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What Is a Clinical Data Manager? | CDM Series EP 01/10
EP 01/10 | What Is a Clinical Data Manager? The critical pharmaceutical role most people have never heard of, yet one that directly determines whether a drug reaches patients. In this episode you'll learn: • What Is a Clinical Data Manager? • The Scale of Clinical Trial Data • The CDM Is Responsible For • Why This Role Exists • CDM Demand Is Growing • Series: What CDMs Actually Do
Neel Gajjar
CCDM® · Program Director
GDPR Implementation Algorithm for Clinical Study Data Flow
A step-by-step algorithm for implementing GDPR-compliant data flows in clinical studies. Covers EU sponsor obligations, multi-country site data transfers, EDC vendor agreements, CRO export controls, and external lab data reconciliation.
The Clinical Trial Lifecycle: A Complete End-to-End Activity Guide
A detailed breakdown of all 20 activities across the 5 phases of a clinical trial from the first research question to final submission written for clinical research professionals who want a practical, operations-level understanding of how studies actually run, grounded in ICH GCP E6(R2) and current regulatory expectations.
Vendor Management for Clinical Data Managers: External Data Oversight, Transfers, and Lock Readiness
The vendor owns the service. The CDM owns data oversight. This guide covers the full vendor management lifecycle: DTA vs DTS, key oversight documents, the vendor data flow from source to database, a 6-question transfer checklist, common vendor issues and CDM responses, and best practices for lock readiness.
EDC UAT in Clinical Data Management: Where Protocol Requirements Become Tested System Behavior
EDC UAT is not clicking through forms - it is a clinical data quality control before the first subject is entered. This guide covers what CDMs should verify, weak vs strong UAT, positive and negative testing, role-based testing, export validation, defect management, and the consequences of rushing UAT.
SAE Reconciliation in Clinical Data Management: A Critical CDM Control for Safety Consistency and Data Integrity
SAE reconciliation is one of the highest-stakes activities in clinical data management. This guide covers why it matters, where SAE data lives, key fields to compare, the 8 most common discrepancy types, a structured 6-step process, roles and ownership, and the risks of weak reconciliation.
External Data Reconciliation for Clinical Data Managers: From Vendor Transfer to Database Lock Readiness
External data reconciliation is a CDM control process - not just a matching exercise. This guide covers what to reconcile, key matching fields, common discrepancy types, an 8-step action pathway, tracker structure, and best practices for every data source from central lab to ePRO.
Good Queries vs Weak Queries: A Practical Guide for Clinical Data Managers
Query writing is not just communication - it is a data quality control. This guide covers why query quality matters, the characteristics of strong vs weak queries, three practical examples with side-by-side comparisons, common mistakes, and a five-step framework any CDM can apply immediately.
Protocol Deviation Review in Clinical Data Management: A Practical CDM Guide
Protocol deviations are not just study conduct issues - they are data quality signals. This guide covers the CDM role in deviation review: what to check, how to reconcile across sources, and how to ensure every deviation is traceable and lock-ready.
Medical Coding Review in Clinical Data Management: Why MedDRA, WHO Drug, and Source-Term Quality Matter
A practical CDM guide to medical coding quality - covering MedDRA vs WHO Drug, what source data requires coding, weak vs strong source terms, common coding issues, the CDM review role, a practical checklist, and coding readiness questions before database lock.
IRB/REB Submissions: What to Submit vs. What to Keep Internal - A Practical CDM Guide
A practical decision guide for CDMs, CRAs, and study teams on IRB/REB submissions - covering the core principle, initial submission package, participant-facing materials, amendment triggers, safety reporting, continuing review, and a fast decision rule table.
GDPR Compliance in Clinical Data Management: From Study Start-Up to Submission
A practical GDPR guide for Clinical Data Managers - covering privacy by design at study start-up, data minimisation in eCRF design, pseudonymisation, vendor transfer controls, DPIA escalation triggers, and a pre-submission QC checklist.
ePRO/eCOA Oversight in Clinical Data Management: A Complete CDM Guide
A complete CDM guide to ePRO and eCOA oversight - covering the full taxonomy, collection window management, pre-go-live setup, UAT scenarios, key data checks, ongoing monitoring, common issues, and vendor data flow from entry to clinical database.
eConsent in Clinical Trials: An End-to-End Guide for Clinical Data Managers
A complete CDM guide to electronic consent - covering the full workflow from requirements and build through validation, monitoring, and inspection readiness. Includes a worked example, case study on wrong version handling, and a UAT checklist.
DTA vs DTS: Essential Documents for External Data Transfers in Clinical Research
A complete guide to Data Transfer Agreements (DTA) and Data Transfer Specifications (DTS) - what each document contains, when each is required, how to choose between them using a risk-based framework, and a step-by-step implementation process.
Database Lock Readiness for Clinical Data Managers: A Complete Guide
A comprehensive guide to database lock readiness - covering the full study lifecycle from setup to hard lock, including data entry completeness, query disposition, external data reconciliation, medical coding, SAE reconciliation, functional sign-offs, and a practical lock checklist.
How to Implement CDASH in a Clinical Database: An 8-Step Evidence-Chain Guide
A practical evidence-chain guide for CDMs, EDC builders, sponsors, and QA teams - covering all 8 steps from protocol review to go-live approval, with real examples, traceability tables, and clear definitions of done.
AI in Clinical Data Management: Where Each Tool Fits, What to Govern, and How to Implement
A strategic guide to generative AI, agentic AI, machine learning, and workflow automation in clinical data management - with real use cases, a 4-phase implementation plan, and a governance model for inspection-safe adoption.
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