. Not every data set is going to end up in the NEJM given the sheer magnitude of papers people are trying to publish on COVID.
I love how the implication here is that we're both posting studies which have an equal chance of containing data which can speak to truth, i.e. causation, when in reality only one of them can.
This isn't about prestige, and bringing up Pfizer's phase 3 being in NEJM is a canard. It doesn't matter where your study was published, because fact remains it was, according to their methods, "designed as a
retrospective registry- and population-based
observational cohort study."
Do you understand what these bolded words mean? I'm guessing not, so let me help you.
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"
The goal of much observational research is to establish causal effects and quantify their magnitude in the context of risk factors and their impact on health and social outcomes. To establish whether a specific exposure has a causal effect on an outcome of interest we need to know what would happen if a person were exposed, and what would happen if they were not exposed. If these outcomes differ, then we can conclude that the exposure is causally related to the outcome.
However, individual causal effects cannot be identified with confidence in observational data because we can only observe the outcome that occurred for a certain individual under one possible value of the exposure (Hernan, 2004). In a statistical model using observational data, we can only compare the risk of the outcome in those exposed, to the risk of the outcome in those unexposed (two subsets of the population determined by an individuals’ actual exposure value); however, inferring causation implies a comparison of the risk of the outcome if all individuals were exposed and if all were unexposed (the same population under two different exposure values) (Hernán & Robins,
2020). Inferring population causal effects from observed associations between variables can therefore be viewed as a missing data problem, where several untestable assumptions need to be made regarding bias due to confounding, selection and measurement (Edwards, Cole, & Westreich,
2015).
The findings of observational research can therefore be inconsistent, or consistent but unlikely to reflect true cause and effect relationships
The goal of much observational research is to identify risk factors that have a causal effect on health and social outcomes. However, observational data are subject to biases from confounding, selection and measurement, which can result in an ...
www.ncbi.nlm.nih.gov
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And beyond the inherent weakness of that paper being observational, I'm guessing you understand that the fact that it's a retrospective registry cohort makes its quirkier findings even more suspect? And to top it off - again - it's a freaking non-peer reviewed preprint in medrxiv, which appears to be your favorite kind of study.
It doesn't matter if the paper had 1,000,000 person-years and the tightest confidence intervals in a century-
that type of study can't be used to make the causal inferences which you're making. At best it's a jumping off point for a hypothesis, one which you apparently (and conspiratorially) think every reputable vaccine scientist on planet earth failed to consider, investigate, and then share their findings with the public.