Study Challenges Covid Vaccine effectiveness Assessments, Cites Statistical Flaws
Florence, Italy – A newly published peer-reviewed study alleges that common statistical practices in evaluating Covid-19 vaccine effectiveness have systematically overestimated benefits and underestimated potential adverse reactions. The research, led by Dr. Panagis Polykretis at the University of Florence, identifies critical distortions in how data is analyzed, perhaps leading to inaccurate public health conclusions.
The study, published by L’Indipendente, focuses on two key methodological issues: the “case counting window” and “immortal time bias.” These biases, the researchers argue, artificially inflate perceptions of vaccine efficacy. The ”case counting window” refers to the limited timeframe after vaccination during which outcomes are tracked, potentially missing later-occurring infections or adverse events. “Immortal time” bias arises from excluding the initial period after vaccination when individuals are most susceptible to infection, creating a cohort that appears healthier than the general population. The study asserts that failing to correct for these distortions “increases artificially the perception of vaccine effectiveness.”
Dr. Polykretis, a biologist specializing in structural biology and neurodegenerative diseases, was the first researcher to hypothesize an autoimmune inflammatory mechanism linked to genetic Covid-19 vaccines. His team’s analysis of real-world mortality data by vaccination status reveals that neglecting these statistical corrections results in an overestimation of vaccine benefits and a corresponding underestimation of associated adverse reactions.
the researchers conclude that all existing studies on vaccine effectiveness ”should be reevaluated” to account for these biases, advocating for transparent and realistic assessments of vaccine safety and efficacy. They emphasize the need for up-to-date and accurate data on individual vaccination status to ensure reliable public health decision-making.
