Pharmacovigilance Adverse Event Signal Detection Query Matrix

Role: Lead Biostatistical SQL Architect specializing in post-marketing drug surveillance and clinical safety repositories.

Context

• Sponsoring pharmaceutical company: {{pharma_sponsor}}
• Investigational compound: {{investigational_drug}}
• Relational warehouse engine: {{safety_database_dialect}}
• Target adverse event domain: {{adverse_event_class}}
• Medical coding hierarchy: {{meddra_hierarchy_level}}
• Longitudinal surveillance window: {{reporting_window_months}}

Task

Build a pharmacovigilance SQL signal detection framework for {{pharma_sponsor}} to monitor {{investigational_drug}} safety events targeting {{adverse_event_class}} over {{reporting_window_months}} using {{safety_database_dialect}}.

Method

1. Establish the base exposure population CTE isolating all registered doses of {{investigational_drug}} and active comparator treatments.
2. Aggregate incident safety cases by joining coded event tables at the specified {{meddra_hierarchy_level}} level.
3. Compute 2x2 contingency table metrics (a, b, c, d cells) comparing drug-event pairings against all background therapies.
4. Write windowed mathematical aggregations calculating Proportional Reporting Ratio (PRR) and Reporting Odds Ratio (ROR).
5. Incorporate lower and upper 95% confidence interval calculations using log-transformed standard errors directly in SQL.
6. Add threshold tagging logic to flag statistically significant signals based on minimum case counts and ratio thresholds.
7. Optimize query execution plans with partition strategies based on event receipt dates and study arms.

Constraints

• MUST formulate mathematical calculations natively within {{safety_database_dialect}} syntax without external procedural scripts.
• MUST NOT discard unclassified adverse events without logging them into a dedicated anomaly capture view.
• Query MUST isolate first-occurrence events per subject to eliminate multi-reporting bias.
• Aggregation steps MUST handle zero-cell divisions using conditional null handling.

Output format

Present the complete framework in four distinct sections:

1. Signal Detection Architecture (mathematical formulation and table lineage map)
2. Query Pipeline Strategy (step-by-step breakdown of analytical CTEs)
3. Production SQL Implementation (annotated code tailored for the specified database engine)
4. Metric Validation Protocol (3 integrity verification queries with expected output schemas)

Self-review

• Confirm that 2x2 contingency table calculations correctly reflect mutually exclusive event-drug counts.
• Verify that MedDRA term expansion strictly adheres to {{meddra_hierarchy_level}}.
• Check that division-by-zero guards prevent pipeline failures on rare event classes.