Detecting Pv Signals
Computes disproportionality signals, PRR, ROR, EBGM, and IC (BCPNN), over FAERS /...
Install
npx skills add maziyarpanahi/openmed --skill detecting-pv-signals -g -a claude-code -y- Repository
- maziyarpanahi/openmed5.8k
- Category
- Engineering
- Updated
- 4d ago
- License
- Apache-2.0
- Context cost
- Mediumabout 2,076 tokens
- Runs scripts
- No, instructions only
What it does
Computes disproportionality signals, PRR, ROR, EBGM, and IC (BCPNN), over FAERS / OpenFDA drug-event data to flag potential safety signals. Use when the user wants to mine spontaneous-report data for drug-reaction associations, build a 2x2 contingency table, compute a Proportional Reporting Ratio or Reporting Odds Ratio, run Empirical Bayes (EBGM/EB05) or Information Component shrinkage, or screen a drug for over-reported reactions. Trigger keywords: disproportionality, signal detection, PRR, ROR, EBGM, EB05, IC, BCPNN, MGPS, 2x2 table, signal of disproportionate reporting, SDR, OpenFDA, FAERS. Pairs adjacent to OpenMed: aggregate de-identified, coded cases (from reporting-adverse-events) then query the public OpenFDA /drug/event count API to build the contingency table. Reaction terms are MedDRA PTs (licensed, user-supplied).
Detecting pharmacovigilance signals (disproportionality)
Spontaneous-report databases like the FDA's FAERS are mined for signals of disproportionate reporting (SDR): drug-reaction pairs that occur together more than expected given the background of all reports. The core device is a 2x2 contingency table and a disproportionality metric computed from it, PRR, ROR, EBGM, or IC (BCPNN).
You can build the 2x2 table directly from the public, free OpenFDA
/drug/event endpoint (no PHI, no MedDRA license to query; the reaction terms
returned are already MedDRA PTs). This skill is statistical screening: a high
PRR is a hypothesis, not a confirmed adverse drug reaction.
When to use
- You have a drug of interest and want to see which reactions are over-reported.
- You need a PRR / ROR with confidence interval, or an Empirical Bayes EBGM/EB05 / IC025 to control for the small-count noise PRR/ROR suffer from.
- You are building a routine signal-screening run over OpenFDA or your own aggregated case counts.
The 2x2 table
For one drug D and one reaction R, classify every report:
| Reaction R | Not R | |
|---|---|---|
| Drug D | a | b |
| Not D | c | d |
- PRR = [a/(a+b)] / [c/(c+d)]
- ROR = (a·d)/(b·c)
- IC (BCPNN, log2 information component) ≈ log2( a·(a+b+c+d) / ((a+b)·(a+c)) )
- EBGM = Empirical Bayes Geometric Mean, a gamma-Poisson shrinkage of the observed/expected ratio (the MGPS method) that pulls small-count estimates toward 1; report EB05 (the 5th percentile) as the conservative signal.
Common signal thresholds (screening only): PRR ≥ 2 with χ² ≥ 4 and a ≥ 3; ROR lower 95% CI > 1; IC025 > 0; EB05 ≥ 2.
