Overcoming Asymptotic Inflation in Massive Healthcare Datasets: The Finite-Count Stress Margin

The contemporary biomedical and epidemiological reliance on large-scale observational databases and massive biobanks has fostered an uncritical confidence in statistical power, culminating in a critical vulnerability: asymptotic inflation. While massive sample sizes are assumed to guarantee inferential robustness, applying standard asymptotic estimators (such as Wald-based approximations and Cox proportional hazards models) to inherently sparse, skewed, and rare clinical events may create an unwarranted impression of inferential certainty. In fact, by assuming continuous and symmetric distributions, these asymptotic tools may produce misleadingly precise inference and inadequate confidence-interval coverage, when event counts are sparse, potentially exaggerating the apparent strength of statistical evidence. Consequently, numerous published findings across several clinical disciplines may be sensitive to the inferential framework adopted. To resolve this vulnerability, we contrast asymptotic frameworks with exact distribution-based methods rooted in the Poisson distribution and Garwood's framework. Moving beyond traditional p-value reliance, we introduce a conservative internal stability stress test index which completely decouples from the massive population denominator, thus permitting the evaluation of empirical inferential stability. Synthesizing four distinct clinical case studies, we show how apparent dose-response relationships and highly significant risk differentials may attenuate structural stability when evaluated under Poisson based finite-count constraints. We conclude by suggesting a broader adoption of robustness assessments and exact methods where appropriate for methodological refinement, advocating for distribution auditing, integration of exact confidence intervals, and careful stress testing in observational science.