Dashboard Design for Trials#

Design layout sketches for clinical trial data monitoring panels, displaying recruitment progress, AE incidence rates, and other key metrics.

Features#

• Generate HTML layout sketches for clinical trial Dashboards
• Support multiple chart types: progress bars, line charts, pie charts, bar charts, etc.
• Customizable study protocol, site count, key metrics
• Responsive design, adaptable to different screen sizes

Usage#

bash

python scripts/main.py [options]

Parameters#

ParameterTypeDefaultRequiredDescription--study-idstringSTUDY-001NoStudy ID--study-namestringClinical Trial ANoStudy Name--sitesint10NoNumber of sites--target-enrollmentint100NoTarget enrollment count--current-enrollmentint45NoCurrent enrollment count--ae-countint12NoAdverse event count--outputstringdashboard.htmlNoOutput HTML file path

Examples#

bash

# Generate default Dashboard
python scripts/main.py

# Customize study parameters
python scripts/main.py \
  --study-id "PHASE-III-2024" \
  --study-name "Phase III Clinical Trial of New Drug for Type 2 Diabetes" \
  --sites 15 \
  --target-enrollment 300 \
  --current-enrollment 120 \
  --ae-count 25 \
  --output my_dashboard.html

Output#

Generates an HTML Dashboard containing the following modules:

1. Study Overview Card - Study ID, name, status
2. Recruitment Progress - Overall progress bar, site-by-site progress comparison
3. Subject Distribution - Gender, age distribution pie charts
4. AE Monitoring - Adverse event incidence rate, severity distribution
5. Data Quality - CRF completion rate, query count
6. Timeline - Study milestones, estimated completion date

Dependencies#

• Python 3.7+
• No additional dependencies (pure standard library generates HTML/CSS/JS)

Author#

Skill ID: 194

Risk Assessment#

Risk IndicatorAssessmentLevelCode ExecutionPython/R scripts executed locallyMediumNetwork AccessNo external API callsLowFile System AccessRead input files, write output filesMediumInstruction TamperingStandard prompt guidelinesLowData ExposureOutput files saved to workspaceLow

Security Checklist#

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• Input file paths validated (no ../ traversal)
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no stack traces exposed)
• Dependencies audited

Prerequisites#

No additional Python packages required.

Evaluation Criteria#

Success Metrics#

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases#

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status#

• Current Stage: Draft

• Next Review Date: 2026-03-06

• Known Issues: None

• Planned Improvements:

  • Performance optimization
  • Additional feature support