Database creation
From TrialTree Wiki
Database creation
A well-designed database is fundamental to the success of any RCT. It ensures reliable data collection, secure storage, efficient management, and regulatory compliance. Proper database planning also supports accurate analysis and valid trial conclusions.
1. Key Considerations Before Database Creation
Before building the database, trial teams should assess the trial design, outcome variables, and timelines. Define all data collection points, including baseline, follow-up visits, and outcome assessments.
Consider the types of data required:
- Participant demographics (e.g., age, sex, ethnicity)
- Randomization details
- Intervention specifics (e.g., dosage, adherence)
- Clinical and patient-reported outcomes
- Adverse events
- Follow-up assessments
Ensure compliance with data protection laws such as HIPAA (US), GDPR (EU), and ICH-GCP guidelines. Audit trails should track all modifications for accountability.
2. Choosing a Database System
Electronic Data Capture (EDC) systems vary in complexity, cost, and functionality. Here is a comparison of common systems:
| System | Pros | Cons |
|---|---|---|
| REDCap | Secure, widely used in academia, free for non-commercial use | Requires institutional hosting and technical support |
| OpenClinica | FDA/EMA compliant, user-friendly | Some features are paid |
| Castor EDC | Cloud-based, easy interface | High cost for larger trials |
| Medidata Rave | Industry-standard, robust audit trails | High licensing costs |
| Oracle Clinical | Scalable, validated | Requires specialized IT support |
| EpiData | Simple, suitable for small studies | Lacks advanced features |
REDCap is the most popular open-source system for academic trials. Castor and Medidata are preferred for industry-sponsored or regulatory-compliant studies.
3. Database Structure and Design
An RCT database typically includes:
- Participants: De-identified IDs, demographics
- Randomization: Allocation, stratification
- Baseline: Pre-intervention clinical data
- Follow-ups: Visits, outcome data
- Adverse Events: Type, severity, relationship to treatment
- Withdrawals: Reasons and dates
Use unique participant IDs and relational tables linked by primary keys. Forms should follow a logical flow and incorporate validation tools such as dropdowns, radio buttons, and conditional logic.
4. Data Collection and Entry Methods
Common data entry modes include:
- Electronic Case Report Forms (eCRFs) via EDC platforms
- Direct data entry at clinical sites using tablets or mobile apps
- Online patient-reported outcome (PRO) surveys
- Integration with wearables and sensors (e.g., Fitbit, ECG monitors)
Ensure quality with double-data entry, real-time validations, and audit trails.
5. Randomization Integration
Incorporate randomization modules directly into the EDC. Methods include:
- Simple randomization
- Blocked randomization
- Stratified randomization
- Adaptive randomization
Ensure allocation concealment and store the randomization log separately to maintain blinding integrity.
6. Data Management and Security
Secure data management is critical:
- Use role-based access and remove personal identifiers
- Enable two-factor authentication (2FA)
- Perform daily backups and maintain redundant storage
- Run data cleaning reports, manage queries, and track missing data
Audit trails should document all edits and access points to ensure integrity.
7. Exporting Data for Analysis
Support export formats that align with the analysis tools used:
- CSV / Excel for basic data review
- SPSS, SAS, or STATA for statistical analysis
- R or Python for custom scripts and modeling
Standardize variable names, remove identifiers, and check for completeness before export.
8. Budget Considerations
| Category | Description | Cost Range (USD) |
|---|---|---|
| EDC Software | REDCap (free) vs commercial platforms | $0 – $50,000+ |
| IT Support | Setup, maintenance, troubleshooting | $10,000 – $30,000 |
| Data Entry Staff | Research assistants for manual entry | $20,000 – $50,000 |
| Security & Compliance | Encryption, audit tools | $5,000 – $20,000 |
| Data Backup | Cloud storage, redundancy | $5,000 – $15,000 |
9. Common Challenges and Solutions
| Challenge | Solution |
|---|---|
| Data Entry Errors | Use validation rules, dropdowns, and real-time checks |
| Missing Data | Automate reminders, monitor follow-up completion |
| Security Breaches | Apply encryption, access controls, audit trails |
| Integration Issues | Choose flexible systems with API support |
| Cost Constraints | Opt for open-source tools like REDCap |
10. Final Recommendations
- Choose a secure, scalable, and validated EDC platform
- Design data forms with validation to reduce errors
- Integrate randomization, follow-ups, and outcomes into one system
- Ensure compliance with ethical and legal standards
- Allocate budget for IT, support staff, and security
A well-designed database is a foundation for the success of an RCT—from data quality and protocol compliance to timely and accurate analysis.
Bibliography
- Piantadosi S. Clinical Trials: A Methodologic Perspective. 3rd ed. Wiley; 2017. Chapter 15 discusses database design and data systems in randomized trials.
- Meinert CL. Clinical Trials: Design, Conduct, and Analysis. Oxford University Press; 2012. Chapter 9 covers trial data systems and database management.
- ICH E6(R2) Good Clinical Practice: Integrated Addendum to ICH E6(R1). International Council for Harmonisation; 2016. Section 5.5 outlines essential data handling and record-keeping practices.
- CDISC. Study Data Tabulation Model (SDTM) v1.8. Clinical Data Interchange Standards Consortium; 2021. Provides standards for structuring trial data for regulatory submission.
- Kush RD, Helton E, Rockhold FW, et al. Electronic health records, medical research, and the Tower of Babel. New England Journal of Medicine. 2008;358(16):1738–1740. Discusses interoperability challenges in research databases.
Adapted for educational use. Please cite relevant trial methodology sources when using this material in research or teaching.