Pharmacovigilance Guide

A skill for computational pharmacovigilance research, covering adverse drug event (ADE) databases, signal detection algorithms, disproportionality analysis, and safety surveillance methods used in post-market drug monitoring.

Adverse Event Data Sources

Key Databases

Database	Operator	Coverage	Access
FAERS (FDA Adverse Event Reporting System)	FDA	US spontaneous reports	Free quarterly downloads
EudraVigilance	EMA	European reports	Research access via application
VigiBase	WHO-UMC	Global (150+ countries)	Research license
VAERS	CDC/FDA	US vaccine adverse events	Free download
MAUDE	FDA	Medical device reports	Free download

Loading FAERS Data

import pandas as pd
import zipfile
import os

def load_faers_quarter(data_dir: str, year: int, quarter: int) -> dict:
    """
    Load FAERS quarterly data files into DataFrames.
    Downloads available from: fis.fda.gov/extensions/FPD-QDE-FAERS/FPD-QDE-FAERS.html
    Returns dict of DataFrames for each file type.
    """
    prefix = f"faers_ascii_{year}Q{quarter}"
    tables = {}

    file_map = {
        "DEMO": "demographics",    # Patient demographics
        "DRUG": "drugs",            # Drug information
        "REAC": "reactions",        # Adverse reactions (MedDRA terms)
        "OUTC": "outcomes",         # Patient outcomes
        "INDI": "indications",     # Drug indications
        "THER": "therapy",          # Therapy dates
        "RPSR": "report_sources",   # Report source
    }

    for suffix, name in file_map.items():
        filepath = os.path.join(data_dir, f"{suffix}{year}Q{quarter}.txt")
        if os.path.exists(filepath):
            tables[name] = pd.read_csv(
                filepath, sep="$", encoding="latin-1",
                low_memory=False, on_error="warn"
            )

    return tables

# Example: Load and inspect
faers = load_faers_quarter("./faers_data", 2024, 3)
print(f"Reports: {len(faers['demographics']):,}")
print(f"Drug-reaction pairs: {len(faers['reactions']):,}")

Signal Detection Methods

Disproportionality Analysis

Disproportionality measures compare the observed frequency of a drug-event pair against the expected frequency under independence:

import numpy as np
from scipy.stats import chi2

def compute_disproportionality(a: int, b: int, c: int, d: int) -> dict:
    """
    Compute disproportionality measures from a 2x2 contingency table:

              Event+    Event-
    Drug+       a          b
    Drug-       c          d

    a: reports with both the drug and the event
    b: reports with the drug but not the event
    c: reports with the event but not the drug
    d: reports with neither
    """
    n = a + b + c + d
    expected = (a + b) * (a + c) / n if n > 0 else 0

    # Reporting Odds Ratio (ROR)
    ror = (a * d) / (b * c) if b * c > 0 else float("inf")
    ln_ror = np.log(ror) if ror > 0 and ror != float("inf") else 0
    se_ln_ror = np.sqrt(1/a + 1/b + 1/c + 1/d) if min(a, b, c, d) > 0 else float("inf")
    ror_lower = np.exp(ln_ror - 1.96 * se_ln_ror)

    # Proportional Reporting Ratio (PRR)
    prr = (a / (a + b)) / (c / (c + d)) if (a + b) > 0 and (c + d) > 0 else 0

    # Information Component (IC, Bayesian shrinkage)
    ic = np.log2((a + 0.5) / (expected + 0.5)) if expected > 0 else 0

    # Chi-squared with Yates correction
    chi2_val = (n * (abs(a * d - b * c) - n / 2) ** 2) / (
        (a + b) * (c + d) * (a + c) * (b + d)
    ) if min(a + b, c + d, a + c, b + d) > 0 else 0

    return {
        "a": a, "b": b, "c": c, "d": d,
        "expected": round(expected, 2),
        "ROR": round(ror, 3),
        "ROR_lower_95": round(ror_lower, 3),
        "PRR": round(prr, 3),
        "IC": round(ic, 3),
        "chi2": round(chi2_val, 3),
        "signal": ror_lower > 1 and a >= 3 and chi2_val > 3.84,
    }

Multi-Item Gamma Poisson Shrinker (MGPS)

The MGPS method (used by FDA) applies empirical Bayesian shrinkage to stabilize estimates for rare events:

def empirical_bayes_geometric_mean(observed: np.ndarray,
                                     expected: np.ndarray) -> np.ndarray:
    """
    Simplified EBGM computation.
    Shrinks observed/expected ratios toward the overall mean,
    reducing false positives from small counts.
    """
    # Raw ratio
    rr = observed / np.maximum(expected, 0.01)

    # Empirical Bayes shrinkage (simplified two-component mixture)
    # Full implementation uses EM algorithm to fit mixture of gammas
    global_mean = np.mean(rr)
    shrinkage = expected / (expected + 1)  # more shrinkage for small expected
    ebgm = shrinkage * rr + (1 - shrinkage) * global_mean

    return ebgm

MedDRA Terminology

Medical Dictionary for Regulatory Activities

MedDRA provides the standardized terminology for adverse event coding:

Hierarchy (5 levels):
  System Organ Class (SOC)      -- e.g., "Cardiac disorders"
    High Level Group Term (HLGT) -- e.g., "Cardiac arrhythmias"
      High Level Term (HLT)      -- e.g., "Supraventricular tachyarrhythmias"
        Preferred Term (PT)      -- e.g., "Atrial fibrillation"
          Lowest Level Term (LLT) -- e.g., "Auricular fibrillation"

Standardized MedDRA Queries (SMQs)

Pre-defined search strategies for known safety topics:

• Anaphylactic reaction (SMQ): Broad and narrow search terms
• Drug-induced liver injury (SMQ): Hy's Law criteria
• Torsade de pointes / QT prolongation (SMQ): Cardiac safety signals
• Rhabdomyolysis (SMQ): Muscle-related adverse events

Temporal Pattern Analysis

Time-to-Onset Analysis

def time_to_onset_analysis(drug_start_dates: pd.Series,
                            event_dates: pd.Series) -> dict:
    """
    Analyze time-to-onset distribution for a drug-event pair.
    Useful for distinguishing causal signals from coincidental reports.
    """
    ttp = (event_dates - drug_start_dates).dt.days
    ttp = ttp[ttp >= 0]  # exclude negative (data quality issue)

    return {
        "n_reports": len(ttp),
        "median_days": ttp.median(),
        "mean_days": ttp.mean(),
        "q25_days": ttp.quantile(0.25),
        "q75_days": ttp.quantile(0.75),
        "within_30_days_pct": (ttp <= 30).mean() * 100,
        "within_90_days_pct": (ttp <= 90).mean() * 100,
    }

Causality Assessment

Standard frameworks for evaluating whether a drug caused an adverse event:

Method	Type	Key Criteria
WHO-UMC	Algorithmic	Temporal, dechallenge, rechallenge, alternative causes
Naranjo Score	Scoring scale	10 questions, score 0-13 (definite/probable/possible/doubtful)
Bradford Hill	Principles	Strength, consistency, specificity, temporality, biological gradient

Tools and Resources

• openFDA API: Direct access to FAERS data via REST
• OHDSI / OMOP CDM: Standardized observational health data for pharmacoepidemiology
• PhViD (R package): Pharmacovigilance signal detection methods
• EHRtemporalVariability: R package for temporal data quality in EHR
• VigiRank: WHO-UMC signal prioritization algorithm
• AEOLUS: Standardized and cleaned version of FAERS data