Overview
Executive Summary
Harmonized and analyzed more than 18.5 million FAERS records to detect pharmacovigilance signals for GLP-1 receptor agonists and tirzepatide across a four-year window (Q1 2021 through Q4 2024).
Applied sex-stratified disproportionality analysis using Reporting Odds Ratio with 95% confidence intervals and Proportional Reporting Ratio as a sensitivity analysis, with formal comparison of female and male signal profiles.
All results are hypothesis-generating signals from a spontaneous reporting system. They reflect patterns in adverse event reporting, not causal relationships or population-level incidence.
Central Question
Research Question
“Do GLP-1 receptor agonists and tirzepatide exhibit differential adverse event signal profiles by sex, and do these patterns vary across drug class, adverse event category, and analysis quarter?”
Background
Project Context
Motivation
GLP-1 receptor agonists have experienced unprecedented prescribing growth since the approval of semaglutide for weight management. With millions of patients initiating treatment across diverse demographic groups, post-market pharmacovigilance has become essential for characterizing real-world safety profiles that clinical trial populations may not fully represent. Sex-stratified analysis is particularly important given documented differences in pharmacokinetics, body composition, and treatment indication between female and male populations.
Background
The FDA Adverse Event Reporting System (FAERS) is the largest spontaneous adverse event database for marketed drugs in the United States. While FAERS cannot establish causality or calculate incidence rates, disproportionality analysis methods identify patterns in reporting that warrant further investigation. Reporting Odds Ratio and Proportional Reporting Ratio are established methods in regulatory pharmacovigilance, used by agencies including the FDA and EMA for signal detection and hypothesis generation.
Contributions
My Role
End-to-end responsibility for database architecture, data harmonization pipeline, methodology design, and analysis execution.
Designed and built the PostgreSQL schema for FAERS quarterly file ingestion across all core tables
Developed the data harmonization pipeline for drug name standardization and MedDRA term validation
Implemented FDA CASEID-based deduplication within and across quarterly files
Mapped brand and generic name variants to five target compounds including the GLP-1/GIP dual agonist
Constructed sex-stratified 2x2 contingency tables for each drug-adverse event pair
Computed Reporting Odds Ratio with 95% confidence intervals and PRR as a sensitivity analysis
Applied multiple-testing adjustment to control false discovery rate across the full signal matrix
Conducted stimulated-reporting checks using quarterly temporal trend analysis
Applied Standardised MedDRA Queries for validated adverse event groupings
Inputs
Data Sources & Scale
Records Harmonized
18.5M+
Analysis Window
Q1 2021 – Q4 2024
GLP-1 Cases
158K+
Signals Detected
665
Exposures analyzed
FDA Adverse Event Reporting System
Period
Q1 2021 through Q4 2024
Scale
18.5M+ records harmonized
Tables used
Primary data source. Spontaneous reporting system. Results represent reporting signals, not population incidence.
Medical Dictionary for Regulatory Activities
Period
Current version at time of analysis
Scale
Standardised MedDRA Queries applied for validated AE groupings
Used for preferred-term validation and SMQ-level adverse event groupings where appropriate.
Pipeline
Data Engineering Workflow
Primary data source. Spontaneous reporting system. Results represent reporting signals, not population incidence.
Load FAERS quarterly ASCII files into PostgreSQL across DEMO, DRUG, REAC, OUTC, RPSR, THER, and INDI tables.
Standardize drug names to generic terms, normalize date formats, and validate MedDRA preferred terms against the current dictionary.
Apply FDA CASEID-based deduplication within and across quarterly files. Retain the most recent follow-up report for each case.
Identify cases with primary or secondary suspect GLP-1 RA or tirzepatide. Map all brand and generic name variants to the five target compounds.
Partition exposure cases into female and male subcohorts. Construct separate 2x2 contingency tables for each drug-adverse event pair within each sex stratum.
Calculate Reporting Odds Ratio with 95% confidence intervals as the primary measure, and Proportional Reporting Ratio as a sensitivity analysis. Apply multiple-testing adjustment.
Conduct stimulated-reporting checks via quarterly trend analysis, reporter-type comparisons, and SMQ-level validation. Flag signals coinciding with known external events.
Methods
Analytical Methodology
Expand each method for description and purpose.
Analysis Outputs
Selected Output Previews
Charts below are rendered from actual analysis outputs. Axes, scales, and data values reflect results from the full Q1 2021 through Q4 2024 FAERS run. Results are disproportionality signals only and do not establish causality or population-level incidence.
BH-Adjusted Signals · Jointly Evaluable PTs · Q1 2021 – Q4 2024
Each point is one type of adverse event, plotted by how often it was reported in female patients (x-axis) versus male patients (y-axis). Points above the dashed diagonal appear more in males; points below appear more in females. Colored points are statistically significant after multiple-comparison adjustment. Hover over any point to see the adverse event name and exact values.
Q1 2021 – Q4 2024 · FAERS Deduplicated Cases · Reporter Composition
Each bar shows the number of GLP-1 adverse event reports received by the FDA per quarter, broken down by who submitted them. The chartreuse line tracks the share from consumer reporters (patients and caregivers). Spikes near labeled approval dates reflect stimulated reporting, a pattern where media attention temporarily inflates submission counts rather than indicating new safety concerns.
Pre-Specified Groups · ROR with 95% CI · Female vs Male
Each row is an adverse event category. The dot shows how much more (or less) often that category was reported with GLP-1 therapies compared to all other drugs in FAERS. Values to the right of 1.0 (the dashed line) indicate elevated reporting. Pink represents female patients; teal represents male patients. Gastrointestinal and pancreatitis signals are elevated across both sexes; cardiovascular events are underrepresented. Hover over any point for exact figures.
Rigor
Data Quality & Methodological Safeguards
FDA CASEID-based deduplication removes within-quarter and cross-quarter duplicate reports, retaining the most recent follow-up for each case
Drug name harmonization validates brand and generic name variants against a curated reference list covering all five target compounds
MedDRA preferred term validation against the current MedDRA hierarchy ensures consistent adverse event classification
Primary suspect and secondary suspect drug roles are analyzed separately to assess role-sensitivity of signals
Reporter source comparisons assess whether consumer-reported and healthcare-provider-reported signals are directionally consistent
Quarterly trend analysis flags reporting periods that coincide with label updates, regulatory communications, or high media coverage events
Minimum case count threshold applied before computing disproportionality measures to exclude unstable low-frequency estimates
Responsible Interpretation
Limitations
Understanding these limitations is essential for correctly interpreting results.
Spontaneous reporting system
FAERS captures adverse events reported voluntarily by patients, healthcare providers, and manufacturers. Cases are not systematically ascertained. The database represents reporting patterns, not true adverse event incidence in treated populations.
Results are framed and interpreted as disproportionality signals only. No claims about incidence, prevalence, or absolute risk are made.
Notoriety and stimulated reporting bias
High-profile drugs attract disproportionate reporting independent of true pharmacological effects. GLP-1 receptor agonists received extensive media attention during the analysis window, which may artificially inflate reporting volumes and signal counts.
Stimulated-reporting checks using quarterly trend analysis identify and flag periods of elevated notoriety-driven reporting. Signals coinciding with known external events are interpreted with additional caution.
Absence of denominator data
FAERS does not include prescription volume, patient-years of exposure, or treated population size. Incidence rates and absolute risk estimates cannot be calculated from this data source alone.
Analysis is explicitly framed as disproportionality analysis. No incidence figures are presented or implied.
Variable missingness
Sex, age, weight, and other demographic variables are not uniformly reported across FAERS submissions. Substantial missingness in sex data requires careful handling in stratified analyses.
Missingness rates are reported per variable. Sex-stratified analyses exclude cases with missing sex or apply sensitivity analyses to assess the impact of exclusions.
Confounding by indication
GLP-1 receptor agonists are approved for both type 2 diabetes and obesity. These populations have different baseline comorbidity profiles, which can confound adverse event comparisons across drugs within the class.
INDI table analysis provides indication-level context where available. Drug-specific results are interpreted in light of each compound's primary approved indication.
Drug name harmonization complexity
Mapping brand name variants, biosimilar entries, and misspellings to the correct generic compound requires a curated reference list. Misclassification could lead to case misattribution.
A manually curated brand-to-generic mapping is maintained and versioned. Ambiguous entries are reviewed individually before inclusion.
Stack
Technology Stack
Database
Query language
Statistical analysis
Data pipeline
Infrastructure
Version control
Reflection
Lessons Learned
FAERS deduplication is substantially more complex than the official documentation suggests. Cross-quarter deduplication using CASEID requires careful logic to retain the most informative follow-up report for each case while discarding redundant submissions.
Sex missingness in FAERS is substantial for GLP-1 analyses. A documented sensitivity analysis protocol for missing sex data must be defined before interpreting stratified results.
Stimulated reporting from media coverage creates temporal artifacts in GLP-1 signal data. GLP-1 drugs received sustained public attention during the analysis window, making notoriety checks an essential step rather than an optional validation.
Drug name harmonization for GLP-1 compounds requires extensive brand-to-generic mapping. Multiple brand names, route-specific formulations, and emerging biosimilar entries all add classification complexity.
Empirical Bayes shrinkage improves estimate stability for low-frequency drug-adverse event pairs but requires careful parameter selection and should be validated against the ROR-based primary analysis.
Forward
Next Steps
Extend the analysis window to pre-2021 data to establish historical signal baselines before the GLP-1 prescribing surge began
Incorporate indication analysis using the INDI table to control for diabetes-versus-obesity confounding in drug comparisons
Implement Empirical Bayes Geometric Mean as a primary analysis alternative to complement the ROR-based approach
Compare detected signals against label-listed adverse events as a validation benchmark for method calibration
Explore integration with WHO-UMC VigiBase for international cross-validation of key signals detected in the US FAERS dataset