What is Clinical Data Management?

The life sciences industry is one of the most rigorously regulated sectors globally, prioritising patient safety during clinical trials and post-marketing phases. All trials must comply with country-specific regulations set by regulatory bodies. When relying on clinical research to introduce new products to the market, adhering to regulations like Good Laboratory Practice (GLP), Good Clinical Practice (GCP), and CFR Part 11 is mandatory. Clinical data, encompassing information for developing and maintaining software systems, databases, processes, and protocols, is crucial for these studies.

Clinical Data Management (CDM) ensures data integrity throughout the research, making sure datasets are accurate, secure, reliable, and ready for analysis. By using a systematic process, a CDM team are responsible for collecting, entering, cleaning and processing information gathered during a clinical research project, and ensure the accuracy, completeness and consistency of the clinical trial data, adhering to regulatory standards and guidelines.

The main goal of CDM is to ensure the validity and reliability of the data, so it's ready for regulatory submission and subsequent analysis. It serves as the backbone for producing high-quality, statistically sound results that inform medical decisions and regulatory approvals.

Regulatory compliance is a fundamental aspect of CDM, ensuring that all collected data adheres to stringent regulatory standards and guidelines set by authorities such as the FDA and EMA. By maintaining records and implementing robust validation processes, CDM guarantees that the data adheres to regulatory requirements to not only facilitate the approval process for new treatments and interventions but to also uphold the integrity of the research. The emphasis on data quality is paramount, as it directly impacts the credibility of research outcomes. High-quality data ensures that the results of clinical trials are statistically sound and dependable, forming a solid foundation for medical decisions and ultimately advancing patient care.

In summary the purpose of Clinical Data Management is to:

• Ensuring Data Quality and Integrity
• Compliance with Regulatory Standards
• Data Collection and Management
• Facilitating Accurate analysis and Reporting

What Work is Done in Clinical Data Management?

The Data Manager will manage the clinical trial data collection, processing, and analysis. Clinical Data Managers work closely with clinical research team members, including clinical research associates, medical monitors, pharmacovigilance, and biostatisticians to ensure that all aspects of the data management process are executed efficiently and accurately.

Here are the main activities in clinical data management:

• Data Management Plan (DMP) Development
• Database Design and Setup
• Data Capture
• Data Validation
• Discrepancy Management
• Medical Coding
• Database Lock

Essential Skills for Effective Clinical Data Managers

Here are some essential skills for Data Managers to ensure the successful capture, clean and lock of clinical trial data, contributing to the efficacy and effectiveness of clinical research outcomes:

• Technical Proficiency
• Analytical Skills
• Attention to Detail
• Regulatory and Compliance Knowledge
• Communication and Collaboration
• Project Management Skills
• Quality Control and Assurance
• Ethical and Confidentiality Awareness

What Roles Are Involved in Clinical Data Management (CDM)?

1. Clinical Data Manger

Clinical Data Managers supervise the entire data management process for clinical trials. Their primary goal is to ensure the integrity and accuracy of the clinical data.

2. Database Programmer

Database programmers create the database, design Case Report Form (CRF) screens, performs CRF annotations to ensure that data fields are clearly defined and mapped to the database, programs, and validates the edit checks for data validation.

3. Clinical Data Coordinator

Clinical Data Coordinators assists Clinical Data Managers with data management related activities like CRF designing, query management, data review, vendor reconciliations, database lock and supports in preparation of data management documents.

4. Medical Coder

Medical Coder reviews and codes variations such as medical history, adverse events and medications into standardised codes using coding dictionaries like MedDRA (Medical Dictionary for Regulatory Activities) and WHO Drug (World Health Organization Drug Dictionary).

5. Clinical Research Associate (CRA)

Clinical Research Associates monitor the clinical trials process to ensure compliance with study protocol, they conduct site visits and perform source data verification and they are in charge of making sure the clinical trials run smoothly, monitoring all the procedures, processes, and results, ensuring the researchers are following established guidelines and protocols every step of the way.

6. Investigator

Investigators are responsible for the conduct and oversight of clinical trials at their respective sites.

7. Clinical Research Coordinator (CRC)

Clinical Research Coordinators manage the day-to-day operations of clinical trials at the study site. They handle patient recruitment, informed consent, data collection and adherence to the study protocol. They are responsible for entering data from Case Report Forms (CRFs) into electronic databases.

8. Biostatistician

Biostatisticians are responsible for designing the statistical aspect of clinical trials and analysing the study data.

9. Medical Writer

Medical Writer is responsible to create and maintain wide spectrum documents such as Protocol writing, Investigator’s brochures, Informed consent forms, Clinical study reports, regulatory submissions and scientific publications.

Stages of Clinical Data Management

Clinical Data Management consists of three primary stages of Start up, Conduct and Close out.

Start-up Stage

• Interpreting Protocol: Reviewing the protocol to understand the study primary objectives, secondary objectives, safety endpoints and data requirements.
• Development of Data Management Plan (DMP): The Data Management Plan (DMP) is a comprehensive document that outlines all data management activities, standards, and procedures for a clinical trial. It details the processes, standards and responsibilities necessary to ensure data integrity and compliance with regulatory requirements.
• CRF Design and Development: Designing Case Report Forms (CRFs) or electronic CRFs (eCRFs) as per protocol requirement to capture all required data systematically.

• Database Design and Development: Developing the clinical database to store and manage collected data. This process ensures that the database is built according to protocol requirements and regulatory standards, ensuring data accuracy, integrity and accessibility. Key activities involved in developing the database design are:
  • Creating Database Specifications: Database specifications detail the requirements and structure of the database, serving as a blueprint for its development.
  • Designing the Database: Database is developed based on the specifications provided in the database specification document within the Clinical Data Management System (CDMS) such as Medidata Rave, Veeva, Inform etc.
• Edit Check Specification Development: Edit checks are automated validation checks applied to data entry to ensure data quality, consistency, and integrity throughout a clinical trial or study. These checks are designed to flag errors or inconsistencies in the data at the point of entry, thereby facilitating the correction and maintaining the accuracy of the dataset. Some of the examples of edit checks are range checks, mandatory fields checks, data format checks, future date checks, cross-form checks, duplicate checks and logical checks.
• Testing the Database: This ensures it functions correctly, meets protocol requirements, and adheres to regulatory standards. Key activities involved in testing the database are:
  • Database Modules Testing: Testing individual components such as CRF forms, fields, visits, dynamics, roles and derivations, to ensure they function as expected and verifying each field captures data correctly as expected.
  • Edit Check Testing: Testing of edit checks to ensure edit checks function as intended without generating false positives or missing errors.
  • Medical Coding: Testing individual components of the medical coding process to ensure they function correctly and to ensure that coding logic accurately maps clinical terms to standardised codes.
  • Integration: Testing the interactions between integrated components or modules to ensure they work as expected. Some of the examples of integrations system testing are ePRO, eCOA, lab modules and safety gateway.
  • Audit trail: Audit trail testing is performed to ensure that all changes to data are accurately recorded, preserving the original data and documenting modifications with accurate and precise timestamps.
• User Acceptance Testing (UAT): Conducting UAT with end users such as Data Managers, Medical Coders, Clinical Research Associates (CRA’s) and external stakeholders to ensure the database meets their needs and expectations. Collecting the feedback from end users and identifying any issues or areas for improvement and making the necessary adjustments or updates to the database based on user input.
• Validation and Documentation: Documenting all testing procedures, results and any modification made to the database and ensuring the database design and testing processes are fully documented to meet regulatory requirements.
• eCRF Completion Guidelines: This provides detailed instructions for completing electronic Case Report Forms (eCRFs), ensuring consistent and accurate data entry across all study sites.
• Data Validation Plan: It outlines the procedures and criteria for validating the data collected during the clinical trial.
• Data Transfer Agreement/Specification: Specifications for external vendors outline the requirements and expectations for third-party service providers involved in the clinical trial such as laboratory services, eCOA and ePRO.

Conduct Stage

The conduct phase is a critical period in the clinical trial lifecycle, where the focus is on ensuring the accurate collection of data and monitoring of clinical trial data to support the study outcomes and regulatory requirements. Conduct phase consists of activities such as:

• Data Collection and Entry: Data collection involves gathering information from various sources such as patient records, laboratory tests, and clinical observations during a clinical trial. Modern CDM systems often use Electronic Data Capture (EDC) tools to streamline data collection. EDC systems reduce errors associated with manual data entry, enhance data accuracy and facilitate real-time data access.

• Data Cleaning and Validation: The data cleaning and validation stage involves a meticulous process to ensure data integrity and readiness for analysis. By effectively managing queries, reviewing data, reconciling external vendor data, SAE reconciliation and medical coding, clinical data management teams can uphold the highest standards of data quality. These activities are essential for generating reliable and credible results in clinical research, supporting regulatory submissions and contributing to advancement in medical research and patient care. Below are the key activities involved in this stage:
  • Discrepancy Management: The process of identifying, generating and resolving queries related to discrepancies, inconsistencies, missing data or error in the collected data.
  • Data Review: Regularly review of entered data and performing quality checks to ensure accuracy, completeness and compliance with protocol requirements.
  • SAE Reconciliation: The process of ensuring that all Serious Adverse Events (SAEs) are reported in the clinical database are consistent with those reported in the safety database by comparing and addressing the discrepancies and documenting the reconciliation process, including discrepancies identified, queries raised and resolutions. This documentation is crucial for audit trails and regulatory inspections.
  • External Vendor Reconciliation: The process of reviewing and reconciling the data from the clinical database with the data from external vendor such as central laboratory or electronic patient-reported outcomes (ePRO) to ensure data consistency and completeness by identifying discrepancies and resolving the differences.
  • Medical Coding Using MedRA and WHODDE Dictionaries: Medical coding is the classification of multiple verbatim terms, using a validated medical dictionary supplied by the customer under license by the relevant licensing bodies (MSSO, Uppsala), to produce a statistically quantifiable count of all similar terms in a clinical database.

Close out Stage

Once the study enters the last patient last visit, the study close-out process will take place. This phase involves several key activities aimed at ensuring the completeness, accuracy and integrity of the collected data and preparing for the study closure and ready for regulatory submission. Each of these closeout phase activities plays a crucial role in the overall clinical data management process. The close out stage consist of the following activities:

• Database Quality Control: This process involves a detailed review of all data entries to ensure accuracy, completeness and consistency. Quality control processes include final data review, addressing any remaining discrepancies and confirming that all data points are correctly captured in the clinical database.
• Medical Coding Review and Approval: During the closeout phase, final medical coding review and approval is conducted to ensure that all terms are accurately coded and consistent with regulatory standards.
• SAE Reconciliation and Approval: The final SAE reconciliation process involves verifying that all reported SAEs are correctly documented and reconciled between the clinical and safety databases. Approval of this reconciliation ensures that all safety data is accurate and complete.
• Database Lock Activities: Closing or locking the database is fundamental for preventing inadvertent or unauthorised to the data. Data Manager will initiate and complete Database lock checklist and performs quality control checks to ensure database ready is ready for locking. Once all necessary approvals for the database lock are in place, the process of locking the database can proceed. The database lock signifies that the data is ready for final analysis and regulatory submission.
• Post-Lock Data Extract and Transfer: After the database is locked, data extracts are performed for final analysis and reporting. This involves secure and accurate transferring of data to statistical analysis systems (SAS) or external stakeholders by maintaining the integrity of the dataset.
• Data Archiving: Data archiving is the process of securely storing all trial related data and documentation. This ensures that the data remains accessible and intact for future reference and regulatory inspections.
• Decommissioning Activities: Decommissioning involves the systematic shutdown of study systems and applications used during the clinical trial. This includes securely transfer of study media to sponsor and sites, archiving all data, removing the systems from active use and retiring systems in accordance with organisational and regulatory guidelines.

The Role of CDASH and CDISC in Clinical Data Management

To further enhance the effectiveness and reliability of clinical data management, industry standard such as CDASH (Clinical Data Acquisition Standards Harmonization) and CDISC (Clinical Data Interchange Standards Consortium) play a pivotal role. These standards ensure that data collected during clinical trials is consistent, accurate and easily interpretable, thus supporting regulatory compliance and facilitating seamless data integration and analysis.

What is a Clinical Data Management System?

Clinical data management system or (CDMS) is a tool used in clinical research to manage the data of a clinical trial. These systems ensure data accuracy, integrity and compliance with regulatory requirements.

Key components of a Clinical Data Management System (CDMS) are:

1. Database Management
2. Data Cleaning and Validation
3. Data Integration
4. Reporting and Analysis
5. Audit Trails
6. Security Features
   
   

Tools used in Clinical data management system (CDMS):

• Electronic Data Capture (EDC): EDC systems are designed to streamline the process of collecting and managing clinical trial data electronically, replacing traditional paper-methods. This transition to digital data capture offers numerous advantages, including improved data accuracy, real-time access to data and enhanced regulatory compliance. Most used EDC systems are Medidata Rave, Oracle Clinical, Veeva, Merative, Veidoc.
• Electronic Patient Reported Outcomes (ePRO): ePRO systems allows patients to record electronically and submit their health outcomes digitally in real time using devices such as smartphones, tablets or computers.
• Interactive Response Technology (IRT): IRT systems are used to manage patient enrolment, randomisation and drug dispensation in clinical trials.
• Randomisation and Trial Supply Management (RTSM): RTSM system are used to manage patient randomisation and supply of investigational products during clinical trial.
• Safety Gateway: Safety gateway systems are integral in managing and reporting adverse events. These systems streamline the collection, assessment, and regulatory reporting of safety information ensuring safety concerns are promptly addressed and regulatory requirements are met.
• Coding Application: Coding systems standardise the classification of medical terms used in clinical trials, such as adverse events and medication.
• Laboratory Data Integration: Lab modules integration system ensures that lab data is seamlessly transferred and managed within the clinical trial database, maintaining data integrity and compliance.

The Future of Clinical Data Management

With technological advancements like artificial intelligence (AI), machine learning (ML) and medical devices, CDM is going through a huge transformation in the drive towards a digital age of real-time data, collection and management. Clinical data management systems will need to adapt to manage diverse data sources and ensure seamless integration and consistency across decentralised trial frameworks.

The future of clinical data management is poised to be shaped by several trends and technologies:

• Artificial Intelligence and Machine Learning
• Patient-Centric Data Collection
• Cloud Computing and Big Data Analytics
• Decentralised Clinical Trials (DCTs)

By embracing these innovations, clinical data management will not only become more efficient and effective, but will also play a crucial role in advancing precision medicine and improving patient outcomes in clinical research.

Improve the accuracy and efficiency of your clinical trial data

Quanticate’s Clinical Data Management Team are dedicated to ensuring high quality clinical data and have a wealth of experience in data capture, processing and collection tools. Our team offer flexible and customised solutions across various unified platforms, including EDC's. If you would like more information on how we can assist your clinical trial submit an RFI.