Clinical data management involves collecting accurate and complete data during clinical trials. It includes designing case report forms, collecting data, tracking forms, entering data, validating data for errors, resolving discrepancies, medical coding, and locking the database. The goal is to ensure data is managed according to good clinical data management practices and regulatory standards.

1. GOOD CLINICAL DATA
MANAGEMENT PRACTICE
Presented by
Dr. Ankita Suman

2. WHAT ARE DATA?
• Information (facts /figures)
• An accounting of the study
Who Can Collect Data?
• Investigators
• Nurses
• Research team
• Participant
• Participant’s family
• Participant’s own physician

3. Where Are Data?
In the source documents
What is a Source Document?
• It is the First Recording
• What does it tell?
1. It is the data that document the trial
2. Study was carried out according to
protocol
• Source Documents
• Original Lab reports
• Pathology reports
• Surgical reports
• Physician Progress Notes
• Nurses Notes
• Medical Record

4. What Do You Collect?
• Biographical data
• Eligibility
• Study agent given
• Concurrent therapy
• Assessments/tests/exams
• Adverse Events
• Response according to protocol

5. What is clinical data management?
 Clinical data management is the process of ensuring that
data collected during the course of a clinical trial is :
 Accurate
 Complete
 Logical
 Consistent
Good Clinical Data Management Practice (GCDMP):
the current industry standards for clinical data management
that consist of best business practice and acceptable
regulatory standards.

6.  In all phases of clinical and laboratory information must be
collected and converted to digital form for reporting trials.
 GCDMP is the version no. 4 of The Society of Clinical Data
Management (SCDM).
 GCDMP provides guidance on the accepted practices in CDM
that are consistent with regulatory practices. Addressed in 20
chapters, it covers the CDM process by highlighting the
minimum standards and best practices.
 Chapters addresses each aspect of CDM
 CRF printing
 Data privacy
 Laboratory &other external data
 Measuring data quality.

7.  BASIC COMPONENTS OF DATA
MANAGEMENT:
Receive
Entry
Verification
Coding
Locked data base

8. Organization of DM dept
 Types of DM personnel varies by organization.
 Data entry specialists
 Clinical data coordinator
 Data manager
 Database manager
 Programmer
 CRF designer
 Coder

9. Managing the Data
• Set up plan early
• Plan CRFs to capture all needed data
• Collect data as it happens
• Standardize data entry procedures

10. Data Management Tools
• Case Report Forms
• Common Data Elements
• Electronic Data Capture
• Audits

11. Data Quality
Data entry procedures
• Certification of data entry personnel
• Edit checks
• Ongoing quality checks
• Different personnel QC data entries
• Correction of errors
• Data lock

12. Lists of CDM activities
 Database designing
 Databases are the clinical software applications. which
are built to facilitate the CDM tasks to carry out
multiple studies. Study details like objectives,
intervals, visits, investigators, sites, and patients are
defined in the database and CRF layouts are designed
for data entry.
 Data collection
 Data collection is done using the CRF that may exist in
the form of a paper or an electronic version.

13.  CRF tracking
 The entries made in the CRF will be monitored by the
Clinical Research Associate (CRA) for completeness
and filled up CRFs are retrieved and handed over to
the CDM team. The CDM team will track the retrieved
CRFs and maintain their record. CRFs are tracked for
missing pages and illegible data manually to assure
that the data are not lost.

14. Data entry
• Data entry takes place according to the guidelines.
• Applicable only in the case of paper CRF retrieved from the
sites.
•Data validation
•the process of testing the validity of data in accordance with
the protocol specifications.
•Edit check programs are written to identify the
discrepancies in the entered data, which are embedded in the
database, to ensure data validity.

15. • Discrepancy management
• This is also called query resolution.
• Discrepancy management includes reviewing
discrepancies, investigating the reason, and resolving
them with documentary proof or declaring them as
irresolvable.
• Discrepancy management helps in cleaning the data
and gathers enough evidence for the deviations
observed in data.
 Medical coding
 Medical coding helps in identifying and properly
classifying the medical terminologies associated with
the clinical trial.

16.  Commonly, Medical Dictionary for Regulatory Activities
(MedDRA) is used for the coding of adverse events as well as other
illnesses and World Health Organization–Drug Dictionary
Enhanced (WHO-DDE) is used for coding the medications.
 Medical coding helps in classifying reported medical terms on the
CRF to standard dictionary terms in order to achieve data
consistency and avoid unnecessary duplication.
 Database locking
 After a proper quality check and assurance, the final data
validation is run. If there are no discrepancies, the SAS datasets
are finalized in consultation with the statistician. This is done as
the database cannot be changed in any manner after locking.