Inferential Z-Test Validation of Dual Structural Bias in Cancer Risk Assessment within Large COVID-19 Vaccine Cohorts

Abstract:

Background: The need for rigorous pre-analysis inferential validation is critical for studies utilizing large administrative health data, especially following reports suggesting increased cancer risk post-COVID-19 vaccination. This study aims to formally validate a severe external validity discrepancy caused by a dual structural bias present in one such influential cohort.

Methods: We applied two Z-Tests for a single proportion to validate the causal chain of bias: 1) The non-representativeness of the cohort's demographic composition (>= 65 years) against the national gold standard (Root Cause). 2) The non-compatibility of the cancer incidence rate in the non-vaccinated control subgroup (>= 65) against the national rate (External Validity Flaw).

Results: The Z-Test for demographic representativeness yielded a Z-score of - 260.39 (p-value < 10^-50), confirming an important structural deficit in the high-risk age bracket (>= 65). The Z-Test on cancer incidence yielded a Z-score of -15.23 (p-value < 10^-50), formally validating a - 45% structural deficit in the baseline cancer risk of the non-vaccinated group, as anticipated in a preliminary analysis.

Findings: The combined inferential evidence confirms a fatal structural bias in the scrutinized cohort of the examined study. The statistical suppression of the baseline cancer incidence (the denominator) inevitably mathematically inflated the relative Hazard Ratios calculated from the scrutinized cohort. Our work establishes inferential validation against gold standards as a methodological mandate before any complex statistical modeling is applied.

Keywords: Biomathematics, Computational Epidemiology; Inferential Statistics, Selection Bias, COVID-19 vaccination, Cancer Research