I provide comprehensive data management support for clinical studies, ensuring that all collected data is accurate, reliable, and compliant with regulatory requirements. My expertise covers the entire data lifecycle, from collection and validation to analysis and reporting, enabling high-quality results and data integrity throughout the study process.
Key Components of Data Management Support:
1. Data Collection & Database Design
- Develop and implement electronic data capture (EDC) systems to ensure efficient and standardized data collection.
- Design study-specific case report forms (CRFs) aligned with protocol requirements.
- Ensure user-friendly interfaces for investigators and site personnel to minimize data entry errors.
- Establish processes for paper-based data collection, if required.
2. Data Entry, Validation & Cleaning
- Monitor data entry to ensure consistency and completeness across study sites.
- Implement edit checks and automated validation rules to detect discrepancies in real time.
- Perform data cleaning procedures, including query resolution and reconciliation of missing or inconsistent data.
- Ensure compliance with Good Clinical Data Management Practices (GCDMP).
3. Data Quality Control & Assurance
- Conduct source data verification (SDV) and implement quality control (QC) checks.
- Perform audit trails and change tracking to maintain transparency in data modifications.
- Ensure adherence to ICH-GCP and 21 CFR Part 11 compliance for electronic records.
4. Data Coding & Standardization
- Apply standard medical coding dictionaries, such as MedDRA and WHO Drug Dictionary, to classify adverse events, medical history, and concomitant medications.
- Ensure data consistency by implementing CDISC standards (SDTM and ADaM) for regulatory submissions.
- Standardize datasets to ensure compatibility with statistical analysis and reporting tools.
5. Data Monitoring & Risk Management
- Conduct real-time data monitoring to detect protocol deviations, trends, or anomalies.
- Implement risk-based monitoring (RBM) strategies to prioritize critical data points.
- Collaborate with clinical monitoring teams to address and resolve data-related issues efficiently.
6. Data Reconciliation & Adverse Event Management
- Perform SAE (Serious Adverse Event) reconciliation to ensure consistency between clinical databases and safety systems.
- Verify data accuracy in pharmacovigilance reports and regulatory submissions.
- Coordinate with safety teams to resolve discrepancies in adverse event reporting.
7. Database Lock & Finalization
- Ensure all data queries are resolved, and database freeze procedures are followed.
- Perform pre-lock data quality reviews to confirm completeness and integrity.
- Securely lock the database to prevent unauthorized changes before statistical analysis.
8. Statistical Programming & Data Analysis Support
- Prepare clean datasets for statistical analysis.
- Support biostatistics teams by providing structured, validated datasets.
- Ensure all data outputs meet regulatory submission requirements.
9. Regulatory Compliance & Submission Readiness
- Prepare Clinical Study Reports (CSR) and regulatory datasets (CDISC-compliant) for submission to health authorities (e.g., FDA, EMA).
- Ensure compliance with data privacy laws such as GDPR and HIPAA.
- Maintain traceability and documentation for regulatory audits and inspections.
10. Data Archiving & Long-Term Retention
- Securely archive study data for required retention periods, ensuring accessibility for future audits.
- Maintain metadata and documentation for study reproducibility and transparency.
- Ensure compliance with sponsor and regulatory guidelines for data storage and retrieval.
By integrating rigorous data management strategies, I ensure high-quality, regulatory-compliant datasets that support robust study conclusions and successful regulatory submissions.