The document discusses data quality management in clinical trials, outlining its definition, components, and the importance of ensuring data integrity and accuracy. It highlights various responsibilities of sponsors and investigators, including adhering to guidelines and maintaining quality assurance and control. Key challenges in data quality management are also addressed, emphasizing the need for proper monitoring of data practices throughout the research process.

1. Welcome
Data Quality Management in clinical trials
Name: Komal Vijay Pode
Qualification : MSC
Microbiology
Student ID :137/072023
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2. Index
• Defination
• Errors occurrence
• Components of Data Quality Management
• Quality Assurance and Quality Control
• Activity of QA, Components of QC
• Quality Guidelines for Data Quality Management in clinical trials
• Quality Management program and Goals of Quality Management
• Data Integrity
• Current Quality challenge in Data Quality Management
• Sponser Responsibilities in Data Quality Management
• Investigator Responsibilities in Data quality management
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3. Clinical Research
Clinical research is the study of health and illness in
people from which we learn how to prevent, diagnose
and treat illness.
• Data Quality: Data quality means the data which
meets criteria for accuracy, completeness, validity,
consistency, uniqueness, timeliness, and fitness for
particular purpose.
• Data quality management (DQM) is a formal process
for managing the quality, of the research data
captured throughout the study from the time it is
collected, stored, and transformed (processed)
through analysis and publication.
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4. Why does Error occur at any stage of
Clinical Research?
•Insufficient written procedure
•Written procedure not followed
•Training not done or incomplete
•Lack of ongoing checks to access errors
•Individual roles and responsibilities are unclear or
undefined.
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5. Components of Quality Management:
Quality management includes two main components of
Quality Management:
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6. Quality Assurance:
All those planned and systematic actions are established to
ensure that the trial is performed and the data are
generated, documented( recorded), and reported in
compliance with GCP and its applicable regulatory
requirements.
Quality control:
The operational techniques and activities are undertaken
within the quality assurance system to verify that the
requirements for the quality of the trial-related activities
have been fulfilled.
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7. Activities of Quality
Assurance
Components of
Quality control
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8. Quality Guidelines for Data Quality
Management (According to ICH GCP
guidelines)
2.10 All clinical trial information should be recorded, handled, and stored in a
way that allows accurate reporting, interpretation, and verification.
2.13 Systems with procedures that assure the quality of every aspect of the trial
should be implemented.
5.1.3 Quality control should be applied at each stage of data handling to
ensure that all data are reliable and have been processed correctly.
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9. Quality Management program :
A plan or system, including the structure and defined
responsibilities, which provides a framework for all
quality management activities, including quality control,
quality assurance, quality improvement, and the
reporting of these activities.
Goals of Quality Management :
•Improve the quality of study conduct
•Improve the quality and scientific validity of collected data
•Improve documentation practices
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10. What is Data integrity?
• Accurate: The recorded data should be correct, truthful, complete,
valid, reliable, and free from errors.
• Legible: The record created, especially the paper-based records
should be legible. The records should be permanent and not
erasable so that they are reliable throughout the data lifecycle.
• Original: the record is a true copy, Data is to be used or presented
as it was created.
• Attributable: the evidence or every piece of data entered into the
record must be capable of being traced back to the person
collecting it.
• Contemporaneous: the evidence of actions, events, or decisions
should be recorded as they take place or are generated.
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11. The current Quality challenge in
Data quality management:
• The ongoing challenge in managing the quality of clinical data is to
continually monitor data collection procedures and data management
practices at every level of the study. This includes:
• Ensuring that data generated during the study reflect what is specified
in the protocol (case report form[CRF] vs. protocol)
• Comparing data in the CRF and data collected in source documents for
accuracy (CRF vs. source documents).
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12. • Ensuring that the data analyzed are the data recorded in the CRF
(database vs. CRF).
• Data presented in tables, listings, and graphs (TLGs) correctly match data
in the database (TLGs vs. database)
• Data reported in the clinical study report (CSR) are the data analyzed (CSR
vs. TLGs)
• All aspects of the data management processes are compliant with SOPs
and GCPS.
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13. Sponsor Responsibilities in data quality
management
• The sponsor should implement a system to manage
quality throughout all stages of the trial process.
• The sponsor is responsible for implementing and
maintaining quality assurance and quality control
systems with written SOPs to ensure that trials are
conducted and data are generated, documented
(recorded), and reported in compliance with the
protocol, GCP, and the applicable regulatory
requirements
• The sponsor should utilize appropriately qualified
individuals to supervise the overall conduct of the trial,
handle the data, verify the data, conduct the statistical
analyses, and prepare the trial reports.
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14. • Investigator : A person responsible for the
conduct of the clinical trial at a trial site. If a
trial is conducted by a team of individuals at a
trial site, the investigator is the responsible
leader of the team and may be called the
principal investigator.
• The investigator should maintain adequate and
accurate source documents and trial records
that include all pertinent observations on each
of the site's trial subjects.
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Investigator Responsibilities in Data
quality management

15. Conclusion :
• Data Quality Management helps to identify the objectives of the
clinical Trials.
• It mainly focus on the the quality of the data.
• It allow only qualificatied individual to participate in conducting clinical trials.
• It helps in a data security, accuracy and integrity of data.
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16. Thank You!
www.clinosol.com
(India | Canada)
9121151622/623/624
info@clinosol.com
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