Disproportionality Analysis

A frequentist disproportionality measure that calculates the ratio of the observed reporting rate of a specific adverse event for a drug of interest to the expected reporting rate of that event for all other drugs in a database.

• Formula: PRR = [a/(a+b)] / [c/(c+d)] using a 2x2 contingency table
• Threshold: A signal is typically flagged when PRR ≥ 2, chi-squared ≥ 4, and the case count (a) ≥ 3
• Strength: Computationally simple and transparent, making it easy to implement and interpret
• Weakness: Highly volatile and prone to false positives when dealing with low expected counts, as it lacks shrinkage for sparse data
• Origin: Introduced by Evans et al. in 2001 for routine signal detection in the UK Yellow Card database

A frequentist disproportionality statistic representing the odds of a specific adverse event being reported with a particular drug compared to the odds of that event being reported with all other drugs.

• Formula: ROR = (a/c) / (b/d) = ad/bc
• Interpretation: An ROR > 1 indicates a higher odds of reporting for the drug-event pair than for the comparator set
• Advantage: Symmetrical measure that can be modeled using logistic regression, allowing for multivariate adjustment for covariates like age, sex, and reporter type
• Limitation: Like PRR, ROR is sensitive to small cell counts and does not incorporate Bayesian shrinkage, leading to exaggerated scores for rare combinations
• Use Case: Commonly applied in EudraVigilance and FAERS analyses where covariate adjustment is desired

A Bayesian disproportionality score representing the posterior mean of the relative reporting ratio for a drug-event combination, calculated using the Multi-item Gamma Poisson Shrinker (MGPS) algorithm.

• Mechanism: Applies Bayesian shrinkage by modeling the observed count as a mixture of Poisson distributions with Gamma priors, pulling extreme ratios toward the null value when data is sparse
• Output: The EBGM score is the geometric mean of the posterior distribution; an EB05 (5th percentile) > 2 is a common signal threshold
• Key Advantage: Robustly handles zero-count cells and rare events without generating infinite ratios, dramatically reducing false-positive rates
• Deployment: The primary algorithm used by the FDA for mining the FAERS database
• Distinction: Unlike PRR/ROR, EBGM provides a stabilized estimate that reflects both the magnitude of disproportionality and the statistical uncertainty

A Bayesian disproportionality measure developed by the Uppsala Monitoring Centre that quantifies the strength of association between a drug and an adverse event on a logarithmic scale using shrinkage regression.

• Formula: IC = log₂[P(drug, event) / (P(drug) × P(event))]
• Interpretation: An IC of 0 indicates no association; positive values indicate a higher-than-expected reporting frequency
• Threshold: IC₀₂₅ (lower 95% credibility interval) > 0 is the standard signal detection criterion in VigiBase
• Temporal Monitoring: Supports time-scan analysis to detect emerging signals by tracking IC trends over sequential time periods
• Global Standard: The primary metric used by the WHO Programme for International Drug Monitoring for analyzing the world's largest ICSR database

A statistical regularization technique applied in pharmacovigilance data mining that shrinks observed disproportionality scores toward a null value to reduce the risk of flagging false-positive signals from drug-event combinations with very low report counts.

• Core Problem: Frequentist methods like PRR produce volatile, inflated scores when expected cell counts are small (e.g., a single report can generate a PRR of 100+)
• Solution: Bayesian methods (EBGM, IC) use prior distributions to pull extreme ratios back toward the population mean, proportional to the uncertainty in the estimate
• Effect: A drug-event pair with 1 observed and 0.01 expected reports might have a raw PRR of 100 but a shrunken EBGM of 2.5
• Trade-off: Shrinkage reduces sensitivity for very rare true signals in exchange for dramatically improved specificity and reviewer efficiency
• Implementation: The degree of shrinkage is inversely proportional to the report count; as data accumulates, the posterior estimate converges toward the observed ratio

An advanced analytical approach that computes disproportionality metrics within homogeneous subgroups to control for confounding variables such as patient age, gender, reporting country, or concomitant medications.

• Methodology: Applies Mantel-Haenszel adjustment or logistic regression to calculate summary estimates across strata, removing the distorting effect of known confounders
• Example: Calculating ROR separately for geriatric and pediatric populations before pooling to avoid Simpson's paradox, where a signal appears in aggregate but disappears within subgroups
• Application: Essential for addressing confounding by indication, where the underlying disease drives both drug use and adverse event reporting
• Limitation: Requires sufficient data within each stratum; over-stratification can lead to data sparsity and unreliable estimates
• Regulatory Relevance: Increasingly expected by regulators for robust signal validation, particularly for drugs used in heterogeneous patient populations

A frequentist disproportionality measure that compares the observed reporting rate of a specific adverse event for a drug of interest against the expected background rate for all other drugs in the database. A PRR > 2 with a chi-squared statistic > 4 is a common threshold for signal generation.

• Formula: [a/(a+b)] / [c/(c+d)]
• Strength: Simple to calculate and intuitive to interpret.
• Weakness: Highly volatile with low expected cell counts, leading to false positives.

A frequentist statistic representing the odds of a specific adverse event being reported with a particular drug compared to the odds of that event being reported with all other drugs. It is structurally similar to the PRR but uses odds instead of rates.

• Formula: (a/c) / (b/d)
• Advantage: Symmetrical measure suitable for case-control analysis.
• Application: Often used in logistic regression models to adjust for covariates like age and gender.

A Bayesian disproportionality score representing the posterior mean of the relative reporting ratio. It is calculated using the Multi-item Gamma Poisson Shrinker (MGPS) algorithm to adjust for data sparsity by shrinking observed ratios toward a null value.

• Key Metric: The lower bound of the 90% confidence interval (EB05) must be > 2 for a signal.
• Benefit: Robust against false positives from drug-event pairs with very low report counts.

A critical bias in pharmacovigilance where the underlying disease being treated, rather than the drug itself, is the true cause of an observed adverse event. This complicates signal interpretation because the treated population inherently has a higher baseline risk for the event.

• Example: A diabetes drug appearing to cause cardiovascular events because diabetic patients are already at high risk.
• Mitigation: Stratified analysis and multivariate regression to control for the indication.

A statistical technique that shrinks observed disproportionality scores toward a null value (typically 1.0) to reduce the risk of flagging false-positive signals from drug-event combinations with very low report counts. This is a core component of the MGPS algorithm.

• Mechanism: The prior distribution assumes no association; data must overcome this assumption.
• Result: More stable and reliable signal scores for rare events compared to frequentist methods like PRR.