Datasets:
Languages:
English
Size:
10M<n<100M
Tags:
biomedical
negative-results
benchmark
drug-target-interaction
clinical-trials
protein-protein-interaction
License:
| """Tests for outcome enrichment pipeline.""" | |
| import sqlite3 | |
| from pathlib import Path | |
| import pandas as pd | |
| import pytest | |
| from negbiodb_ct.ct_db import create_ct_database, get_connection | |
| from negbiodb_ct.etl_outcomes import ( | |
| enrich_results_with_aact, | |
| enrich_results_with_shi_du, | |
| load_shi_du_efficacy, | |
| load_shi_du_safety, | |
| upgrade_confidence_tiers, | |
| ) | |
| MIGRATIONS_DIR = Path(__file__).resolve().parent.parent / "migrations_ct" | |
| def ct_db(tmp_path): | |
| """Create a fresh CT database with all migrations applied.""" | |
| db_path = tmp_path / "test_ct.db" | |
| create_ct_database(db_path, MIGRATIONS_DIR) | |
| return db_path | |
| def enrichment_db(ct_db): | |
| """CT database with sample data for enrichment testing.""" | |
| conn = get_connection(ct_db) | |
| try: | |
| # Interventions | |
| conn.execute( | |
| "INSERT INTO interventions (intervention_type, intervention_name) " | |
| "VALUES ('drug', 'DrugA')" | |
| ) | |
| # Conditions | |
| conn.execute("INSERT INTO conditions (condition_name) VALUES ('DiseaseX')") | |
| # Trials | |
| conn.execute( | |
| "INSERT INTO clinical_trials " | |
| "(source_db, source_trial_id, overall_status, trial_phase, has_results) " | |
| "VALUES ('clinicaltrials_gov', 'NCT001', 'Completed', 'phase_3', 1)" | |
| ) | |
| conn.execute( | |
| "INSERT INTO clinical_trials " | |
| "(source_db, source_trial_id, overall_status, trial_phase, has_results) " | |
| "VALUES ('clinicaltrials_gov', 'NCT002', 'Terminated', 'phase_2', 0)" | |
| ) | |
| # Junction tables | |
| conn.execute( | |
| "INSERT INTO trial_interventions (trial_id, intervention_id) VALUES (1, 1)" | |
| ) | |
| conn.execute( | |
| "INSERT INTO trial_conditions (trial_id, condition_id) VALUES (1, 1)" | |
| ) | |
| conn.execute( | |
| "INSERT INTO trial_interventions (trial_id, intervention_id) VALUES (2, 1)" | |
| ) | |
| conn.execute( | |
| "INSERT INTO trial_conditions (trial_id, condition_id) VALUES (2, 1)" | |
| ) | |
| # Publications (for tier upgrade test) | |
| conn.execute( | |
| "INSERT INTO trial_publications (trial_id, pubmed_id) VALUES (1, 12345678)" | |
| ) | |
| # Failure results — one bronze, one silver | |
| conn.execute( | |
| "INSERT INTO trial_failure_results " | |
| "(intervention_id, condition_id, trial_id, " | |
| " failure_category, confidence_tier, highest_phase_reached, " | |
| " source_db, source_record_id, extraction_method) " | |
| "VALUES (1, 1, 1, 'efficacy', 'bronze', 'phase_3', " | |
| " 'clinicaltrials_gov', 'terminated:NCT001', 'nlp_classified')" | |
| ) | |
| conn.execute( | |
| "INSERT INTO trial_failure_results " | |
| "(intervention_id, condition_id, trial_id, " | |
| " failure_category, confidence_tier, highest_phase_reached, " | |
| " source_db, source_record_id, extraction_method) " | |
| "VALUES (1, 1, 2, 'efficacy', 'silver', 'phase_2', " | |
| " 'clinicaltrials_gov', 'terminated:NCT002', 'nlp_classified')" | |
| ) | |
| conn.commit() | |
| finally: | |
| conn.close() | |
| return ct_db | |
| # ============================================================ | |
| # AACT ENRICHMENT TESTS | |
| # ============================================================ | |
| class TestEnrichResultsWithAact: | |
| def test_updates_p_value(self, enrichment_db): | |
| conn = get_connection(enrichment_db) | |
| try: | |
| aact_df = pd.DataFrame({ | |
| "nct_id": ["NCT001"], | |
| "p_value": [0.073], | |
| "ci_lower_limit": [-0.5], | |
| "ci_upper_limit": [0.1], | |
| "param_value": [-0.2], | |
| "param_type": ["Mean Difference"], | |
| "method": ["ANCOVA"], | |
| }) | |
| n = enrich_results_with_aact(conn, aact_df) | |
| assert n >= 1 | |
| row = conn.execute( | |
| "SELECT p_value_primary, primary_endpoint_met " | |
| "FROM trial_failure_results WHERE trial_id = 1" | |
| ).fetchone() | |
| assert abs(row[0] - 0.073) < 1e-6 | |
| assert row[1] == 0 # p > 0.05 | |
| finally: | |
| conn.close() | |
| def test_endpoint_met_when_p_low(self, enrichment_db): | |
| conn = get_connection(enrichment_db) | |
| try: | |
| aact_df = pd.DataFrame({ | |
| "nct_id": ["NCT002"], | |
| "p_value": [0.003], | |
| "ci_lower_limit": [None], | |
| "ci_upper_limit": [None], | |
| "param_value": [None], | |
| "param_type": [None], | |
| "method": ["t-test"], | |
| }) | |
| enrich_results_with_aact(conn, aact_df) | |
| row = conn.execute( | |
| "SELECT primary_endpoint_met FROM trial_failure_results " | |
| "WHERE trial_id = 2" | |
| ).fetchone() | |
| assert row[0] == 1 # p <= 0.05 | |
| finally: | |
| conn.close() | |
| def test_empty_df(self, enrichment_db): | |
| conn = get_connection(enrichment_db) | |
| try: | |
| n = enrich_results_with_aact(conn, pd.DataFrame()) | |
| assert n == 0 | |
| finally: | |
| conn.close() | |
| # ============================================================ | |
| # SHI & DU LOAD TESTS | |
| # ============================================================ | |
| class TestLoadShiDuEfficacy: | |
| def test_basic_load(self, tmp_path): | |
| csv = tmp_path / "efficacy.csv" | |
| csv.write_text("nct_id,p_value,effect_size\nNCT001,0.05,1.2\nNCT002,0.8,-0.3\n") | |
| result = load_shi_du_efficacy(csv) | |
| assert len(result) == 2 | |
| assert "p_value" in result.columns | |
| def test_missing_file(self, tmp_path): | |
| result = load_shi_du_efficacy(tmp_path / "nonexistent.csv") | |
| assert result.empty | |
| class TestLoadShiDuSafety: | |
| def test_basic_load(self, tmp_path): | |
| csv = tmp_path / "safety.csv" | |
| csv.write_text( | |
| "NCT_ID,serious/other,affected,at_risk\n" | |
| "NCT001,Serious,5,100\n" | |
| "NCT001,Other,10,100\n" | |
| "NCT002,Serious,3,50\n" | |
| ) | |
| result = load_shi_du_safety(csv) | |
| # NCT001 has 5 serious affected, NCT002 has 3 | |
| assert len(result) == 2 | |
| assert "sae_total" in result.columns | |
| nct1 = result[result["nct_id"] == "NCT001"]["sae_total"].iloc[0] | |
| assert nct1 == 5 # Only "Serious" rows counted | |
| def test_missing_file(self, tmp_path): | |
| result = load_shi_du_safety(tmp_path / "nonexistent.csv") | |
| assert result.empty | |
| # ============================================================ | |
| # SHI & DU ENRICHMENT TESTS | |
| # ============================================================ | |
| class TestEnrichResultsWithShiDu: | |
| def test_updates_safety_data(self, enrichment_db): | |
| conn = get_connection(enrichment_db) | |
| try: | |
| safety_df = pd.DataFrame({ | |
| "nct_id": ["NCT001"], | |
| "sae_total": [10], | |
| }) | |
| n = enrich_results_with_shi_du(conn, safety_df) | |
| assert n >= 1 | |
| row = conn.execute( | |
| "SELECT serious_adverse_events " | |
| "FROM trial_failure_results WHERE trial_id = 1" | |
| ).fetchone() | |
| assert row[0] == 10 | |
| finally: | |
| conn.close() | |
| def test_empty_df(self, enrichment_db): | |
| conn = get_connection(enrichment_db) | |
| try: | |
| n = enrich_results_with_shi_du(conn, pd.DataFrame()) | |
| assert n == 0 | |
| finally: | |
| conn.close() | |
| # ============================================================ | |
| # TIER UPGRADE TESTS | |
| # ============================================================ | |
| class TestUpgradeConfidenceTiers: | |
| def test_bronze_to_silver(self, enrichment_db): | |
| conn = get_connection(enrichment_db) | |
| try: | |
| # Use trial_id=2 (Phase 2, no PubMed) — won't cascade to gold | |
| conn.execute( | |
| "UPDATE trial_failure_results SET " | |
| "confidence_tier = 'bronze', p_value_primary = 0.12 " | |
| "WHERE trial_id = 2" | |
| ) | |
| conn.commit() | |
| stats = upgrade_confidence_tiers(conn) | |
| assert stats["bronze_to_silver"] >= 1 | |
| row = conn.execute( | |
| "SELECT confidence_tier FROM trial_failure_results WHERE trial_id = 2" | |
| ).fetchone() | |
| assert row[0] == "silver" | |
| finally: | |
| conn.close() | |
| def test_silver_to_gold(self, enrichment_db): | |
| conn = get_connection(enrichment_db) | |
| try: | |
| # Set up silver Phase III with PubMed | |
| # trial_id=1 is already Phase III with results and has a publication | |
| conn.execute( | |
| "UPDATE trial_failure_results SET " | |
| "confidence_tier = 'silver', highest_phase_reached = 'phase_3' " | |
| "WHERE trial_id = 1" | |
| ) | |
| conn.commit() | |
| stats = upgrade_confidence_tiers(conn) | |
| assert stats["silver_to_gold"] == 1 | |
| row = conn.execute( | |
| "SELECT confidence_tier FROM trial_failure_results WHERE trial_id = 1" | |
| ).fetchone() | |
| assert row[0] == "gold" | |
| finally: | |
| conn.close() | |
| def test_no_upgrade_without_evidence(self, enrichment_db): | |
| conn = get_connection(enrichment_db) | |
| try: | |
| stats = upgrade_confidence_tiers(conn) | |
| # Bronze result has no p-value → stays bronze | |
| assert stats["bronze_to_silver"] == 0 | |
| finally: | |
| conn.close() | |