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BytesAgain β€Ί Skills β€Ί Clinical Data Cleaner
πŸ¦€ ClawHub

Clinical Data Cleaner

by @renhaosu2024

Use when cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing SDTM datasets, handling missing values in clinical studies, detec...

Versionv0.1.1
Downloads1,036
#clawhub
TERMINAL
clawhub install clinical-data-cleaner

πŸ“– About This Skill

name: clinical-data-cleaner description: 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. allowed-tools: "Read Write Bash Edit" license: MIT metadata: skill-author: AIPOCH version: "2.0"

Clinical Data Cleaner

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

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

    πŸ’‘ Examples

    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')

    πŸ“‹ Tips & Best Practices

    See references/troubleshooting.md for solutions to:

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