Faers Analytics Tools#
Configuration File: faers_analytics_tools.json
Tool Type: Local
Tools Count: 6
This page contains all tools defined in the faers_analytics_tools.json configuration file.
Available Tools#
FAERS_analyze_temporal_trends (Type: FAERSAnalyticsTool)#
Analyze temporal trends in adverse event reporting by year. Returns yearly counts and trend direc…
FAERS_analyze_temporal_trends tool specification
Tool Information:
Name:
FAERS_analyze_temporal_trendsType:
FAERSAnalyticsToolDescription: Analyze temporal trends in adverse event reporting by year. Returns yearly counts and trend direction (increasing/decreasing/stable). Example: ibuprofen GI bleeding reports 2015-2025 → +25% increase. Use for signal evolution monitoring.
Parameters:
operation(unknown) (optional) Operation type (fixed)drug_name(string) (optional) Generic drug nameadverse_event(string) (optional) MedDRA Preferred Term (optional, omit for all events). Use exact MedDRA Preferred Term capitalization (e.g., “Haemorrhage” not “hemorrhage”).reaction(string) (optional) Alias for adverse_event. MedDRA Preferred Term for the adverse drug reaction.drug(string) (optional) Alias for drug_name. Generic drug name.
Example Usage:
query = {
"name": "FAERS_analyze_temporal_trends",
"arguments": {
}
}
result = tu.run(query)
FAERS_calculate_disproportionality (Type: FAERSAnalyticsTool)#
Calculate statistical disproportionality measures (ROR, PRR, IC) with 95% CI for drug-event pairs…
FAERS_calculate_disproportionality tool specification
Tool Information:
Name:
FAERS_calculate_disproportionalityType:
FAERSAnalyticsToolDescription: Calculate statistical disproportionality measures (ROR, PRR, IC) with 95% CI for drug-event pairs. Returns signal strength classification. Example: drug=’ibuprofen’, event=’hepatotoxicity’ → ROR=2.3 [1.8-2.9]. Use for pharmacovigilance signal detection.
Parameters:
operation(unknown) (optional) Operation type (fixed)drug_name(string) (optional) Generic drug name (e.g., ‘IBUPROFEN’, ‘ATORVASTATIN’)adverse_event(string) (optional) MedDRA Preferred Term (e.g., ‘Hepatotoxicity’, ‘Myopathy’). Use exact MedDRA Preferred Term capitalization (e.g., “Haemorrhage” not “hemorrhage”).reaction(string) (optional) Alias for adverse_event. MedDRA Preferred Term for the adverse drug reaction.drug(string) (optional) Alias for drug_name. Generic drug name.
Example Usage:
query = {
"name": "FAERS_calculate_disproportionality",
"arguments": {
}
}
result = tu.run(query)
FAERS_compare_drugs (Type: FAERSAnalyticsTool)#
Compare safety signals for two drugs with the same adverse event. Returns ROR/PRR/IC for both dru…
FAERS_compare_drugs tool specification
Tool Information:
Name:
FAERS_compare_drugsType:
FAERSAnalyticsToolDescription: Compare safety signals for two drugs with the same adverse event. Returns ROR/PRR/IC for both drugs with comparative analysis. Example: compare fulvestrant vs tamoxifen for hepatotoxicity. Use for safety benchmarking. Each drug’s arm also returns the 2x2 contingency_table its metrics were computed from – a_drug_and_event, b_drug_no_event, c_no_drug_event, d_no_drug_no_event, the same keys FAERS_calculate_disproportionality uses – so the case counts behind the verdict are visible without extra calls. This matters because a ratio of two ratios is uninterpretable on its own: “similar-strength signals” means something very different when both arms rest on thousands of co-reported cases than when one rests on a handful. When either arm’s a_drug_and_event falls below 25, comparison_caveat names that arm and its count and warns that comparison is not an equal-confidence statement; it is null when both arms clear the threshold. 25 is where an arm’s own 95% ROR interval becomes narrower than the 1.5x ratio this comparison uses to separate “similar-strength” from “stronger”.
Parameters:
operation(unknown) (optional) Operation type (fixed)drug1(string) (optional) First drug name (generic)drug2(string) (optional) Second drug name (generic)adverse_event(string) (optional) MedDRA Preferred Term to compare. Use exact MedDRA Preferred Term capitalization (e.g., “Haemorrhage” not “hemorrhage”).reaction(string) (optional) Alias for adverse_event. MedDRA Preferred Term for the adverse drug reaction.drugs(array) (optional) Alias for drug1/drug2. List of two drug names to compare, e.g., [“tofacitinib”, “baricitinib”].
Example Usage:
query = {
"name": "FAERS_compare_drugs",
"arguments": {
}
}
result = tu.run(query)
FAERS_filter_serious_events (Type: FAERSAnalyticsTool)#
Filter for serious adverse events (death, hospitalization, disability, life-threatening). Returns…
FAERS_filter_serious_events tool specification
Tool Information:
Name:
FAERS_filter_serious_eventsType:
FAERSAnalyticsToolDescription: Filter for serious adverse events (death, hospitalization, disability, life-threatening). Returns top reactions meeting seriousness criteria. Example: fulvestrant serious events → Death: 150 cases, Hospitalization: 500 cases. Use for risk-benefit assessment.
Parameters:
operation(unknown) (optional) Operation type (fixed)drug_name(string) (optional) Generic drug nameseriousness_type(string) (optional) Type of serious event to filterdrug(string) (optional) Alias for drug_name. Generic drug name.event_type(string) (optional) Alias for seriousness_type. Type of serious event (e.g., hospitalization, death, life_threatening).adverse_event(string) (optional) Specific adverse event MedDRA term to filter within serious events (e.g., MYOCARDIAL INFARCTION, DEATH). Use uppercase MedDRA PT terms.
Example Usage:
query = {
"name": "FAERS_filter_serious_events",
"arguments": {
}
}
result = tu.run(query)
FAERS_rollup_meddra_hierarchy (Type: FAERSAnalyticsTool)#
Aggregate adverse events by MedDRA hierarchy (Preferred Term level). Returns the most-reported pr…
FAERS_rollup_meddra_hierarchy tool specification
Tool Information:
Name:
FAERS_rollup_meddra_hierarchyType:
FAERSAnalyticsToolDescription: Aggregate adverse events by MedDRA hierarchy (Preferred Term level). Returns the most-reported preferred terms with counts, ranked by descending report count, up to openFDA’s per-request maximum (999 anonymous, 1000 with FDA_API_KEY set). openFDA does not report how many distinct terms exist, so ‘unique_PTs_returned’ is the number of rows returned, NOT a total: check ‘truncated’ and read ‘truncation_note’ before treating the list as the drug’s full reaction profile. A PT absent from the list is not evidence it was never reported. Note: Full HLT/SOC hierarchy requires MedDRA license. Use for adverse event profile overview.
Parameters:
operation(unknown) (optional) Operation type (fixed)drug_name(string) (optional) Generic drug namedrug(string) (optional) Alias for drug_name. Generic drug name.
Example Usage:
query = {
"name": "FAERS_rollup_meddra_hierarchy",
"arguments": {
}
}
result = tu.run(query)
FAERS_stratify_by_demographics (Type: FAERSAnalyticsTool)#
Stratify adverse event reports by demographics (sex, age group, country). Returns counts and perc…
FAERS_stratify_by_demographics tool specification
Tool Information:
Name:
FAERS_stratify_by_demographicsType:
FAERSAnalyticsToolDescription: Stratify adverse event reports by demographics (sex, age group, country). Returns counts and percentages by demographic group. Example: ibuprofen + GI bleeding stratified by sex → Female: 60%, Male: 40%. Use for subpopulation risk assessment. IMPORTANT – the stratification covers only a SUBSET of the matching reports: FAERS records demographics inconsistently, and openFDA computes a count facet solely over records that populate the counted field, so reports missing the demographic are absent from the groups entirely rather than bucketed as unknown. The response therefore reports two figures side by side: total_reports_matching_query (every report matching the drug/event query, from openFDA’s own meta.results.total) and stratified_report_count (the subset where the demographic is recorded, which the groups sum to) – for ondansetron stratified by age these are ~136,000 and ~25,000 respectively. A coverage_note names both figures and the coverage fraction. Each group percentage is a share of stratified_report_count, which is the correct denominator for a stratification. The legacy total_reports key repeats stratified_report_count for backward compatibility and is NOT the drug’s report count – read total_reports_matching_query for that. If the extra total request fails, total_reports_matching_query is null and coverage_note says so; everything else is still returned.
Parameters:
operation(unknown) (optional) Operation type (fixed)drug_name(string) (optional) Generic drug nameadverse_event(string) (optional) MedDRA Preferred Term. Use exact MedDRA Preferred Term capitalization (e.g., “Haemorrhage” not “hemorrhage”).stratify_by(string) (optional) Demographic dimension to stratify by. Use “sex”, “age”, or “country” (“age_group” is also accepted as alias for “age”).reaction(string) (optional) Alias for adverse_event. MedDRA Preferred Term for the adverse drug reaction (e.g., “hemorrhage”, “nausea”).demographic(string) (optional) Alias for stratify_by. Demographic dimension to stratify by (sex, age, or country).drug(string) (optional) Alias for drug_name. Generic drug name.
Example Usage:
query = {
"name": "FAERS_stratify_by_demographics",
"arguments": {
}
}
result = tu.run(query)