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What Is Clinical Data Management?

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Clinical data management (CDM) is the handling of information that results from clinical trials. All aspects of processing study information are part of clinical data management. This includes developing and maintaining software systems, databases, processes, procedures, training, and protocols to support collecting, cleaning, and managing subject or trial data.

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Clinical data management processes that weave through an entire clinical study result in high-integrity data for use in research and reporting.

The primary role of clinical data management is to produce and maintain high-quality data from data entry through analysis. Performed correctly, the result of clinical data management is a dataset that is accurate, secure, reliable, and ready for analysis at the end of a study.

Study data must also be collected, organized, and saved to meet compliance requirements, such as CFR21 Part 11, good laboratory practices (GLP), good manufacturing practices (GMP), and good clinical practice (GCP).

Eight Stages of a Clinical Data Management Cycle

  1. 1. Set up
    Prepare the overall plan, database, and forms.
  2. 2. Collect
    Gather data over the course of the study.
  3. 3. Assure
    Confirm that the plan, tools, and data meet the requirements, including data quality.
  4. 4. Identify
    Monitor for issues or risks.
  5. 5. Preserve
    Protect the integrity of collected data.
  6. 6. Integrate
    Map different datasets and information together in a cohesive, consistent format.
  7. 7. Analyze
    Review data to identify trends and report outcomes.
  8. 8. Lock
    Secure the database and prevent changes to protect data integrity.

CDM activities include:

  • Audit trail
  • Data abstraction
  • Data acquisition
  • Data analysis
  • Data archiving
  • Data coding
  • Data collection
  • Data extraction
  • Data management plan
  • Data privacy
  • Data processing
  • Data quality analysis
  • Data storage
  • Data transmission
  • Database closure
  • Monitoring of data and its safety
  • Security and confidentiality
  • Study set up
  • Training

Why Is Clinical Data Management Important?

Clinical data management provides critical support for the evaluation process of regulated products (e.g., pharmaceuticals, medical devices, cosmetics, food). In addition to ensuring that these products are safe, perform as expected, and adhere to regulatory requirements, clinical data management also provides these benefits:

  • Assurance of data quality  
  • Expedited development
  • Protection from data loss
  • Reduced costs
  • Security

Clinical data management also ensures:

  • Clean study dataset to support statistical analysis and reporting
  • Complete and accurate collection of data
  • Data formatted for optimal usability in a timely manner
  • Integrity and quality of data transferred from trial subjects to study database
  • True representation of trial in the study’s database

Three Objectives of Clinical Data Management

  1. 1. Data collection
    Capturing data from paper and electronic media
  2. 2. Data integration
    Integrating all of the data into a single database to ensure consistency and correctness
  3. 3. System and data validation
    User acceptance testing (UAT), quality controls (QC), programming done with edit check programs, and manual review

How Does Clinical Data Management Work?

Clinical data management covers a range of data services. Common responsibilities that explain how clinical data management works include:

  • Design and develop user-friendly case report forms (CRF)—traditional paper or digital (eCRF), including these forms:
    • Adverse effect (AE) form
    • Concomitant therapy form
    • Eligibility or screening
    • Follow-up visit
    • Lab test form
    • Medical history
    • Physical exam and vitals
    • Randomization
    • Severe adverse effect (SAE) form
    • Status evaluation
  • Database design and build should be done in a secure, non-study environment or test site and include:
    • Automated edit checks to ensure that data meets previously defined rules
    • Back-end tables
    • Data stored in the clinical data management system (CDMS)
    • Study-specific data entry fields and screens
  • Preparation and testing, including:
    • Controls for data entry accuracy
    • Detection of data inconsistencies
    • Testing performed at the test site
    • Entry screens and programming testing using test subjects’ information for database entries
    • Testing and checks based on the list approved by the study sponsor
  • Study conduct phase should include:
    • CRF tracking
      • Logistic mechanism for paper-based study
      • Electronic data capture (EDC) for eCRF
    • Data entry or transferring data from a CRF to the CDMS
      • Single data entry—performed by a single person
      • Double data entry—entered twice by different people
    • Discrepancy management—cleaning the subject data in the CDMS, including:
      • Checks—manual and programmed
      • Resolution—as per self-evident correction or internal ruling from the site (via a data clarification form or DCF)
    • Data coding using approved dictionaries, such as:
      • MedDRA or the Medical Dictionary for Regulatory Activities
      • WHODrug Global or World Health Organization Global
    • Data review and ongoing quality control
    • Data transfer
    • Data import protocols
    • Sponsor submissions
  • Study closeout phase should include:
    • SAE reconciliation
      • Compare safety variables in the CDMS with the study sponsor’s pharmacovigilance (PV)
      • Confirm that events are consistent in both systems
    • Quality control
      • Maintain for the overall study
      • Include quality checks at intervals for all data throughout the study
      • Ensure that all data processed is accurate, clean, and correct
      • Generate and review data inconsistency listings
      • Confirm randomization schedules
      • Resolve data queries
      • Produce data reports
      • Review data analysis metrics
    • Database lock
      • Data lock checklist
      • Ensure that there is no manipulation of study data during the final analysis
      • Lock once all data management activities are complete, including:
        • All discrepancies resolved
        • Data clarification forms (DCFs) received
        • Coding complete
        • SAE reconciliation process complete
      • Database prepared for transfer to designated biostatisticians, according to data export protocols and specified data file format(s)
    • Database maintenance and archival
    • Final clinical study report

Security procedures to be followed for clinical data management include:

  • Audit trails of server and record access
  • Data backups
  • Interval changes for user passwords
  • Restoration of data
  • Secure storage
  • Specific access roles
  • Track modifications made to data and files

Roles in Clinical Data Management

There are many roles in clinical data management. While the roles and responsibilities vary for each study, the following are roles that are considered the minimum requirements for a clinical data management team.

  • Investigators and clinicians collect data during the study using case report forms
  • Project manager supervises the overall conduct of the trial
  • Data manager leads the clinical data management team and:
    • Acts as a key player in early discussions about data collection options
    • Assures overall accuracy and integrity of the clinical trial data
    • Coordinates data management activities
    • Handles and verifies the data
    • Manages data validation
    • Oversees the application of quality control procedures
    • Supervises the entire clinical data management process
    • Takes responsibility for database locks
  • Clinical data analyst designs the case report form and prepares the CRF completion instructions, also referred to as CRF completion guidelines (CCGs)
  • Database programmer, or database designer:
    • Creates the study database
    • Annotates the CRF
    • Programs the edit checks for data validation
    • Validates the edit checks with a dummy dataset
  • Site personnel and data entry personnel enter the data into the database following receipt of paper CRF pages (as needed)
  • Biostatisticians conduct statistical analysis of study data
  • Medical writer prepares study reports

Clinical Data Management Tools

Clinical Trial Management System

One of the clinical data management tools most commonly used is a clinical trial management system (CTMS). This is a type of project management software designed to meet the unique requirements of clinical research and clinical data management.

A CTMS centralizes planning, reporting, and tracking of all aspects of clinical trials by a single organization or a group, which makes them more efficient, compliant, and successful.

Key considerations when evaluating a clinical data management system are:

  • Adverse and severe adverse event (AE and SAE) tracking
  • Calendar
  • Compliance—e.g., 21 CFR Part 11, HIPAA
  • Document management
  • Ease of use
  • Electronic data capture (EDC)
  • Enrollment management, including randomization
  • Installation—cloud-based or on-premises solution
  • Minimum number of users
  • Mobile capabilities—support for iOS and Android
  • Patient database
  • Payment capabilities
  • Pricing
  • Recruiting management
  • Scheduling capabilities
  • Site communication
  • Study planning and workflows
  • Support

Benefits of a clinical data management system include:

  • Controlled, standardized data repository
  • Centralized data management and analysis
  • Reduced operations costs for both IT and the business
  • Increased process efficiency
  • Improved submission quality
  • Conformity with defined standards
  • Access to a single source of truth system
  • Consistent automated data collection
  • Assurance of regulatory compliance
  • Accelerates time to market

Clinical Data Management Plan

Another important tool is the clinical data management plan, which outlines all the data management work needed in a clinical research project. It should be comprehensive and agreed upon by all stakeholders.

The clinical data management plan incorporates the timeline, milestones, deliverables, and strategies for how the data manager should handle disparate data sets. It includes:

  • Detailed database design specifications (DDS)
  • Database development checklist
  • Data management plans
  • Risk analysis assessment
  • Database validation plan
  • User acceptance test plan
  • Data processing guidelines
  • CRF review
  • Data cleaning guidelines
  • Validation reports

CDM Regulations, Guidelines, and Standards

Clinical data management must follow a number of regulations, guidelines, and standards, including the following.

Clinical Data Interchange Standards Consortium (CDISC)

Adherence to CDISC standards is required by the United States Food and Drug Administration (FDA) and Japan’s Pharmaceuticals and Medical Devices Agency (PMDA). It is recommended by the China National Medical Products Administration (NMPA) and has been adopted by the world’s leading research organizations.

CDISC has two standards that help ensure the accessibility, interoperability, and reusability of data.

  • Study Data Tabulation Model Implementation Guide for Human Clinical Trials (SDTMIG)
  • Clinical Data Acquisition Standards Harmonization (CDASH)

Good Clinical Data Management Practices (GCDMP)

The GCDMP guidelines come from the Society for Clinical Data Management (SCDM), which also administers the International Association for Continuing Education and Training (IACET) credential for certified clinical data managers.

National Accreditations Board of Hospitals Health (NABH)

NABH guidance includes pharmaceutical study auditing checklists.

Good Clinical Practice (GCP)

GCP guidelines include ethical and quality standards in clinical research. Specific parts of the GCP that apply to clinical data management are:

  • 2.10, 5.5.3 a-d—trial management, data handling, record keeping
  • 2.11; 5.5.3 g—subject and data confidentiality
  • 4.11—safety reporting
  • 4.9.1; 4.9.3; 5.1.3—quality control
  • 5.21; 5.22—records and reporting
  • 5.5.4—monitoring

Code of Federal Regulations (CFR) or 21 CFR Part 11

21 CFR 11 details the rules that are required to ensure that electronic records adhere to the ALCOA standard for data integrity (i.e., attributable, legible, original, and accurate). It applies to all data in an electronic record that will be submitted to the FDA.

The scope of 21 CFR 11 includes:

  • Accounting for legacy systems and databases
  • Audit trail for corrections in the database
  • Copies of records
  • Record retention
  • Validation of databases

Clinical Data Management Process

Before the study begins, the following items need to be developed:

  • A case report form (CRF) for data collection from protocol-specific activities
  • The data management plan (DMP) with details about data handling according to the protocol
  • The data validation manual (DVM), which contains the edit check programs for discrepancy identification
  • Coding dictionaries with all medical terms that will be used as part of the data collection

After the study begins, steps in the clinical data management process include:

  • Generate source data, such as clinical site medical records, laboratory results, and patient diaries.
  • Enter data from the CRFs and other source data into the clinical trial database (paper CRFs; electronic CRFs (eCRFs) allow data to be entered directly into the database from source documents). 
  • Check data for accuracy, quality, and completeness.
  • Validate data with queries to the clinical site if needed.
  • Use the pre-lock checklist to confirm that all necessary activities have been completed.
  • Lock the database when all data has been validated.
  • Reformat data for reporting and analysis, including generating tables, listings, and figures.
  • Analyze data and create reports based on the results of the analysis.
  • Integrate results into other materials, such as investigator’s brochures (IBs) and clinical study reports (CSRs).
  • Archive the database and other study data.

Clinical Data Management—A Guidepost for Study Integrity

Clinical data management provides guidance that ensures data integrity from before a clinical study begins to the time the results are archived. Taking time to follow the clinical data management processes that weave through an entire clinical study results in high-integrity data for use in research and reporting.

Egnyte has experts ready to answer your questions. For more than a decade, Egnyte has helped more than 16,000 customers with millions of customers worldwide.

Last Updated: 17th October, 2021

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