Reporting Odds Ratio (ROR)

ROR is calculated from a 2x2 contingency table that cross-classifies reports by drug and event presence:

• a: Reports with the target drug AND target adverse event
• b: Reports with the target drug WITHOUT the target event
• c: Reports WITHOUT the target drug WITH the target event
• d: Reports WITHOUT the target drug AND WITHOUT the target event

The formula is: ROR = (a / b) / (c / d) = (a × d) / (b × c). This represents the odds of the event occurring with the drug relative to the odds of it occurring without the drug.

The precision of an ROR estimate is quantified using a 95% confidence interval (CI). The standard error of the natural logarithm of ROR is:

SE(ln ROR) = √(1/a + 1/b + 1/c + 1/d)

The 95% CI is then: exp(ln ROR ± 1.96 × SE)

A signal of disproportionate reporting (SDR) is typically defined when the lower bound of the 95% CI exceeds 1.0, indicating a statistically significant elevation above the null value.

ROR is highly sensitive to sparse data. When any cell in the 2x2 table contains a very low count (especially zero), the ROR can become unstable or undefined:

• A single report in cell a with zero reports in cell c produces an infinite ROR
• Small fluctuations in low-count cells cause dramatic swings in the point estimate
• This volatility generates false-positive signals that waste reviewer resources

For this reason, many pharmacovigilance systems apply a minimum cell count threshold (e.g., n ≥ 3) before calculating ROR.

Unlike Bayesian approaches such as the Empirical Bayes Geometric Mean (EBGM), ROR does not apply shrinkage to adjust for sampling variability:

• ROR: Unshrunk, frequentist estimate; high sensitivity but low specificity with sparse data
• EBGM: Shrinks estimates toward the null based on the prior distribution of all drug-event pairs
• PRR: Similar to ROR but uses proportions instead of odds; shares the same small-cell instability

ROR is often used as a screening tool alongside Bayesian methods, with EBGM providing a more conservative, confirmatory estimate.

ROR calculations can be distorted by confounding by indication and masking:

• Confounding by indication: The underlying disease, not the drug, causes the event. For example, a high ROR for insulin and hypoglycemia reflects the diabetic condition, not a safety signal.
• Masking: A drug with an extremely strong, known association (e.g., warfarin and hemorrhage) inflates the background rate, obscuring signals for other drugs.

Stratification by age, sex, and concomitant medications can partially mitigate these biases.

ROR can be calculated within stratified subgroups to identify effect modification:

• Age-stratified ROR: Detects signals specific to pediatric or geriatric populations
• Sex-stratified ROR: Identifies differential risk profiles between males and females
• Reporter-stratified ROR: Compares signals from healthcare professionals versus consumer reports

Subgroup analysis increases the granularity of signal detection but reduces cell counts, exacerbating small-cell bias. Interaction terms in logistic regression models offer an alternative approach.

A statistical technique 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. This directly addresses a key limitation of the ROR.

• Mechanism: Applies a prior distribution that pulls extreme estimates toward the mean
• Benefit: Stabilizes ROR estimates when cell counts are < 5
• Implementation: Used in EBGM and Information Component (IC) calculations
• Trade-off: May mask true signals with very sparse data

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 confound can inflate ROR values and generate spurious safety signals.

• Example: Higher rates of myocardial infarction with diabetes drugs may reflect the disease, not the medication
• Mitigation: Stratified analysis, restriction to specific indications, or active comparator designs
• Impact on ROR: Can produce elevated disproportionality scores that fail causality assessment