Abstract: A current crisis in large-scale epidemiology stems from a methodological regression, prioritizing computational complexity over foundational rigor. Large observational studies using national registries have generated alarming risk signals (e.g., cancer) post-COVID-19 vaccination. Although these findings achieve high internal validity via sophisticated balancing algorithms, their interpretation as biological signals constitutes a critical External Validity failure, violating STROBE Principle 21. The error lies in cohort construction: complexity masked a catastrophic structural flaw in the baseline risk assessment. Specifically, the Non-Vaccinated (NV) cohort suffered from asymmetric selection bias, creating an artificially depressed baseline incidence rate, the Failing Denominator, that inflates risk for the Vaccinated (V) subgroup. Applying Blinder-Oaxaca decomposition, we formally prove the entire observed risk signal is attributable solely to this bias. Correcting this structural bias with the national oncological gold standard entirely neutralizes the spurious signal. We conclude that modern epidemiology must restore rigor by mandating quantitative external validity checks, returning to the foundational descriptive science established by the European pioneers of biostatistics. Failure to reassert this rigor risks plunging European medicine and public health discourse into long-term chaos.
Roccetti, Marco (Wed,) studied this question.
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