Interpreting Positive and Inverse Disproportionality Signals in Spontaneous Reporting Systems: A Claim-Strength Framework with Direction-Aware Evaluation

Disproportionality analysis of spontaneous reporting systems is usually used to identify positive signals, meaning drug–event pairs reported more often than expected. The same statistical structure also has a lower-reporting side. A reporting odds ratio (ROR) above unity indicates higher-than-expected reporting, whereas an ROR below unity indicates lower-than-expected reporting. Neither result directly estimates incidence, absolute risk, or causality. Existing critiques of inverse disproportionality signals rightly warn against interpreting ROR < 1 as protection, risk reduction, a beneficial reaction, or therapeutic effect. The same caution applies to ROR > 1. Higher-than-expected reporting is not, by itself, causal evidence of harm. This narrative methodological review argues that positive and inverse disproportionality signals should be interpreted according to the same scientific principles. This does not imply symmetric regulatory action thresholds. In this review, “symmetric scientific standards” means that claims of the same strength require commensurate evidence in both directions; it does not mean identical case-level information, bias mechanisms, detectability, predictive value, regulatory priority, or follow-up procedures. Instead, it calls for interpretation that is consistent with the strength of the claim. To make this approach practical, the review outlines a symmetry check that asks whether critiques of inverse signals also apply to positive signals and whether they address descriptive reporting or causal interpretation. It also proposes a claim-strength framework in which the required evidence depends on the claim being made, not on the direction of the signal. Under this approach, inverse signals may be reported as lower-than-expected reporting. They may also support hypothesis generation or candidate prioritization for follow-up evaluation when robust to comparator and sensitivity analyses. Claims of protection or risk reduction require external validation.