Ensuring data quality is crucial in clinical trials to generate reliable and valid results. High-quality data allows for accurate analysis, interpretation, and decision-making regarding the safety and efficacy of investigational products. Here are some best practices and quality control measures to enhance data quality in clinical trials: Standardized Data Collection: Implement standardized data collection procedures, including the use of case report forms (CRFs) or electronic data capture (EDC) systems. Clearly define data elements, variables, and measurement scales to minimize inconsistencies and errors in data entry. Training and Education: Provide comprehensive training to investigators, site staff, and data entry personnel on the protocol, data collection procedures, and Good Clinical Practice (GCP) guidelines. Training ensures understanding and adherence to the study requirements, leading to accurate and consistent data collection. Source Data Verification (SDV): Perform source data verification to compare data recorded in the CRFs or EDC systems with the original source documents (e.g., medical records, laboratory reports). This process helps identify discrepancies, errors, or missing data, ensuring data accuracy and integrity. Data Management Plan: Develop a robust data management plan that outlines procedures for data collection, handling, storage, and analysis. The plan should include data validation checks, query resolution processes, and data reconciliation between different data sources. Electronic Data Capture (EDC) Systems: Utilize EDC systems to facilitate real-time data capture, improve data accuracy, and streamline data management processes. EDC systems often have built-in data validation checks, range checks, and skip patterns to minimize data entry errors.

1. Welcome
Enhancing Data Quality in Clinical Trials:Best
Practices and Quality Control Measures
S.Vignesh
M.Tech(Biotechnology)
CLS_111/062023
10/18/2022
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2. Index
 Data quality Management
 Characteristics of Data Quality
 Key Features of Data Quality Management
 Data Quality Management in Clinical Research
 Regulatory and Policy Basis for Data Quality Management
 Elements of Data Management Form
 Quality Control in Clinical Trials
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3. Data Quality Management
Data quality management (DQM) is a formal process for managing the quality, validity and integrity of
the research data captured throughout the study from the time it is collected, stored and transformed
(processed) through analysis and publication. This is achieved via two processes referred to as Quality
Assurance and Quality Control .
DQM starts with a data management plan that is specified in the protocol, as a component of the data
safety monitoring plan and approved by the IRB and sponsor, as applicable, before the protocol starts.
DQM includes the organization and retention of key study documentation, known collectively as the
Regulatory File. The Regulatory File is organized and retained to support monitoring and auditing by
ICs, IRBs, the HRPP, Sponsors or Contract Research Organizations and regulatory authorities.
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4. Characteristics of Data Quality
Data quality is crucial as it assesses whether information can serve its purpose in a particular
context
• Accuracy
• Completeness
• Reliability
• Relevance
• Timeliness
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Characteristic How it’s measured
Accuracy Is the information correct in every detail
Completeness How comprehensive is the information
Reliability Does the information contradict other trusted resources
Relevance Do you really need this information
Timeliness How up- to-date is information? Can it be used for real-time
reporting

5. Key features of Data Quality Management
A good DQM makes use of a system that has various features that will help in
improving the trustworthiness of organizational data. Let us outline the various features
of a good Data Quality Management
Data cleansing corrects unknown data types, duplicate records, as well as substandard
data representations. Data cleansing ensures that data standardization rules that are
needed to enable analysis and insights from your data sets are followed. The data
cleansing process also establishes hierarchies and makes data customizable to fit an
organization’s unique data requirements.
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6. Contd..,
Data profiling is the process of monitoring and cleansing data. Data profiling is used to
• Validate available data against the standard statistical measures,
• Create data relationships
• Verify the available data against matching descriptions
• The data profiling process establishes trends that help in discovering, understanding
and exposing inconsistencies in the data, for any corrections and adjustments
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7. Data Quality Management in Clinical Research
• All research results in the production of data that must be processed and analyzed to
support or refute the study hypothesis
• The data must be of sufficient quality and integrity to support the endpoint analyses
• The data used to support marketing applications for FDA-regulated research must be
accurate and validated
• FDA E6 Good Clinical Practice (GCP) standards will be used as the basis of this
course
• Following best practices for data management is a continuous process that utilizes
quality control (QC)/quality improvement (QI) methodology
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8. Regulatory and Policy Basis for Data Quality
Management
Below is a list of regulations, policy and guidance regarding data quality management
FDA GCP Guidelines: Below are a selected list of sections from the FDA E6 Guidelines that may be of
interest:
• Investigator Responsibilities
• Sponsor Responsibilities
• Essential Documents for the Conduct of a Clinical Trial
NIH Policy:
• SOP 15 - Research Regulated by the Food and Drug Administration (FDA): General Procedures for Both
IND and IDE Applications
• SOP 17 - Data and Safety Monitoring
• SOP 19 - Investigator Responsibilities
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9. Elements of Data Management Form
• Specification of the data to be collected on the protocol
• Case Report Forms (CRFs) to be developed
• Establishment of desired best practices (QA) for:
• Collecting source documentation (raw data)
• Extracting research data from the source data to complete the Case Report Forms (CRFs)/data management
system
• Maintaining confidentiality of data
• Plans for secure storage of data with limited data access
• Processes for Quality control (QC) of data and the specification of processes for corrections of incorrect data
• How data will be transformed (processed) to prepare it for analysis
• Quality assurance processes and timing for raw and transformed data
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10. Quality Control in Clinical Trials
Quality Control in human clinical trials means the procedures which ensure the protection of
human subjects from research risk, reliability of the data, and thereby assuring internal
consistency. This has been developed since the 1970s in the US, by establishing various
regulations, which are now called GCP.
Good clinical practice (GCP):
It is an international ethical and scientific quality standard for designing, conducting,
recording, and reporting trials that involve the participation of human subjects. Since 1997,
the ICH-GCP E6(R2) guidelines have been a requirement for conducting human clinical trials,
which should be used as documentation to the authorities.
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11. Contd.,
GCP clinical trials adherence to quality standards during the clinical trial process assures that the data and
the reported results are credible and accurate and that the rights, integrity, and confidentiality of the trial
subjects are protected.
The Quality Challenge:
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. Maintaining accuracy and quality
throughout clinical studies is a continual, dynamic process.
Although study requirements are carefully set forth initially in detailed documents such as an approved
clinical protocol, A clinical trial data management plan, and an accompanying project plan, expectations
and requirements can change during a study. This ongoing process requires revising mechanisms and
communicating these revisions clearly to all investigators and support staff.
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12. Improvement of Data Quality
Gathering high-quality, reliable, and statistically sound data is the goal for every clinical trial, and
effective data management is essential to ensuring accurate data collection, entry, reports, and
validation. As a critical phase of the clinical research process, it’s important to establish and
maintain organization-wide standards for data management to ensure consistency across all
individuals and teams involved.
• Ensure data is “fit for purpose”
• Identify critical data points
• Establish detailed standard operating procedures (SOPs)
• Invest in staff education
• Find the right system for your operational needs
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13. References
• https://www.precisely.com/blog/data-quality/5-characteristics-of-data-quality
• https://www.dqlabs.ai/blog/what-is-data-quality-management/
• https://oir.nih.gov/system/files/media/file/2021-08/data_quality_management-
2015_05_15.pdf
• https://www.advarra.com/blog/improve-data-quality-with-5-fundamentals-of-
clinical-data-management/
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14. Thank You!
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