Data Management Plan Template Clinical Trial

Standard Operating Procedure: Clinical Trial Data Management Plan (DMP) Development

---

1. Executive Summary & Purpose

The purpose of this SOP is to define the standardized methodology for creating, maintaining, and archiving a Data Management Plan (DMP) for clinical trials. A robust DMP ensures data integrity, compliance with 21 CFR Part 11 and GCP/ICH-GCP guidelines, and audit-readiness throughout the trial lifecycle.

2. Scope & Prerequisites

• Scope: Applies to all phase I-IV clinical trials managed by the Template Registry ecosystem.
• Prerequisites: Access to the Electronic Data Capture (EDC) system, Clinical Data Management System (CDMS), and the centralized Document Management System (DMS).
• Software: CDISC-compliant metadata repository, SAS/R statistical programming environment, and secure cloud-based version control (e.g., SharePoint/Veeva).

3. Roles & Responsibilities (RACI Matrix)

---

4. Step-by-Step Procedure

Phase I: DMP Drafting & Documentation

• Initialize DMP document from TR-DMP-MASTER-TEMPLATE.
• Document data sources (e.g., eCRF, ePRO, Central Lab, PK/PD data).
• Define data flow architecture and integration points.
• Establish Data Validation Plan (DVP) specifications.

Phase II: Coding & Standardization

• Map all data points to CDISC SDTM domains.
• Define dictionary coding standards (MedDRA for Adverse Events, WHODrug for Concomitant Medications).
• Configure edit check programming specifications within the EDC.

Phase III: Quality Control & Reconciliation

• Execute User Acceptance Testing (UAT) for the EDC and peripheral data transfers.
• Implement query management process: define triggers for manual vs. automated queries.
• Conduct external data reconciliation (e.g., matching central lab results to eCRF data).

Phase IV: Database Lock & Archival

• Run "Final Data Check" to ensure zero open queries.
• Secure regulatory signatures from PI and Sponsor.
• Perform Database Lock (DBL) per protocol requirements.
• Generate archival package for long-term retention.

---

5. Quality Assurance & Pro-Tips

Best Practices:

• Automation: Utilize automated data cleaning scripts for common logic errors (e.g., date ranges, unit mismatches).
• Modularization: Keep DMP appendices separate so updates to specific lab procedures do not trigger a full document re-approval.

Common Pitfalls:

• Vague Definitions: Failure to define "missing data" handling early leads to statistical bias.
• Scope Creep: Adding data collection fields post-study start without updating the DMP.

Metric Thresholds:

• Query Aging: Average time to close a query must be < 10 business days.
• Data Integrity: 0% critical findings during GCP audits.

---

6. Frequently Asked Questions (FAQ)

Q: At what stage should the DMP be finalized? A: The DMP must be finalized and signed off prior to the First Patient In (FPI) to ensure all data cleaning logic is vetted before the first data point is captured.

Q: How do we handle protocol amendments regarding data collection? A: Any protocol amendment triggers a formal DMP version update. The impact analysis must be documented in the "Revision History" section, highlighting changes to validation rules or data mappings.

---

Authorized by: Julian Vance, Chief Architect, Template Registry