Hi, Chris. I am late to this, but found it and quite liked it. Nice work.
I am struggling a bit with where you think you saw saw immortal person-time errors. There is clearly survivorship bias (people who would have been current smokers with leukemia or cervical cancer often did not live long enough to be included, whereas the vapers with those diseases are likely still alive even if their prognosis is poor). There might also be some reverse causation there (people with cancer at an early age are probably more likely to avoid smoking). But there is no person-time measure, right? So there cannot really be immortal time in the denominator. Survivorship bias is a lot like immortal person-time bias, and they are close to interchangeable in some cases, but this is not really one of those cases.
Also, you let them off too easy on the "controlling for age" thing. It is really almost as bad as the failure to control for former smoking (which, as you say, is the clear central fatal flaw in the analysis). There is no possible way you can make a comparison of age-dominated diseases between a group who average 25yo and groups that measure c.60yo without doing a stratified analysis. Any other method of "controlling" is just going to produce model-driven nonsense.
More crap being portrayed as "science" by the king of junk science. We've all been "Glantzed" again...
Hi, Chris. I am late to this, but found it and quite liked it. Nice work.
I am struggling a bit with where you think you saw saw immortal person-time errors. There is clearly survivorship bias (people who would have been current smokers with leukemia or cervical cancer often did not live long enough to be included, whereas the vapers with those diseases are likely still alive even if their prognosis is poor). There might also be some reverse causation there (people with cancer at an early age are probably more likely to avoid smoking). But there is no person-time measure, right? So there cannot really be immortal time in the denominator. Survivorship bias is a lot like immortal person-time bias, and they are close to interchangeable in some cases, but this is not really one of those cases.
Also, you let them off too easy on the "controlling for age" thing. It is really almost as bad as the failure to control for former smoking (which, as you say, is the clear central fatal flaw in the analysis). There is no possible way you can make a comparison of age-dominated diseases between a group who average 25yo and groups that measure c.60yo without doing a stratified analysis. Any other method of "controlling" is just going to produce model-driven nonsense.