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Synthetic Longitudinal Clinical Trial Dataset - 50K Patient Visits wit
Synthetic Data Generation
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£60
About
50,000 synthetic patient visit records representing longitudinal clinical trial monitoring across multiple time points and multiple patients.
DATA STRUCTURE (52 variables):
VOLUME:
- Total patient visits: 50,000 records
- Longitudinal tracking: Multiple visits per patient
- Time range: Enrollment through trial completion or withdrawal
- Visit frequency: Scheduled follow-up visits (weeks 4, 8, 12, 16, etc.)
USE CASES:
✓ Large-scale clinical trial simulation
✓ ML model training for patient outcome prediction (sufficient data volume)
✓ Adverse event detection algorithms
✓ Treatment compliance modeling
✓ Patient dropout risk assessment
✓ Longitudinal data analysis and mixed-effects modeling
✓ Quality of life trajectory analysis
✓ Endpoint optimization and statistical power analysis
✓ Regulatory submission practice datasets
✓ Clinical data management system testing
✓ Real-world evidence (RWE) simulation
Dataset Features
DATA FORMAT: CSV (Comma-separated values)
TOTAL SIZE:
- Full dataset: ~10 MB (50,000 records)
- Demo sample: 1,000 records (205 KB) for validation
STRUCTURE:
- Total records: 50,000 patient visit records
- Total variables: 52 columns
- Missing Realistic 3-10% missingness patterns
- Longitudinal structure: Multiple time points per patient
- Visit completion rate: ~85% (realistic trial attendance)
- Dropout patterns: Lost to follow-up, withdrawal, protocol violations included
DATA QUALITY:
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Statistically validated clinical ranges
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Realistic adverse event frequencies
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Patient-reported outcome correlations
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Treatment compliance variability
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Time-dependent changes in endpoints
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Column 1 Name: Description of what this column represents.
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Column 2 Name: Add as needed...
Distribution
Detail the format, size, and structure of the dataset.
- Data Volume: Number of rows/records, number of columns, etc.
Usage
This dataset is ideal for a variety of applications:
- Application: Brief description of the first use case.
- Application: Add more as needed.
Coverage
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- Geographic Coverage: Region, country, or global.
- Time Range: Start date - End date of data collection.
- Demographics (if applicable): Age groups, gender, industries, etc.
License
Proprietary
Who Can Use It
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- Data Scientists: For training machine learning models.
- Researchers: For academic or scientific studies.
- Businesses: For analysis, insights, or AI development.
Include any additional notes or context about the dataset that might be helpful for users.
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