener but different free-circulating levels of the chemical, the higher levels of the chemical could be an indication of other confounding health issues causing malabsorption. I am not a biologist or doctor and don't know anything about sweeteners or any of the mechanisms that may or may not play a role here, just commenting on design.

fartsucker69 4y ago
>The design appears to be based on the notion that most of the confounders are washing out in the propensity to seek cardiac treatment, which was a precursor for recruitment into the study What a bad assumption for a design. It should be obvious that there is still a relationship between lifestyle health and things like artificial sweetener intake even among that filtered group. Nutritional studies are just all kinds of worthless when you try to use it for making actual decisions. They should be used to guide future and more detailled/well funded research, and that is literally the only thing they should be used for.

hombre_fatal 4y ago

Because multiple colinear variables like that would make the confidence intervals blow up because not every person in the study is obese much less the same degree of obese, and adjusting for colinearity is a basic part of data modeling. Just like how smokers may partake in worse health practices in general, but not every smoker does, and not every smoker is unhealthy to the same degree, thus you can find an effect across all smokers, like O2 deficiency, even a health nut smoker.

zackees 4y ago

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