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Bayesian Modeling for the Detection of Adverse Events Underreporting in Clinical Trials
1F. Hoffmann-La Roche AG, 4070, Basel, Switzerland.
Drug Safety
|July 14, 2021
Summary
A new Bayesian model estimates adverse event underreporting in clinical trials, complementing machine learning. This enhances patient safety and reduces manual audits by identifying high-risk sites for better quality assurance.
Area of Science:
- Clinical Trials
- Pharmacovigilance
- Data Science
Background:
- Adverse event (AE) underreporting is a persistent challenge in clinical trials, compromising patient safety and data integrity.
- Current quality assurance (QA) relies on on-site audits, which are resource-intensive and may not fully capture reporting issues.
- Existing predictive models have limitations in providing comprehensive oversight of AE reporting.
Purpose of the Study:
- To develop a robust method for calculating the probability of AE underreporting.
- To enhance patient safety and data integrity in clinical trials.
- To reduce reliance on manual, on-site QA activities.
Main Methods:
- Utilized a Bayesian hierarchical model to estimate site-specific reporting rates.
- Assessed the risk of underreporting by analyzing patient-level observations.
- Employed publicly available, anonymized clinical trial data from Project Data Sphere.
Main Results:
- Developed a model that infers site reporting behavior from patient data.
- Enabled robust detection of outlier reporting patterns across clinical sites.
- Provided a quantitative measure of underreporting probability.
Conclusions:
- The new Bayesian model complements existing machine learning approaches for AE reporting oversight.
- Integration into clinical quality program lead (QPL) dashboards will shift QA focus to high-risk areas, reducing on-site audits.
- This approach enhances safety reporting QA and generates valuable evidence for regulatory inspections.
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