Inferential Z-Test Validation of Dual Structural Bias in Cancer Risk Assessment within Large COVID-19 Vaccine Cohorts
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
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2025
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| _version_ | 1866902060716261376 |
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| author | ROCCETTI, MARCO |
| author_facet | ROCCETTI, MARCO |
| contents | <p><strong><span lang="EN-US">Abstract:</span></strong></p> <p><strong><span lang="EN-US">Background: </span></strong><span lang="EN-US">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.</span></p> <p><strong><span lang="EN-US">Methods: </span></strong><span lang="EN-US"><span lang="EN-US">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)</span>.</span></p> <p><strong><span lang="EN-US">Results: </span></strong><span lang="EN-US">The Z-Test for demographic representativeness yielded a Z-score of - 260.39 (<em>p-value</em> < 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 (<em>p-value</em> < 10^50), formally validating a - 45% structural deficit in the baseline cancer risk of the non-vaccinated group, as anticipated in a preliminary analysis.</span></p> <p><strong><span lang="EN-US">Findings: </span></strong><span lang="EN-US">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 <span lang="EN-US">calculated from the scrutinized cohort</span>. Our work establishes inferential validation against gold standards as a methodological mandate before any complex statistical modeling is applied.</span></p> <p><strong><span lang="EN-US">Keywords: </span></strong>Biomathematics, Computational Epidemiology; Inferential Statistics, Selection Bias, COVID-19 vaccination, Cancer Research</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17672554 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Inferential Z-Test Validation of Dual Structural Bias in Cancer Risk Assessment within Large COVID-19 Vaccine Cohorts ROCCETTI, MARCO Cancer COVID-19 Biostatistics Epidemiology Applied mathematics <p><strong><span lang="EN-US">Abstract:</span></strong></p> <p><strong><span lang="EN-US">Background: </span></strong><span lang="EN-US">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.</span></p> <p><strong><span lang="EN-US">Methods: </span></strong><span lang="EN-US"><span lang="EN-US">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)</span>.</span></p> <p><strong><span lang="EN-US">Results: </span></strong><span lang="EN-US">The Z-Test for demographic representativeness yielded a Z-score of - 260.39 (<em>p-value</em> < 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 (<em>p-value</em> < 10^50), formally validating a - 45% structural deficit in the baseline cancer risk of the non-vaccinated group, as anticipated in a preliminary analysis.</span></p> <p><strong><span lang="EN-US">Findings: </span></strong><span lang="EN-US">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 <span lang="EN-US">calculated from the scrutinized cohort</span>. Our work establishes inferential validation against gold standards as a methodological mandate before any complex statistical modeling is applied.</span></p> <p><strong><span lang="EN-US">Keywords: </span></strong>Biomathematics, Computational Epidemiology; Inferential Statistics, Selection Bias, COVID-19 vaccination, Cancer Research</p> |
| title | Inferential Z-Test Validation of Dual Structural Bias in Cancer Risk Assessment within Large COVID-19 Vaccine Cohorts |
| topic | Cancer COVID-19 Biostatistics Epidemiology Applied mathematics |
| url | https://doi.org/10.5281/zenodo.17672554 |