I just did provide evidence for it, from a public health agency. You haven't given any justification for why you think that data was wrong. At least majewsky im

I just did provide evidence for it, from a public health agency. You haven't given any justification for why you think that data was wrong. At least majewsky implies a specific problem (albeit I didn't understand what they think it is). That's as good as it's possible to get because academics don't have access to the raw data with the correct definitions, so there can be no peer reviewed evidence. Not that peer review is any good anyway. "that doesn't apply if we are looking at daily data points of cases from the rates of those who contract COVID among vaxxed/unvaxxed population. There is no 'dead time' artefact by that kind of measure. Especially for case counts, then all persons who 'get covid' on a particular day have obviously 'survived' to be in the sampled cohort on that day." It does apply. Those populations are still classified as vaxxed/unvaxxed in a way that creates immortal time bias. Perhaps the explanation I give above isn't clear enough, but any classification system in which someone is assigned to a group some time after the intervention for that group actually started will have this problem. This was true both for the trials but also all government released stats (except it seems, those Alberta graphs which were so rapidly pulled). Again with cases this time: if you take the vaccine then it's not possible for you to be considered infected within the first two weeks after the shot, because if you were, that infection would be allocated to the unvaccinated cohort. This creates an "infection free period" for the vaccinated which would create an appearance of effectiveness even if the vaccine were a placebo. Fenton talks about it in the form of 'delayed reporting', but the underlying issue is more likely to be immortal time. "The distortion fades as quickly as the reporting delay. So the effect is null 1 week after vaccination rates stabilise (by his example of 1 week delay). His own 'real world data' comparison show vaccine effectiveness waning over a much longer periods." They didn't stabilize yet, did they? The vaccination campaign proceeds in waves in which new people are constantly becoming eligible, for the initial shots and then the boosters. His point is that for as long as we're doing this we will see what looks like fast-fading efficacy and we would see this even if the vaccine did nothing. We would also see the impossible data artifacts found in the ONS data, like vaccination increasing the non-vaccinated risk of non-COVID deaths. It's extremely problematic that this problem exists and creates the exact patterns in the data that are being used to drive the campaign forward. "The implication that health authorities 'count as boosted' anyone who received a jab, but then only count COVID cases based on 'received a jab + 2 weeks' makes no sense at all." That's his point. It doesn't make sense but that's what they do. When calculating how many people have been boosted by a given date, they report the total number of booster shots actually administered up to that point, because that's what their total records show. They aren't subtracting the number of people who received it recently. But when classifying the status of someone specific who has been infected/hospitalized/died, they classify people who have received a booster dose within the past 2 weeks as double vaxxed (because, they claim, it doesn't have any effect until 2 weeks later). And then when they sum those specific reports it creates immortal time, just phrased differently. You can see the effects of it in the ONS stats where you see the artifacts Fenton describes.