COVID-19 Vaccine Efficacy Grossly Overestimated from Non-Randomized Studies
TrialSitenews - Peter A. McCullough March 2023
Refers to: Sources of bias in observational studies of covid-19 vaccine effectiveness
J Eval Clin Pract . 2023 Mar 26. doi: 10.1111/jep.13839
First portion of LESSONS LEARNED (from PDF)
A recent commentary discussed multiple factors that can bias estimates of covid-19 vaccine effectiveness, such as vaccination status mis- classification, testing differences, and disease risk factor confounding.7 Our article complements these observations by providing examples based on actual data sets that quantify how case-counting window bias, age bias, and background infection rate bias can profoundly complicate the analysis of observational studies, shifting covid-19 vaccine effectiveness estimates by an absolute magnitude as high as 50% to 70%. Randomised trials aim to mitigate these biases by virtue of design features, such as randomisation, placebo controls, and blinding. But while randomised trials should offer far superior protection against these biases, premarketing trials left many important questions unstudied, such as the durability of protection, interaction with other countermeasures, and effectiveness in highest-risk and other important subpopulations. Pragmatic, placebocontrolled randomised trials might have addressed some of these limitations, but after manufacturers began unblinding their trials following the emergency use authorisation in December 2020, observational studies are all we have.
Our analysis shows that real-world conditions such as nonrandomised vaccination, crossovers, and trends in background infection rates introduce strong, complex biases into these observational datasets. Our contribution is to size up three important biases, the magnitude of which surprised us and may surprise you. We conclude that “real-world” studies using methodologies popular in early 2021 overstate vaccine effectiveness.
Our finding highlights how difficult it is to conduct high- quality observational studies during a pandemic.
While the current situation leaves much to be desired, several steps can be taken going forward to enhance the quality of observational studies. Greater awareness of these biases could promote more appropriate adjustments in future studies, including using quasi-experimental methods. In addition, journal editors could improve transparency and reproducibility of observational studies by requiring the disclosure of underlying data and code, as well as publishing modelling equations, tables of coefficients, and standard errors.23 Data availability severely restricted our choice of studies to examine, and also prevented us from analysing all three biases simultaneously, among the ones we selected.
As shown in Table 2, we would have needed additional information, such as
- (a) cases from first dose by vaccination status;
- (b) age distribution by vaccination status;
- (c) case rates by vaccination status by age group;
- (d) match rates between vaccinated and unvaccinated groups on key matching variables;
- (e) background infection rate by week of study; and
- (f) case rate by week of study by vaccination status.
Also: virtually no human trials of any kind of vaccines with current COVID strains
208+ VitaminDWiki pages with VACCIN in title
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