4 ms·

We feed the animals regular chow mixed with a percentage of pure cholesterol (0.5% by mass, if I recall, but I might be wrong). I do not mean a high saturated f

by et2o 8y ago

We feed the animals regular chow mixed with a percentage of pure cholesterol (0.5% by mass, if I recall, but I might be wrong). I do not mean a high saturated fat diet. This is not the introduction of complex "diet" variables as in humans whatsoever. I am quite aware of the differences in various lipids. Feeding animals pure cholesterol is about as close as we can get. There might be studies that have literally infused LDL in animals, I am not exactly sure. It sounds like a huge pain to do experimentally since we would need to obtain vascular access at least each day and risk infection, stress and pain in the animals, etc. We measure animal blood using the same machines used on humans and see primarily increases in total cholesterol and LDL cholesterol following the diet. There is not "one good" study for secondary prevention of statins, there are dozens. There is very good evidence for the effectiveness of dose-response in statins. Again, I don't even know where exactly to start with this body of literature. Look up "high intensity statin therapy." This review for primary prevention, which is probably why it was underpowered in this meta-analysis of 19 trials in primary prevention (where I said the evidence was more equivocal; this study actually shows pretty good evidence for the effectiveness of statins. in 1° prevention). A RR of 0.86 in only 1-6 years is pretty damn good for a chronic disease. Finally, I think you misunderstand statistics when you say that this review (in which there were only three studies which could be used to analyze dose-response) implies there is no dose response. When they analyze dose-response (which is done through increasing statin dosage until the LDL-C levels are low enough), I quote: "RR for cardiovascular mortality, 0.61 [95% CI, 0.37 to 1.02]" Yes, this is underpowered because the CI includes 1 but the point estimate of 0.6 is pretty impressive. This is a poor conclusion by the authors via incorrect application of the NHST framework, and not evidence that there is no dose-response effect. The best evidence is actually pretty strong (RR of 0.6 is big!). I personally doubt it's even that big. Furthermore, RR for composite cardiovascular outcomes, 0.63 [95% CI, 0.53 to 0.76] was very statistically significant and again with a pretty strong signal. So I don't think this paper says what you say it does. You didn't feel the need to address mendelian randomization studies in 500,000 plus people?

AstralStorm 8y ago
I'm not disputing effectiveness of statins one bit. They're effective, though not as hugely effective as we're led to believe by marketing. (15% RRR is probably worthwhile even if we don't know how these work.) I'm disputing that the effect is related to LDL-C. There were no important differences in trials that titrated based on LDL-C or used fixed dose. Compare RR 0.63 vs 0.71 and full error bar overlap. There were also no relevant differences of outcome based on stratification by LDL-C levels. I did not feel the need to address "genetic"/"metabolomic" studies. (Also known as data sifting.) Standard MR is essentially n-D crosscorrelation of LDL-C levels with LDLR genetic variant and CHD occurences. We know that beforehand. It doesn't tell us anything of causation nor anything we don't know. The authors like to misuse R as "explains". The stronger MR variant results suggest adiposity causes LDL-C levels. Which we already know, again, does not give us a clear treatment target. The other advanced studies pinpoint specific lipids as the cause of CHD, but cannot say where they are from. And no, they're not quite LDL-C. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4816855/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4816855/ I'm not entirely sure how the authors got the rationale from what they're writing about. The better MR-like methods as mentioned are like stepwise crosscorrelation and median clustering. Not entirely sure how they can actually show causality. When the statistical guesswork method (MR is one kind of) does not match the observations... it is likely that either the method is incorrect or that our treatment is misapplied and reality does not match it. MR suggests RRR 3.5 for all LDL-C lowering drugs. We see 1.15 on statins and none on PCSK9 inhibitors vs CVD mortality. I'd bet on MR being invalid/confused.
- et2o 8y ago
  You misunderstand what mendelian randomization and instrumental variable estimation are. I'm not even sure what the methods you propose are, they're definitely not popular ways to do causal inference in medicine or epidemiology.
  - AstralStorm 8y ago
    Sorry, but I actually understand precisely what mendelian randomization and instrumental variable estimation are. You apply nonlinear least squares estimation to every selected suspected variable which is a bunch of linearization/polynomial approximating cross correlations with normalized by estimated covariances. That is nonlinear IV method. MR is this based on an assumption that genomes are random fields of genes with equal independent probabilities of occurence (known to be false, partly corrected by population statistics which is not enough) giving direct results on marker conditions/correlates but not on endpoints (known to be false, partial attempts to correct for it are made to check for pleiotropy but those checks are weak and linear) which markers are nonlinearly covariate with endpoints (hope you used the right mapping) and that highest magnitudes of the "correlations" say something about causation related to certain endpoints. (not in general) MR shines as a negative method to rule out genetic causation or susceptibility, not positive method to confirm it. It can be used to confirm internal validity of a marker - invariance of it vs genetics. The choice of variables (dimensions) is an educated guess too. The assumptions that are verified is that genetic sequence is not heterogenous, heteroskedastic, there are no hidden linkages etc. The assumptions are not typically met or verified for complex system such as lipid metabolism alone, much less endpoints such as CVD. Polygenetic phenotypes are common and typical, not to say anything about epigenetics. Nothing is said about the choice of the checked variables which is critical, and trying too many gives the problem of multiple measurement reducing power. The strongest assumption is that the variables are not covariant with estimation error. This is not possible to check with MR statistics alone unless you can somehow check those variants experimentally directly. (e.g. via gene splicing or mutation) Good luck with that.
    - et2o 8y ago
      No, I don't think you have a clue. You wrote a completely wrong thing, edited it when I pointed out it was incorrect, and then 4 hours later you read up about MR on wikipedia or something and came back here and posted a partially correct answer with a lot of buzzwords and nice-sounding phrases, but with even more straw-man arguments and outright errors. Peace out, I'm done with you.

by et2o 8y ago

AstralStorm 8y ago

I'm not disputing effectiveness of statins one bit. They're effective, though not as hugely effective as we're led to believe by marketing. (15% RRR is probably worthwhile even if we don't know how these work.) I'm disputing that the effect is related to LDL-C. There were no important differences in trials that titrated based on LDL-C or used fixed dose. Compare RR 0.63 vs 0.71 and full error bar overlap. There were also no relevant differences of outcome based on stratification by LDL-C levels. I did not feel the need to address "genetic"/"metabolomic" studies. (Also known as data sifting.) Standard MR is essentially n-D crosscorrelation of LDL-C levels with LDLR genetic variant and CHD occurences. We know that beforehand. It doesn't tell us anything of causation nor anything we don't know. The authors like to misuse R as "explains". The stronger MR variant results suggest adiposity causes LDL-C levels. Which we already know, again, does not give us a clear treatment target. The other advanced studies pinpoint specific lipids as the cause of CHD, but cannot say where they are from. And no, they're not quite LDL-C. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4816855/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4816855/ I'm not entirely sure how the authors got the rationale from what they're writing about. The better MR-like methods as mentioned are like stepwise crosscorrelation and median clustering. Not entirely sure how they can actually show causality. When the statistical guesswork method (MR is one kind of) does not match the observations... it is likely that either the method is incorrect or that our treatment is misapplied and reality does not match it. MR suggests RRR 3.5 for all LDL-C lowering drugs. We see 1.15 on statins and none on PCSK9 inhibitors vs CVD mortality. I'd bet on MR being invalid/confused.

et2o 8y ago
You misunderstand what mendelian randomization and instrumental variable estimation are. I'm not even sure what the methods you propose are, they're definitely not popular ways to do causal inference in medicine or epidemiology.
- AstralStorm 8y ago
  Sorry, but I actually understand precisely what mendelian randomization and instrumental variable estimation are. You apply nonlinear least squares estimation to every selected suspected variable which is a bunch of linearization/polynomial approximating cross correlations with normalized by estimated covariances. That is nonlinear IV method. MR is this based on an assumption that genomes are random fields of genes with equal independent probabilities of occurence (known to be false, partly corrected by population statistics which is not enough) giving direct results on marker conditions/correlates but not on endpoints (known to be false, partial attempts to correct for it are made to check for pleiotropy but those checks are weak and linear) which markers are nonlinearly covariate with endpoints (hope you used the right mapping) and that highest magnitudes of the "correlations" say something about causation related to certain endpoints. (not in general) MR shines as a negative method to rule out genetic causation or susceptibility, not positive method to confirm it. It can be used to confirm internal validity of a marker - invariance of it vs genetics. The choice of variables (dimensions) is an educated guess too. The assumptions that are verified is that genetic sequence is not heterogenous, heteroskedastic, there are no hidden linkages etc. The assumptions are not typically met or verified for complex system such as lipid metabolism alone, much less endpoints such as CVD. Polygenetic phenotypes are common and typical, not to say anything about epigenetics. Nothing is said about the choice of the checked variables which is critical, and trying too many gives the problem of multiple measurement reducing power. The strongest assumption is that the variables are not covariant with estimation error. This is not possible to check with MR statistics alone unless you can somehow check those variants experimentally directly. (e.g. via gene splicing or mutation) Good luck with that.
  - et2o 8y ago
    No, I don't think you have a clue. You wrote a completely wrong thing, edited it when I pointed out it was incorrect, and then 4 hours later you read up about MR on wikipedia or something and came back here and posted a partially correct answer with a lot of buzzwords and nice-sounding phrases, but with even more straw-man arguments and outright errors. Peace out, I'm done with you.