Inferential Inflation in Clinical Epidemiology: Interpreting Evidence Beyond Measurement and Model Limits

Statistical analyses in clinical research often convey a level of interpretive certainty that exceeds the limits imposed by measurement properties, surrogate validation evidence, ordinal scale structure and predictive model calibration. This article develops an epistemologic framework describing how such inferential inflation arises in routine analytical practice.

Conceptual synthesis integrating principles from measurement science, surrogate endpoint validation, ordinal outcome modelling and predictive model evaluation. Illustrative examples and simulations are used to demonstrate how modelling assumptions can shape interpretive boundaries even when analyses are technically correct.

Across these domains, interpretive certainty may be extended beyond empirical support when analytic measurement uncertainty is unrecognised, surrogate endpoints are interpreted outside validated mediation evidence, ordinal scales are treated as interval measures, or predictive models are interpreted without calibration assessment. These practices can produce apparent precision or significance that reflects modelling assumptions rather than underlying biological or clinical reality.

Explicit attention to measurement uncertainty, validation boundaries, scale structure and calibration performance may help prevent inferential overreach in clinical research. The proposed framework and checklist offer practical guidance for aligning statistical interpretation with the epistemic limits of measurement and modelling.