Clinical Data Cleaner

Use when cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing SDTM datasets, handling missing values in clinical studies, detecting outliers in lab results, or converting raw CRF data to CDISC format. Cleans and standardizes clinical trial data for regulatory compliance with audit trails.

Clean, validate, and standardize clinical trial data to meet CDISC SDTM standards for regulatory submissions to FDA or EMA.

Quick Start

from scripts.main import ClinicalDataCleaner

# Initialize for Demographics domain
cleaner = ClinicalDataCleaner(domain='DM')

# Clean data with default settings
cleaned = cleaner.clean(raw_data)

# Save with audit trail
cleaner.save_report('output.csv')

Core Capabilities

1. SDTM Domain Validation

cleaner = ClinicalDataCleaner(domain='DM')  # or 'LB', 'VS'
is_valid, missing = cleaner.validate_domain(data)

Required Fields:

• DM: STUDYID, USUBJID, SUBJID, RFSTDTC, RFENDTC, SITEID, AGE, SEX, RACE
• LB: STUDYID, USUBJID, LBTESTCD, LBCAT, LBORRES, LBORRESU, LBSTRESC, LBDTC
• VS: STUDYID, USUBJID, VSTESTCD, VSORRES, VSORRESU, VSSTRESC, VSDTC

2. Missing Value Handling

cleaner = ClinicalDataCleaner(
    domain='DM',
    missing_strategy='median'  # mean, median, mode, forward, drop
)
cleaned = cleaner.handle_missing_values(data)

3. Outlier Detection

cleaner = ClinicalDataCleaner(
    domain='LB',
    outlier_method='domain',  # iqr, zscore, domain
    outlier_action='flag'     # flag, remove, cap
)
flagged = cleaner.detect_outliers(data)

Clinical Thresholds:

Parameter	Range	Unit
Glucose	50-500	mg/dL
Hemoglobin	5-20	g/dL
Systolic BP	70-220	mmHg

4. Date Standardization

standardized = cleaner.standardize_dates(data)
# Converts to ISO 8601: 2023-01-15T09:30:00

5. Complete Pipeline

cleaner = ClinicalDataCleaner(
    domain='DM',
    missing_strategy='median',
    outlier_method='iqr',
    outlier_action='flag'
)
cleaned_data = cleaner.clean(data)
cleaner.save_report('output.csv')

Output Files:

• output.csv - Cleaned SDTM data
• output.report.json - Audit trail for regulatory submission

CLI Usage

# Clean demographics
python scripts/main.py \
  --input dm_raw.csv \
  --domain DM \
  --output dm_clean.csv \
  --missing-strategy median \
  --outlier-method iqr \
  --outlier-action flag

# Clean lab data with clinical thresholds
python scripts/main.py \
  --input lb_raw.csv \
  --domain LB \
  --output lb_clean.csv \
  --outlier-method domain

Common Patterns

See references/common-patterns.md for detailed examples:

• Regulatory Submission Preparation
• Interim Analysis Data Preparation
• Database Migration Cleanup
• External Lab Data Integration

Troubleshooting

See references/troubleshooting.md for solutions to:

• Validation failures
• Date parsing errors
• Memory errors with large datasets
• Outlier detection issues

Quality Checklist

Pre-Cleaning:

• IACUC approval obtained (animal studies)
• Sample size adequately powered
• Randomization method documented

Post-Cleaning:

• Validate against CDISC SDTM IG
• Review all cleaning actions in audit trail
• Test import to analysis software

References

• references/sdtm_ig_guide.md - CDISC SDTM Implementation Guide
• references/domain_specs.json - Domain-specific field requirements
• references/outlier_thresholds.json - Clinical outlier thresholds
• references/common-patterns.md - Detailed usage patterns
• references/troubleshooting.md - Problem-solving guide

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Skill ID: 189 | Version: 2.0 | License: MIT