Practical Guide to Clinical Data Management

The management of clinical data, from its collection to its extraction for analysis, has become a critical element in the steps to prepare a regulatory submission and to obtain approval to market a treatment. As its importance has grown, clinical data management (CDM) has changed from an essentially clerical task in the late 1970s and early 1980s to the highly computerized specialty it is today.

Practical Guide to Clinical Data Management, Second Edition provides a solid introduction to the key process elements of clinical data management. Offering specific references to regulations and other FDA documents, it gives guidance on what is required in data handling.

Updates to the Second Edition include -

A summary of the modifications that data management groups have made under 21 CFR 11, the regulation for electronic records and signatures

Practices for both electronic data capture (EDC)-based and paper-based studies

A new chapter on Necessary Infrastructure, which addresses the expectations of the FDA and auditors for how data management groups carry out their work in compliance with regulations

The edition has been reorganized, covering the basic data management tasks that all data managers must understand. It also focuses on the computer systems, including EDC, that data management groups use and the special procedures that must be in place to support those systems. Every chapter presents a range of successful and, above all, practical options for each element of the process or task.

Focusing on responsibilities that data managers have today, this edition provides practitioners with an approach that will help them conduct their work with efficiency and quality.

PART ONE: ELEMENTS OF THE PROCESS
The data management plan: What goes into a plan? Revising the DMP. Using plans with CROs. Quality assurance and DMPs. SOPs for DMPs and study files. Using data management plans.
Case report form design considerations: Data cleaning issues. Data processing issues. Revisions to the CRF. Quality assurance for CRFs. SOPs on CRF design . Reuse and refine CRF modules.
Database design considerations: Making design decisions. High-impact fields. Tall-skinny versus short-fat. Using standards. After deciding on a design. Quality assurance for database design. SOPs for database design. Responsibilities in database design
Study setup: A plan for validation. Specification and building. Testing. Moving to production. Change control. Setup for EDC systems. Quality assurance1. SOPs for study setup. Setup is programming.
Entering data: Transcribing the data. How close a match. Dealing with problem data. Modifying data. Quality control through database audits. SOPs for data entry. Enter quality.
Tracking case report form pages and corrections: Goals of tracking. CRF workflow. Tracking challenges. Missing-pages reports. Tracking query forms. CROs and tracking. Quality assurance and quality control. SOPs for tracking. Tracking throughout the process.
Cleaning data: Identifying discrepancies. Managing discrepancies. Resolving discrepancies. Quality assurance and quality control. SOPs for discrepancy management. Making a difference.
Managing laboratory data: Storing lab data. Storing units. Ranges and normal ranges. Checking result values. Using central labs. Using specialty labs. Loading lab data. Quality assurance. SOPs for processing lab data. Taking lab data seriously.
Collecting adverse event data: Collecting AEs. Coding AE terms. Reconciling SAEs. Quality assurance and quality control. SOPs for AE data. Impact on data management. Creating reports and transferring data: Specifying the . Standard and ad hoc reports. Data transfers. Review of printed reports and presentations. SOPs for reports and transfers. Putting in the Effort.
Locking studies. Final data and queries. Final QC. Locking and unlocking. Time to study lock. After study lock. Quality assurance. SOPs for study lock. Reducing time to study lock.
PART TWO: NECESSARY INFRASTRUCTURE.
Standard operating procedures and guidelines: What is an SOP? SOPs for data management. Creating standard procedures. Complying with standard procedures. SOPs on SOPs. SOP work never ends.
Training: Who gets trained on what? How to train. Training records. SOPs on training. Allotting time for training.
Controlling access and security: Account management. Access control. SOPs and guidelines for accounts. Taking security seriously.
Working with CROs: The CRO myth. Auditing CROs. Defining responsibilities. Oversight and interaction. SOPs for working with CROs. Benefiting from CROs.
PART THREE: CDM SYSTEMS
Clinical data management systems: Where CDM systems come from. Choosing a CDM system. Using CDM systems successfully. SOPs for CDM systems. CDM systems are for more than data entry.
Electronic data capture systems: What makes EDC systems different? Working with EDC systems. Main advantages of EDC. Some problems with EDC. Will data management groups disappear? SOPs for EDC. Making EDC successful.
Choosing vendor products: Defining business needs. Initial data gathering. Requests for information. Evaluating responses. Extended demos and pilots. Additional considerations. What is missing? Preparing for implementation.
Implementing new systems: Overview and related plans. Essential preparation. Integration and extensions. Migration of legacy data. Benefiting from pilots. Validation. Preparation for production. Successful implementation.
System validation: What is validation? Validation plans or protocols. Change control and revalidation. What systems to validate. Requirements and benefits.
Test procedures: Traceability matrix. Test script . Purchasing test scripts. Training for testers. Reviewing results. Test outcome. Retaining the test materials.
Change control: What requires change control? What is a change? Documenting the change. Releasing changes. Problem logs. Considering version control. The value of change control.
Coding dictionaries: Common coding dictionaries. Using autocoders. Special considerations for AE terms. Dictionary maintenance. Quality assurance and quality control. Effective coding.
Migrating and archiving data: Simple migrations within systems. Why migrate between systems? Complex migrations. Archiving data. Migration and archive plans. Future directions.
Appendices: Data management plan outline. Typical data management standard operating procedures. Contract research organization-sponsor responsibility matrix. Implementation plan outline. Validation plan outline. CDISC and HIPAA.