Case Study · Pharmacovigilance

Comparative Pharmacovigilance Analysis of GLP‑1 Therapies Using FAERS Signal Detection

FocusComparative drug-safety signal detection
MethodsFAERS · Reporting Odds Ratios (RORs)
To be presentedAPHA 2026 Annual Meeting (November)
0
GLP-1 therapies compared
FAERS
FDA post-marketing surveillance data
ROR
disproportionality signal detection
Age · Sex
demographic reporting patterns

How disproportionality analysis flags a signal

Reporting Odds Ratio (ROR) with 95% confidence interval
0.51.02.04.08.0
ROR = 1.0 (no disproportionality) Signal: ROR & lower CI bound > 1
Illustrative methodology - not study figures. Therapies are anonymized and values are schematic, shown to demonstrate how Reporting Odds Ratios and confidence intervals are used to flag potential safety signals for further clinical evaluation.

This project illustrates how we help pharmaceutical and healthcare organizations identify potential safety signals and evaluate comparative drug risks using real-world pharmacovigilance data.

Client Profile

Pharmaceutical manufacturers, regulatory stakeholders, and healthcare organizations seeking to understand the safety profiles of GLP-1 receptor agonists using post-marketing surveillance data.

The Challenge

GLP-1 therapies have experienced rapid adoption for the treatment of diabetes and obesity. As utilization increased, questions emerged regarding the relative safety profiles of different products within the class.

Stakeholders required a systematic approach to:

  • Evaluate adverse event patterns across therapies
  • Identify potential safety signals requiring further investigation
  • Understand demographic differences in reporting
  • Support evidence-based pharmacovigilance and risk management activities
Key Requirements
  • Analyze post-marketing safety data from FAERS
  • Compare adverse event reporting across GLP-1 therapies
  • Quantify reporting disproportionality using signal detection methods
  • Evaluate reporting patterns by age and sex
  • Generate decision-ready insights for safety monitoring
The Solution
1

FAERS Data Integration

Collected and standardized adverse event reports for five GLP-1 therapies.

2

Signal Detection Analysis

Performed disproportionality analysis using Reporting Odds Ratios (RORs) to evaluate potential safety signals across key adverse events.

3

Demographic Assessment

Analyzed reporting patterns by age and sex to identify population-specific trends.

4

Comparative Safety Evaluation

Developed visualizations and comparative analyses to support pharmacovigilance decision-making and safety monitoring.

The Impact
  • Identified potential safety signals requiring further clinical evaluation
  • Quantified comparative reporting patterns across five GLP-1 therapies
  • Highlighted demographic differences in adverse event reporting
  • Demonstrated the application of FAERS data for signal detection and risk assessment
  • Provided a scalable framework for ongoing pharmacovigilance surveillance and regulatory monitoring
Key Insight

Real-world pharmacovigilance data can reveal clinically relevant safety patterns that may not be fully apparent during pre-approval clinical trials, enabling earlier detection of emerging risks and more informed risk-benefit assessments.

Outcome

The project demonstrated how post-marketing surveillance and disproportionality analysis can support pharmacovigilance activities, signal detection, and comparative safety evaluation across therapies within the same drug class. The methodology can be adapted to other therapeutic areas and regulatory safety monitoring initiatives.

Monitoring a drug-safety profile?

Tell us the therapy or signal you're tracking. We'll show you how an evidence-based pharmacovigilance analysis would approach it.