Clinical data management (CDM) encompasses the collection, cleaning, and management of clinical trial data to ensure the integrity and accuracy required for regulatory submissions. A comprehensive data management plan details how data will be handled throughout the study, covering database structure, validation procedures, and data review processes. The role of CDM professionals is crucial in adhering to quality standards, maintaining patient confidentiality, and supporting effective communication within clinical research teams.

1. CLINICAL
DATA
MANAGEMEN
T

2. CLINICAL DATA MANAGEMENT
INTRODUCTION:
Clinical Data Management is involved in all aspects of processing the clinical data, working with a range of
computer applications, and database systems to support the collection, cleaning, and management of subject or
trial data.
Data Management in clinical research relates to the processes of gathering, capturing, monitoring, analyzing,
and reporting on data.
Data management begins with the development of the data management plan and design of the data capture
instrument (e.g. the case report form), continues with data collection and regular quality control procedures,
the database cleaning, locking, and ends with the analysis, archiving and write-up.

3. IMPORTANCE OF CDM
• The objectives of good clinical data management are
to ensure that the study database is: An accurate and
true representation of what took place in the study
and sufficiently clean to support the statistical
analysis and its interpretation.
• Review & approval of new drugs by Regulatory
Agencies is dependent upon a trust that clinical trials
data presented are of sufficient integrity to ensure
confidence in results & conclusions presented by a
pharmaceutical company.
• Important to obtaining that trust is adherence to
quality standards & practices.
• Hence, companies must assure that all staff involved
in the clinical research are trained & qualified to
perform data management tasks.

4. THE FLOW OF ACTIVITIES IN A CLINICAL
TRIAL
Regulatory submission
Analyse & Clinical study report
Database lock
Data review & Discrepancy Management
Data entry
CRF scanning & Indexing
Data collection
Conduct of the data
Data Management Plan:
DMP should give a complete picture of how the
data will be handled throughout the study by
outlining all the information relating to the
study's data management procedures.
It should include:
• database structure specifications
• A description of the database building and
testing procedures
• A list of SOPs foe the data management
process which will be used to ensure
consistency
• A description of how the data will be
reviewed and information about how changes
in data will be managed.
• details of how the data will be coded,
analyzed and achieved.

5. Data
management
Tools
CRF
Commo
n data
element
s
Electron
ic data
capture
Audits
Database designing:
• Plan the way the data is to be
structured
• Data sets defined and coded with
alphanumeric codes
• These codes are annotated to the
blank CRF
• Based on the protocol requirements
the validation rules are created
• A validation plan is written and
reviewed by the QC group ad the
Statistician

6. ESSENTIAL CHARACTERS OF DATABASE
 Security
 Continuity
 Audit trails
 Null values
 Reporting
 In-Built data validation
 System validation
 CRF receipt, checking, & tracking
STANDARD GLOBAL DICTIONARIES:
 MedDRA: Medical Dictionary for Regulatory
activities
 WHO-ART: World Health organization
Adverse Reaction Terms
 CTCAE v3.0: Common Terminology Criteria
for Adverse Events

7. DATA REVIEW AND VALIDATION
• Data cleaning or validation refers to a collection of activities by data management,
used to assure validity and accuracy of the clinical data.
• It comprises both logical and statistical checks to detect impossible values due to data
entry errors, coding, and inconsistent data.
• Point-by-point check
• Missing data/blank field checks
• Data consistency checks
• Lab data range checks
• Header inconsistency checks
• Protocol violation checks
• External data checks
• Textual data checks
• SAE reconciliation

8. Data Management
Issues Data clarification
forms (DCF)
Site Investigator cross
checks with Source
Document and answers the
query forms
Data Management updates
database
All CRFs are
received in-house
All CRFs are
entered and
verified
All e-Data received
& integrated into
database
All lab ranges
applied
All edit checks
performed &
discrepancies
resolved
All queries
resolved &
integrated
Ongoing QC &
review of coding
performed
Closure activities
performed
QA audit
completed
Database Locked
QUERY GENERATION DATA BASE LOCK

9. WHY TECHNOLOGY
IN CDM
• More automation reduces
manual input
• Allows processes to be linked
• Increases processing speed
• Stores large volumes of data
• Enables global studies
• Automates tracking of
processes
• Eliminates/ simplifies steps
in the process
• Reduces chance of human
error
Clinical Data Management Professionals are:
 Committed to following the laws & guidelines applicable
to clinical research, participating in the protection of the
safety, dignity & well-being of patients & to maintaining
the confidentiality of medical records.
 Committed to creating, maintaining & presenting quality
clinical data, thus supporting accurate & timely
statistical analysis, & to adhere to applicable standards
of quality &truthfulness in scientific research.
 Committed to facilitating communication between
clinical data management professionals & all other
clinical research professionals, maintaining competency
in all areas of clinical data management, keeping current
with technological advances, & ensuring the
dissemination of information to members of all clinical
research teams.
 Committed to working as an integral member of a
clinical research team with honesty, integrity, & respect.

10. AKSHATA KAWASTE
STUDENT OF
ELITE INSTITUTE OF PHARMA SKILLS