Regulated industries, such as pharmaceutical, medical device, and life science industries, need clinical research studies to market new products. These studies are controlled by country- or region-specific regulations. The most common regulations are GLP, GCP, and CFR Part 11. But the list can be extensive.
Clinical data contains information for developing and sustaining software systems, databases, processes, procedures, training, and protocols. Clinical data management enables organizations to maintain data integrity throughout the duration of a clinical research study. Correct data management ensures that a dataset is accurate, secure, reliable, and ready for analysis.
Why is CDM important?
Clinical research studies provide crucial information. For example, results should demonstrate that a product is safe and meets requirements. Clinical data management provides:
- Assurance of data quality
- Accelerated development
- Protection from data loss
- Reduced expenses
- Security
- Complete and accurate collection of data
- A clean dataset to support statistical analysis and reporting
- A formatted dataset for optimal and timely usability
- Assurance of data integrity and quality during database transfer
- True representation of the trial in the study database
The five stages of CDM
Clinical data management consists of five stages, which span data collection, archiving, and presentation. The workflow starts when the CDM team generates a case report form (CRF) and ends when the database locks. The data manager executes quality checks and data cleaning throughout the workflow.
| Stages |
1 |
2 |
3 |
4 |
5 |
| CRF design |
Database design |
Data mapping |
Study conduct |
Study closeout |
| Definition |
This stage comprises the design of the CRF, which guides data collection. |
The database should have space for the study data. |
This stage combines data that comes in various formats, which enables researchers to report continuously. |
This stage includes activities associated with SAEs and potential events, which should be reviewed and corrected by the data manager. |
Once a study is complete, the data manager should lock the database so that no one can change the data. |
| Activities |
Adverse effect (AE) forms
Severe adverse effect (SAE) forms
Concomitant therapy forms
Eligibility screenings
Follow-up visits
Lab test forms
Medical histories
Physical exams and vitals
Randomization
Status evaluations
|
Automated edit checks
Backend tables
Data stored in the
CDM system (CDMS)
Study-specific data entry fields and screens
|
Data entry assessment
Data inconsistency detection
Testing performed at the site
Data entry screens and programming testing using subject information
Testing and checks based on the list approved by the study sponsor
|
CRF tracking
Data entry or data transfer from
the CRF to the CDMS
Discrepancy management
Data coding
Data review and ongoing quality control
Data transfer
>Data import protocols
Sponsor submissions
SAE reconciliation
|
Quality control
Database lock
Database maintenance and archiving
Final study report
|
Roles and responsibilities
Depending on the study, roles and responsibilities can differ.
Data manager
(project manager) |
Supervises the CDM team throughout the process
Acts as a key player in discussions about data collection
Ensures the accuracy and integrity of the study data
Coordinates data management activities
Handles and verifies the data
Manages data validation
Oversees the application of quality control procedures
Takes responsibility for database locks
|
Database programmer
or designer |
Performs the CRF annotation
Creates the study database
Designs the data entry screens
Programs the edit checks for data validation
Validates the edit checks with a dummy dataset
|
| Medical coder |
Codes variations, such as adverse events and medical histories |
| Clinical data coordinator |
Designs the CRF
Creates the CRF instructions (CRF completion guidelines)
Creates the discrepancy protocols
|
| Quality control associate |
Checks the accuracy of data entry and performs data audits |
| Data entry associate |
Tracks receipt of the CRF pages and enters the data into the database |
| Investigators and clinicians |
Collect the data during the study using CRFs |
| Site and data personnel |
Enter the data into the database following receipt of the CRF pages |
| Biostatisticians |
Conduct statistical analysis of the study data |
| Medical writers |
Prepare the study reports |
Tools for CDM
Managing clinical data while keeping it secure and accessible is a challenge. Thankfully, using a CDM plan (DMP) or CDMS makes the task easier.
What is a DMP?
Clinical DMPs contain all of the work that is needed for a clinical research study. A comprehensive DMP should be generated and agreed upon by all parties. It should include milestones, deliverables, timelines, and industry-related data standards, such as The Clinical Data Acquisitions Standards Harmonization (CDASH). The CDASH specifies 16 standards for maintaining consistent data collection across studies. A DMP should be a living document so that it can be updated during a clinical research study. Organizations can make DMPs a part of their QMS documentation by using a controlled DMP template that fits their studies. A template can contain the following details:
- Database design specifications
- Data management plans
- Database development checklists
- Database validation plans
- Risk analysis assessments
- User acceptance test plans
- Data processing guidelines
- CRF reviews
- Data cleaning guidelines
- Validation reports
What is a CDMS?
Clinical data management systems, also known as clinical trial management systems (CTMS), are designed to help clinical research studies meet CDM requirements. A CDMS or CTMS allows for in-house planning, reporting, and tracking. Therefore, clinical research studies can collect more efficient, compliant, and successful results. Companies use these systems to collect, integrate, and validate data. A CDMS or CTMS offers the following advantages:
- Build trust with regulatory authorities
- Monitor data remotely
- Incorporate artificial intelligence
- Balance risk reduction and lead time
- Use module-based programming, which allows users more functionality
