What is Clinical Data Management (CDM)?
Process of ensuring that the data collected during a clinical trial:
- Accurate = Conforms to truth or facts
- Logical = Legitimate
Begins with design of data capture instrument & data collection, continues with data QC procedures to assure quality of all aspects of process, & ends with database finalization.
All data should be recorded, handled and stored in a way that allows its accurate reporting, interpretation and verification (GCP 2.10)
Data transformed during processing it should always be possible to compare the original data and observations with the processed data (ICH GCP 5.5.4).
India is poised to see tremendous growth in this field. The high availability of trainable talent is helping fuel this growth. Higher government and private investments in training students in the field of clinical research during the graduate and post-graduate curricula will help reduce the training costs that are borne by the industry today. With high growth expected, this would be a key area of focus for industry and academia alike.
CDM has evolved from a data entry process into a diverse process.
Provides clean data in a useable format in a timely manner and a database fit for use.
Ensures the data are clean & database is ready to lock.
Now CDM manages:
- entry of CRF data.
- merging of non-CRF data.
- systems & processes designed to identify bad data (e.g. database design).
- generate & track CRFs & queries.
- determine protocol violators.
- interact with site personnel to resolve data issues.
CDM as a Science
Factors contributing to CDM as Science subject are:-
2. Growth predictions.
4. Need for a supporting infrastructure.
Role of CDM in overall drug development organization is continuing to evolve.
Relationships with other organizations are continuing to be defined & developed.
CDM is a very visible & strong organization now.
Considered as an integral, respected, highly valued member of clinical development team.
OTHER PLAYERS ASSISTING CDM PERSONNEL IN CLINICAL RESEARCH
Core CDM Processes
- Design of Data Validation Strategy
- Specification of Design
- Data Collection Tool Design (paper)
- Data Collection Tool Design (electronic)
- Forms Processing
- Data Entry
- Cleaning (manual clinical review & programmatic checks)
- Database Structure Specification
- Forms Management
- Data Archival (paper & electronic)
- LAB, SAFETY REPORTING & OTHER EXTERNAL DATA
- Data Transfers & Loads
- Database Reconciliation
- Quality Control Procedure
- Statistical Sampling
- Quantification of Database Quality
- Lock Criterion & Approval
- Breaking the Blind
- Handling of Post-lock Errata
- Range checks
- To identify inaccurate or invalid data & statistical outliers
- To ensure that data outside of permitted range are to be clarified & verified
To highlight area where data in database are inconsistent
To ensure completeness of data
- Design CRF along with protocol to assure collection of only data protocol specifies.
- Guidelines to collect data through independent means Design CRF with primary safety & efficacy endpoints in mind as main goal of data collection.
- Establish & maintain a library of standard forms CRF to be available for review at clinical site prior to approval.