Have full fleged clinical trial data management systems which bring them a good amount of business and revenue.
CDM is a fundamental process which controls data accuracy of each trial besides helping the timelessness to be achieved.
It helps in linking clinical research co-ordinator = who monitor all the sites & collects the data.
it Links with biostatisticians = who analyze, interpret and report data in clinically meaningful way.
Clinical Data Management Plan_Katalyst HLSKatalyst HLS
Introduction to Data Management Plan in Clinical Data Management in Clinical Trials of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
An brief introduction to the clinical data management process is described in this slides. These slides provides you the information regarding the data evaluation in the clinical trials , edit checks and data review finally data locking,then the data is submitted to the concerned regulatory body.
Visit:www.acriindia.com
ACRI is a leading Clinical data management training Institute in Bangalore India.
ACRI creates a value add for every degree. Our PGDCRCDM course is approved by the Mysore University. Graduates and Post Graduates and even PhDs have trained with us and got enviable positions in the Clinical Research Industry. ACRI supplements University training with Industry based training, coupled with hands-on internships and projects based on real case studies. The ACRI brand gives the individual the confidence and expertise to join the ever-growing workforce both in the country and abroad.
Have full fleged clinical trial data management systems which bring them a good amount of business and revenue.
CDM is a fundamental process which controls data accuracy of each trial besides helping the timelessness to be achieved.
It helps in linking clinical research co-ordinator = who monitor all the sites & collects the data.
it Links with biostatisticians = who analyze, interpret and report data in clinically meaningful way.
Clinical Data Management Plan_Katalyst HLSKatalyst HLS
Introduction to Data Management Plan in Clinical Data Management in Clinical Trials of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
An brief introduction to the clinical data management process is described in this slides. These slides provides you the information regarding the data evaluation in the clinical trials , edit checks and data review finally data locking,then the data is submitted to the concerned regulatory body.
Visit:www.acriindia.com
ACRI is a leading Clinical data management training Institute in Bangalore India.
ACRI creates a value add for every degree. Our PGDCRCDM course is approved by the Mysore University. Graduates and Post Graduates and even PhDs have trained with us and got enviable positions in the Clinical Research Industry. ACRI supplements University training with Industry based training, coupled with hands-on internships and projects based on real case studies. The ACRI brand gives the individual the confidence and expertise to join the ever-growing workforce both in the country and abroad.
Study setup_Clinical Data Management_Katalyst HLSKatalyst HLS
Introduction to Study Setup in Clinical Data Management in Clinical Trials of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Clinical Data Management: Best Practices and Key ConsiderationsClinosolIndia
Clinical data management (CDM) is a critical component of clinical research, involving the collection, processing, and analysis of data generated during clinical trials. Implementing best practices and considering key considerations is essential for ensuring data quality, integrity, and regulatory compliance. Here are some important considerations and best practices in clinical data management:
Data Standardization: Standardizing data collection and documentation across study sites is crucial for ensuring consistency and facilitating data analysis. Develop standardized data collection forms, case report forms (CRFs), and electronic data capture (EDC) systems that capture relevant data elements in a consistent manner.
Data Validation and Quality Control: Implement robust data validation procedures to ensure the accuracy and completeness of collected data. Conduct thorough quality control checks, including data validation checks, range checks, and consistency checks, to identify and resolve data discrepancies or errors.
Data Security and Privacy: Ensure data security and protect participant privacy by implementing appropriate measures such as data encryption, secure data transfer protocols, access controls, and adherence to applicable data protection regulations like GDPR or HIPAA.
Data Monitoring and Cleaning: Regularly monitor data collection processes to identify and address data discrepancies, missing data, or outliers. Implement data cleaning procedures to identify and resolve data errors, inconsistencies, and outliers that may impact the integrity and reliability of the study data.
Data Traceability and Audit Trail: Maintain a comprehensive audit trail that captures all changes and activities related to data entry, data modifications, and data review. This ensures data traceability and facilitates data validation and regulatory inspections.
Standard Operating Procedures (SOPs): Develop and adhere to well-defined SOPs for data management activities. SOPs should cover all aspects of data collection, processing, validation, cleaning, and archiving, ensuring consistency and adherence to regulatory requirements.
Electronic Data Capture & Remote Data CaptureCRB Tech
CRB Tech is one of the best leading Software Development Company in Pune. We are offering Software Development Services as well as IT Training including Java, Dot Net, SEO and Clinical Research training in pune.
“CSR is a detailed regulatory document which gives the information about the methods and results (related to efficacy and safety) of a clinical trial. CSRs are created as a part of the process of submitting applications to the Regulatory Authorities for new medical treatments and for its approval. CSRs can be full, abbreviated, synopsis, supplementary, observational etc as per the results and requirements”.
Database Designing in Clinical Data ManagementClinosolIndia
When designing a Clinical Data Management (CDM) database, several key considerations should be taken into account to ensure efficient data capture, storage, and retrieval. Here are some important aspects to consider in CDM database design:
Define Study Requirements:
Understand the specific requirements of the study and the data to be collected. This includes variables, data types, formats, and any specific rules or calculations required for data validation and derivation. Consult with the study team and stakeholders to determine the necessary data elements.
Data Model Design:
Develop a data model that represents the structure and relationships of the data. Use standard data models, such as CDISC (Clinical Data Interchange Standards Consortium) standards, as a foundation. Define entities (e.g., patients, visits, assessments) and attributes (e.g., demographics, lab results) and establish relationships between them.
Data Dictionary:
Create a comprehensive data dictionary that provides a detailed description of each data element, including its name, definition, data type, length, format, allowable values, and any validation or derivation rules. The data dictionary serves as a reference for data entry and validation checks.
Database Schema:
Design the database schema based on the data model and data dictionary. Identify the tables, fields, and relationships needed to store the data. Determine primary and foreign keys to establish relationships between tables. Normalize the schema to reduce redundancy and improve data integrity.
Data Capture Forms:
Design user-friendly data capture forms to facilitate efficient and accurate data entry. Align the form layout with the data model and data dictionary. Include necessary data validation checks and provide clear instructions or prompts for data entry.
Data Validation and Quality Checks:
Incorporate data validation checks to ensure data accuracy and completeness. Implement range checks, format checks, consistency checks, and logic checks to identify and prevent data entry errors. Include data quality control processes to identify and resolve data discrepancies or anomalies.
Security and Access Controls:
Implement appropriate security measures to protect the confidentiality, integrity, and availability of the data. Define user roles and access levels to control data access and modification. Employ encryption, authentication, and audit trails to ensure data security and compliance with regulatory requirements.
Data Extraction and Reporting:
Consider the need for data extraction and reporting capabilities. Design mechanisms to extract data from the database for analysis or reporting purposes. Implement data export functionalities in commonly used formats, such as CSV or Excel, or integrate with reporting tools or systems.
Clinical data management (CDM) is a covered part in the clinical trial and most commonly used tools for the purpose of effectivity of clinical research
Clinical Data Management (CDM) is a critical component of clinical research that involves the collection, cleaning, validation, and management of clinical trial data to ensure its accuracy, integrity, and compliance with regulatory requirements. The workflow of CDM typically consists of several key stages, each with specific activities and processes. Here is an overview of the typical workflow of CDM:
Study Startup:
Protocol Review: CDM teams begin by reviewing the clinical trial protocol to understand the study's objectives, endpoints, data collection requirements, and timelines.
Database Design: Based on the protocol, the team designs a data capture system or electronic data capture (EDC) system. This includes creating data entry forms, defining data validation checks, and setting up data dictionaries.
Data Collection:
Case Report Form (CRF) Design: CDM professionals design electronic or paper CRFs to collect data during the trial. CRFs capture specific data points required by the protocol.
Data Entry: Data is entered into the CRFs, either electronically by site personnel or through paper CRFs.
Data Validation: CDM teams implement validation checks to ensure data quality and consistency. Data validation checks may include range checks, consistency checks, and logic checks.
Query Management: Queries are generated when data discrepancies or inconsistencies are identified. CDM teams send queries to investigational sites for resolution.
Data Cleaning and Quality Control:
Data Cleaning: Data are cleaned to resolve discrepancies, discrepancies, and inconsistencies. This involves querying data discrepancies with clinical trial sites.
Data Review: CDM teams review data to ensure completeness and accuracy, and any outstanding queries are resolved.
Quality Control: Quality control processes are applied to verify the integrity and accuracy of data.
Database Lock:
Once the data are cleaned, reviewed, and validated, the database is locked, indicating that no further changes can be made to the data. Database lock is a critical step before data analysis begins.
Data Export and Analysis:
Data is exported from the database and provided to biostatisticians and researchers for statistical analysis. This analysis is conducted to determine the study's outcomes, efficacy, and safety profile.
Data listings, summaries, and tables are generated for regulatory submissions, reports, and publications.
Final Study Reporting:
After data analysis, CDM teams contribute to the preparation of final study reports, which provide a comprehensive overview of the trial's results, data quality, and regulatory compliance.
Archiving and Documentation:
Clinical trial data, documentation, and databases are archived to ensure their long-term availability for regulatory audits and future reference.
Regulatory Submission: CDM teams provide support for regulatory submissions.
clinical data management in clinical research, helpful for pharmacy, nursing, medical, health care providers, clinical research organization, PharmD, CROs, Clinical trial industry, human biomedical research.
Table of contents
-Definition of CRF
-What is CRF
-Types & Methods of filling of CRF
-CRF Input team
-CRF Approval team
-Review team
-Facts about CRF
-Purpose of CRF
-CRF Development process & Guidelines
-Elements of CRF
-CRF Design
-CRF completion checklist
-CRF Design tools
-CRF use
-GCP connection
Database design in the context of Clinical Data Management (CDM) is a crucial aspect of organizing and managing clinical trial data effectively and efficiently. A well-designed database ensures that data collected during a clinical trial is accurate, consistent, and accessible, facilitating data analysis, reporting, and regulatory submissions. Clinical Data Management involves various steps, including data collection, validation, cleaning, and reporting
Challenges and Opportunities Around Integration of Clinical Trials DataCitiusTech
Conducting a Clinical Trial is a complex process, consisting of activities such as protocol preparation, site selection, approval of various authorities, meticulous collection and management of data, analysis and reporting of the data collected
Each activity is benefited from the development of point applications which ease the process of data collection, reporting and decision making. The recent advancements in mobile technologies and connectivity has enabled the generation and exchange of a lot more data than previously anticipated. However, the lack of interoperability and proper planning to leverage this data, still acts as a roadblock in allowing organizations truly harness their data assets. This document will help life sciences IT professionals and decision makers understand challenges and opportunities around clinical data integration
Data Management and Analysis in Clinical Trialsijtsrd
Data management and analysis play a critical role in the successful conduct of clinical trials. Proper collection, validation, and handling of data are essential for ensuring the reliability and integrity of study findings. Data management involves the design and implementation of data capture tools, such as electronic case report forms eCRFs, to efficiently collect and store clinical data. Additionally, data analysis is a crucial step that involves applying statistical methods to extract meaningful insights from the collected data. This paper provides an overview of the key components of data management and analysis in clinical trials, highlighting the importance of adherence to data standards, ensuring data quality, and maintaining data security. Effective data management and analysis not only lead to robust study outcomes but also contribute to the overall advancement of medical knowledge and patient care. S. Reddemma | Chetana Menda | Manoj Kumar "Data Management and Analysis in Clinical Trials" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-4, August 2023, URL: https://www.ijtsrd.com/papers/ijtsrd59667.pdf Paper Url:https://www.ijtsrd.com/pharmacy/pharmacology-/59667/data-management-and-analysis-in-clinical-trials/s-reddemma
Study setup_Clinical Data Management_Katalyst HLSKatalyst HLS
Introduction to Study Setup in Clinical Data Management in Clinical Trials of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Clinical Data Management: Best Practices and Key ConsiderationsClinosolIndia
Clinical data management (CDM) is a critical component of clinical research, involving the collection, processing, and analysis of data generated during clinical trials. Implementing best practices and considering key considerations is essential for ensuring data quality, integrity, and regulatory compliance. Here are some important considerations and best practices in clinical data management:
Data Standardization: Standardizing data collection and documentation across study sites is crucial for ensuring consistency and facilitating data analysis. Develop standardized data collection forms, case report forms (CRFs), and electronic data capture (EDC) systems that capture relevant data elements in a consistent manner.
Data Validation and Quality Control: Implement robust data validation procedures to ensure the accuracy and completeness of collected data. Conduct thorough quality control checks, including data validation checks, range checks, and consistency checks, to identify and resolve data discrepancies or errors.
Data Security and Privacy: Ensure data security and protect participant privacy by implementing appropriate measures such as data encryption, secure data transfer protocols, access controls, and adherence to applicable data protection regulations like GDPR or HIPAA.
Data Monitoring and Cleaning: Regularly monitor data collection processes to identify and address data discrepancies, missing data, or outliers. Implement data cleaning procedures to identify and resolve data errors, inconsistencies, and outliers that may impact the integrity and reliability of the study data.
Data Traceability and Audit Trail: Maintain a comprehensive audit trail that captures all changes and activities related to data entry, data modifications, and data review. This ensures data traceability and facilitates data validation and regulatory inspections.
Standard Operating Procedures (SOPs): Develop and adhere to well-defined SOPs for data management activities. SOPs should cover all aspects of data collection, processing, validation, cleaning, and archiving, ensuring consistency and adherence to regulatory requirements.
Electronic Data Capture & Remote Data CaptureCRB Tech
CRB Tech is one of the best leading Software Development Company in Pune. We are offering Software Development Services as well as IT Training including Java, Dot Net, SEO and Clinical Research training in pune.
“CSR is a detailed regulatory document which gives the information about the methods and results (related to efficacy and safety) of a clinical trial. CSRs are created as a part of the process of submitting applications to the Regulatory Authorities for new medical treatments and for its approval. CSRs can be full, abbreviated, synopsis, supplementary, observational etc as per the results and requirements”.
Database Designing in Clinical Data ManagementClinosolIndia
When designing a Clinical Data Management (CDM) database, several key considerations should be taken into account to ensure efficient data capture, storage, and retrieval. Here are some important aspects to consider in CDM database design:
Define Study Requirements:
Understand the specific requirements of the study and the data to be collected. This includes variables, data types, formats, and any specific rules or calculations required for data validation and derivation. Consult with the study team and stakeholders to determine the necessary data elements.
Data Model Design:
Develop a data model that represents the structure and relationships of the data. Use standard data models, such as CDISC (Clinical Data Interchange Standards Consortium) standards, as a foundation. Define entities (e.g., patients, visits, assessments) and attributes (e.g., demographics, lab results) and establish relationships between them.
Data Dictionary:
Create a comprehensive data dictionary that provides a detailed description of each data element, including its name, definition, data type, length, format, allowable values, and any validation or derivation rules. The data dictionary serves as a reference for data entry and validation checks.
Database Schema:
Design the database schema based on the data model and data dictionary. Identify the tables, fields, and relationships needed to store the data. Determine primary and foreign keys to establish relationships between tables. Normalize the schema to reduce redundancy and improve data integrity.
Data Capture Forms:
Design user-friendly data capture forms to facilitate efficient and accurate data entry. Align the form layout with the data model and data dictionary. Include necessary data validation checks and provide clear instructions or prompts for data entry.
Data Validation and Quality Checks:
Incorporate data validation checks to ensure data accuracy and completeness. Implement range checks, format checks, consistency checks, and logic checks to identify and prevent data entry errors. Include data quality control processes to identify and resolve data discrepancies or anomalies.
Security and Access Controls:
Implement appropriate security measures to protect the confidentiality, integrity, and availability of the data. Define user roles and access levels to control data access and modification. Employ encryption, authentication, and audit trails to ensure data security and compliance with regulatory requirements.
Data Extraction and Reporting:
Consider the need for data extraction and reporting capabilities. Design mechanisms to extract data from the database for analysis or reporting purposes. Implement data export functionalities in commonly used formats, such as CSV or Excel, or integrate with reporting tools or systems.
Clinical data management (CDM) is a covered part in the clinical trial and most commonly used tools for the purpose of effectivity of clinical research
Clinical Data Management (CDM) is a critical component of clinical research that involves the collection, cleaning, validation, and management of clinical trial data to ensure its accuracy, integrity, and compliance with regulatory requirements. The workflow of CDM typically consists of several key stages, each with specific activities and processes. Here is an overview of the typical workflow of CDM:
Study Startup:
Protocol Review: CDM teams begin by reviewing the clinical trial protocol to understand the study's objectives, endpoints, data collection requirements, and timelines.
Database Design: Based on the protocol, the team designs a data capture system or electronic data capture (EDC) system. This includes creating data entry forms, defining data validation checks, and setting up data dictionaries.
Data Collection:
Case Report Form (CRF) Design: CDM professionals design electronic or paper CRFs to collect data during the trial. CRFs capture specific data points required by the protocol.
Data Entry: Data is entered into the CRFs, either electronically by site personnel or through paper CRFs.
Data Validation: CDM teams implement validation checks to ensure data quality and consistency. Data validation checks may include range checks, consistency checks, and logic checks.
Query Management: Queries are generated when data discrepancies or inconsistencies are identified. CDM teams send queries to investigational sites for resolution.
Data Cleaning and Quality Control:
Data Cleaning: Data are cleaned to resolve discrepancies, discrepancies, and inconsistencies. This involves querying data discrepancies with clinical trial sites.
Data Review: CDM teams review data to ensure completeness and accuracy, and any outstanding queries are resolved.
Quality Control: Quality control processes are applied to verify the integrity and accuracy of data.
Database Lock:
Once the data are cleaned, reviewed, and validated, the database is locked, indicating that no further changes can be made to the data. Database lock is a critical step before data analysis begins.
Data Export and Analysis:
Data is exported from the database and provided to biostatisticians and researchers for statistical analysis. This analysis is conducted to determine the study's outcomes, efficacy, and safety profile.
Data listings, summaries, and tables are generated for regulatory submissions, reports, and publications.
Final Study Reporting:
After data analysis, CDM teams contribute to the preparation of final study reports, which provide a comprehensive overview of the trial's results, data quality, and regulatory compliance.
Archiving and Documentation:
Clinical trial data, documentation, and databases are archived to ensure their long-term availability for regulatory audits and future reference.
Regulatory Submission: CDM teams provide support for regulatory submissions.
clinical data management in clinical research, helpful for pharmacy, nursing, medical, health care providers, clinical research organization, PharmD, CROs, Clinical trial industry, human biomedical research.
Table of contents
-Definition of CRF
-What is CRF
-Types & Methods of filling of CRF
-CRF Input team
-CRF Approval team
-Review team
-Facts about CRF
-Purpose of CRF
-CRF Development process & Guidelines
-Elements of CRF
-CRF Design
-CRF completion checklist
-CRF Design tools
-CRF use
-GCP connection
Database design in the context of Clinical Data Management (CDM) is a crucial aspect of organizing and managing clinical trial data effectively and efficiently. A well-designed database ensures that data collected during a clinical trial is accurate, consistent, and accessible, facilitating data analysis, reporting, and regulatory submissions. Clinical Data Management involves various steps, including data collection, validation, cleaning, and reporting
Challenges and Opportunities Around Integration of Clinical Trials DataCitiusTech
Conducting a Clinical Trial is a complex process, consisting of activities such as protocol preparation, site selection, approval of various authorities, meticulous collection and management of data, analysis and reporting of the data collected
Each activity is benefited from the development of point applications which ease the process of data collection, reporting and decision making. The recent advancements in mobile technologies and connectivity has enabled the generation and exchange of a lot more data than previously anticipated. However, the lack of interoperability and proper planning to leverage this data, still acts as a roadblock in allowing organizations truly harness their data assets. This document will help life sciences IT professionals and decision makers understand challenges and opportunities around clinical data integration
Data Management and Analysis in Clinical Trialsijtsrd
Data management and analysis play a critical role in the successful conduct of clinical trials. Proper collection, validation, and handling of data are essential for ensuring the reliability and integrity of study findings. Data management involves the design and implementation of data capture tools, such as electronic case report forms eCRFs, to efficiently collect and store clinical data. Additionally, data analysis is a crucial step that involves applying statistical methods to extract meaningful insights from the collected data. This paper provides an overview of the key components of data management and analysis in clinical trials, highlighting the importance of adherence to data standards, ensuring data quality, and maintaining data security. Effective data management and analysis not only lead to robust study outcomes but also contribute to the overall advancement of medical knowledge and patient care. S. Reddemma | Chetana Menda | Manoj Kumar "Data Management and Analysis in Clinical Trials" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-4, August 2023, URL: https://www.ijtsrd.com/papers/ijtsrd59667.pdf Paper Url:https://www.ijtsrd.com/pharmacy/pharmacology-/59667/data-management-and-analysis-in-clinical-trials/s-reddemma
Role of Clinical Data Management in Clinical ResearchClinosolIndia
Clinical data management (CDM) plays a critical role in clinical research by ensuring the accuracy, completeness, and consistency of clinical trial data. Here are some key roles and responsibilities of CDM in clinical research:
Data collection: CDM is responsible for designing and implementing data collection procedures to ensure that all data is collected in a standardized and consistent manner.
Data quality control: CDM is responsible for implementing quality control procedures to ensure that data is accurate, complete, and consistent across all study sites.
Data cleaning: CDM is responsible for identifying and resolving data discrepancies or errors in the data that may impact the analysis of study results.
Data analysis: CDM is responsible for performing statistical analyses of the data collected in the clinical trial, which are used to evaluate the safety and efficacy of the investigational product.
Database management: CDM is responsible for developing and maintaining the study database, which is used to store and manage all data collected in the clinical trial.
Study documentation: CDM is responsible for ensuring that all study documentation is accurate, complete, and up-to-date, including study protocols, data collection forms, and standard operating procedures.
Compliance with regulatory requirements: CDM is responsible for ensuring that all data collected in the clinical trial is compliant with regulatory requirements and industry standards
A crucial stage in clinical research is clinical data management CDM , which produces high quality, reliable, and statistically sound data from clinical trials. This results in a significantly shorter period of time between drug development and marketing. Team members of CDM are laboriously involved in all stages of clinical trials right from commencement to completion. They should be able to sustain the quality standards set by CDM processes by having sufficient process expertise. colorful procedures in CDM including Case Report Form CRF designing, CRF reflection, database designing, data entry, data confirmation, distinction operation, medical coding, data birth, and database locking are assessed for quality at regular intervals during a trial. In the present script, theres an increased demand to ameliorate the CDM norms to meet the nonsupervisory conditions and stay ahead of the competition by means of brisk commercialization of products. With the perpetration of nonsupervisory biddable data operation tools, the CDM platoon can meet these demands. also, its getting obligatory for companies to submit the data electronically. CDM professionals should meet applicable prospects and set norms for data quality and also have the drive to acclimatize to the fleetly changing technology. This composition highlights the processes involved and provides the anthology an overview of the tools and norms espoused as well as the places and liabilities in CDM. Syed Shahnawaz Quadri | Syeda Saniya Ifteqar | Syed Shafa Raoof "Data Management in Clinical Research" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-2 , April 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd55050.pdf Paper URL: https://www.ijtsrd.com.com/pharmacy/other/55050/data-management-in-clinical-research/syed-shahnawaz-quadri
Clinical Data management is one of the vital part of clinical research.
Clinical research is research on drugs,devices ,medicines that has to be adminstered for various diseases and illness,to check the efficacy and safety in human voluteers or patients.
It helps in determining dose and dosages of a particular drug or treatment regimen.CR also helps in label expansion of investigational drug. Furthermore it helps in checking any adverse event in post marketed drug which increases the potability of drug among population of various geographical regions.There are various guidelines and regulatory bodies from several parts of world . Each country has its own regulatory body both at state and central level,eg.CDSCO for India,TGA for Australia,USFDA for USA,MCC for South Africa ,UNCST for Uganda,EMEA for European Union,MHRA for UK.Thus CDM plays important role in maintaining accuracy,consistencies,validity reliabilty of available data.It also in decreasing redundancy of duplicate and inconsistent data.It is required to resolve issues pertaining to inaccuracy , signal detection in pharmacovigilance. CDM is completed in three steps set up,conduct ,close out.Database used i n cdm are DBMS ,MS -Access,OC-RDC.Data managers,operators,programmers,developers are include in the process.CDMS Clinical data management system ,clinical system validation.
Clinvigilant is a leading clinical trial solutions Providers. Clinvigilant provides end to end solutions in clinical trials, which includes clinical trial solutions, Clinical Digital Solutions and Clinical Consultancy. For more info visit clinvigilant.com
Revelatory Trends in Clinical Research and Data ManagementSagar Ghotekar
Revelatory Trends in Clinical Research and Data Management
Clinical data management is a heart and important part of a clinical trials, the outcome to generate quality data and accounting of records to protect clinical trial participants data leads to highest quality and integrity of clinical trials.
Dale W. Usner, Ph.D., President of SDC, co-authored the article "The Clinical Data Management Process," which was published in the November/December 2014 issue of Retina Today.
The article reviews the clinical data management (CDM) process in its entirety - from protocol review and CRF design through database lock. Describing the roles of various CDM team members and tips for efficient data management practices, "The Clinical Data Management Process" provides a comprehensive yet concise summary of this essential function in clinical trial research, specifically with respect to retina trials.
Enhancing Data Quality in Clinical Trials: Best Practices and Quality Control...ClinosolIndia
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.
4/22/2020 Originality Report
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Spring 2020 - Data Science & Big Data Analy (ITS-836-9) - Second Bi-T… • Final Exam (project)
%59Total Score: High riskNaveeda Reddy Anugu
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1
Highest Match
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Final.pptx
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Submitted on
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Average Word Count
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Highest: Final.pptx
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FINAL SPONSOR PRESENTATION
Naveeda Reddy Anugu
University of the Cumberlands
Professor: Dr Kelly Wibbenmeyer
DATE: 04/22/2020
SITUATION
The hospital intends to utilize patient data gathers to enhance the health of its members and offer services at relatively lower costs
This is in response to patients’ previous complaints about the high cost of treatment and associated low health results
The impacts points toward ineffective treatment plans and harmful prescription of medication to susceptible populations
Data collected include diseases treated and billed for, prescriptions were given, and the types of treatments given
The data may be used in big data analytics to enhance treatment options, customize patients’ risks and to support customized treatment intervention
Advanced and frontier big data analytics has the potential to improve health. Data can be used to distinguish between effective and ineffective medical practices
depending on an individual patient. Aetna, is a company that collects data for more than 20 million customers and from its pilot project studies, it is evident that
health institutions can promote health and reduce costs by utilizing Big Data Analytics. 2
PROJECT GOALS
The project entails unveiling an invention that will permit big data analytics to buttress decisions and intervention in healthcare settings
The aim of the intervention include: Creation of patient centered decision making support systems
1
2
3
4
2
https://ucumberlands.blackboard.com/webapps/mdb-sa-BB5a31b16bb2c48/originalityReport?attemptId=c27f66fc-c5df-47dc-9f48-d005b34b35d9&course_id=_114115_1&download=true&includeDeleted=true&print=true&force=true
4/22/2020 Originality Report
https://ucumberlands.blackboard.com/webapps/mdb-sa-BB5a31b16bb2c48/originalityReport/ultra?attemptId=c27f66fc-c5df-47dc-9f48-d005b34b35d9… 2/4
The aim of the intervention include: Creation of patient-centered decision making support systems
Utilization of patient's data contained in clinical trails, insurance companies records and in patient’s health reco.
Role of computer in clinical developmentDivyaShukla61
computers have always played a crucial role in our daily lives, Here i have presented its role in Clinical development.Hope you understand easily from my presentaion.
Data Validation in Clinical Data ManagementClinosolIndia
Data validation is a crucial component of Clinical Data Management (CDM) to ensure the accuracy, completeness, and consistency of clinical trial data. It involves the systematic and rigorous examination of data to identify and address errors, inconsistencies, or discrepancies. Effective data validation enhances the integrity of the clinical trial data and is essential for regulatory compliance and the generation of reliable results. Here are key aspects of data validation in CDM
NS1450X - Computerized Systems in Clinical ResearchJudson Chase
I am guest lecturer (paid) at the Boston College William F. Connell School of Nursing (more information at http://www.bc.edu/schools/son/aboutus.html).
Three or four times a year I lecture on the application of Computerized Systems in Clinical Research; this is my course deck from 2014.
Best Practices to Risk Based Data Integrity at Data Integrity Conference, Lon...Bhaswat Chakraborty
Data integrity can be implemented using several approaches. One of the most effective ways to implement DI is a risk based approach. The speaker elaborates this.
Similar to Clinical Data Management Process Overview_Katalyst HLS (20)
Introduction to Aggregate Reporting in Drug Safety & Pharmacovigilance in Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
MANAGEMENT OF ATRIOVENTRICULAR CONDUCTION BLOCK.pdfJim Jacob Roy
Cardiac conduction defects can occur due to various causes.
Atrioventricular conduction blocks ( AV blocks ) are classified into 3 types.
This document describes the acute management of AV block.
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
These lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
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The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
3. Clinical data management includes the entry, verification, validation
and quality control of data gathered during the conduct of a clinical
trial.
Clinical Data Management is involved in all aspects of processing the
clinical data. It involves working with a range of computer applications,
database systems to support collection, cleaning and management of
clinical trial data.
Review and approval of new drugs by Regulatory agencies is
dependent upon the integrity of clinical trial data which is the
core purpose of CDM.
Overview
3
Overview
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4. After this chapter you will be able to understand:
• Overview of Clinical Data Management
• Process flow of data management activities
• Activities performed during the course of a trial
• Analysis and reporting process overview
• Roles and responsibilities of all personals involved in CDM
Objectives
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5. The average number of discrepancies created during the course of a
Phase 3 study ranges from 3,000 to 30,000
The turn around time to action a discrepancy from the time of
generation is 2-3 days
A single open discrepancy or a Database update can lead to Database
unlock
Do You Know
5 2/21/2017Katalyst Healthcares & Life Sciences
6. Abbreviations
6
CRF Case Report Form
DB Database
QC Quality Control
DMP Data Management Plan
CSR Clinical Study Report
UAT User Acceptance Testing
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7. Definition of Clinical Trial
It is a systematic study of new drug(s) in human subject(s) to generate
data for discovering and/or verifying the clinical, pharmacological
(pharmacokinetic and Pharmacodynamics), and/or adverse effects with
the objective of determining safety and /or efficacy of the a new drug.
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8. Clinical Trial Phases
Phase I Trials —Involve a small group (20 to 100) of healthy volunteers
to discover if the drug is safe in humans
Phase II Trials —Involve 100 to 500 patients who actually have the
disease. Clinical studies are conducted to evaluate the effectiveness of
the drug and to determine the common short-term side effects and
risks associated with the drug
Phase III Trials —Involves thousands of patients to generate
statistically significant data about safety, efficacy, and an overall
benefit/risk profile
Phase IV Trials —Certain post marketing studies to find out
additional information about the drug's risks, benefits, and its optimal
use.
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9. Why Clinical Trials?
Species difference
Some effects seen only in humans
Correlation of effects in animals and human –not always possible
To assess if the treatment is safe and effective in humans
Man is final experimental animal to be tested
9
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Katalyst Healthcares & Life Sciences
10. Multidisciplinary Roles in Clinical
trial
1. Clinical Investigator
2. Site coordinator
3. Pharmacologist
4. Trialist/Methodologist
5. Biostatistician
6. Lab Coordinator
7. Reference lab
8. Project manager
9. Clinical Research Manager/Associate
10. Monitor
10
11. Regulatory affairs
12. Clinical Data Management*
13. Clinical Safety Surveillance
Associate (SSA)
14. IT
15. IT/IS personnel
16. Trial pharmacist
17. Clinical supply
18. Auditor/Compliance
19. Study Physician
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11. Clinical Data Management -
Overview
11
Investigator Monitor
Central
Laboratory
Data Manager
Statistician
Clinician
Regulatory
Authority
Subject
CRF
DCF
CRF DCF
Sample
Lab
Results
Clinical
Data
NDA
DCF
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12. Definition of Data
Data with reference to CDM means the Patient Information which is
collected during Clinical trial.
Data is collected to establish whether the objective of the Clinical Trial
is met
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13. Objectives of CDM
13
Data Collection
Data integration
System / Data
Validation
Paper, Electronic and Remote
data capture
Integration of data received
from all sources in a single
DB. Ensures consistency and
correctness
System validation done via
UAT, QC and Programming
Data Validation via Edit
check programs and
manual review
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14. Scope of CDM
Main scope of CDM is to Collect, Validate and Analyze the clinical data
Design and development of data collection instrument such as Paper
CRF, Electronic CRF, Clinical database etc
Design and development of tools for Validation such as Edit Checks,
User Acceptance Testing etc
Design and development of tools for Analyzing data such as DDR/DDS
(Derived Dataset Requirement/Specification) etc.
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15. Importance of CDM
CDM is a vital vehicle in Clinical Trials to ensure integrity & quality of data
being transferred from trial subjects to a database system. It helps :
To provide consistent, accurate & valid clinical data
To support accuracy of final conclusions & report
Clinical Data Management ensures:
That collected data is complete & accurate so that results are correct
That trial database is complete, accurate & a true representation of what took
place in trial
That trial database is sufficiently clean, to support statistical analysis, its
subsequent presentation & interpretation
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16. 16
Inter-dependent groups in CDM
Data Cleaning
BiostatisticsProgramming
Clinical Data
Management
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17. DM role in Clinical Research
CDM has evolved from a mere data entry process to a much diverse process
today
• The data management function provides all data collection and data
validation for a clinical trial program
• Data management is essential to the overall clinical research function, as
its key deliverable is the data to support the submission
• Assuring the overall accuracy and integrity of the clinical trial data is the
core business of the data management function
• It provides data and database in a usable format in a timely manner
• It ensures clean data and a ‘ready to lock’ database
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18. DM role in Clinical Research
• At the study level, data management ends when the database is locked
and the Clinical Study Report is final
• At the compound level (of the drug), data management ends when the
submission package is assembled and complete
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21. 21
Study Start-up Process
Protocol
CRF Design
Database
Design
Validation/
Derivation
Procedures
Activated
database ready
to accept
production data
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22. Study Set-up –Roles and Responsibilities
CRF Designers -Design CRF as per protocol
DB designers -Design DB as per protocol OR CRF OR CRF Specs and
activate the same
Programmers -Program Validation and Derivation procedures, and
activate the same
Data Managers -Review the CRF prior to activation, test the database
prior to activation, write the validation and derivation
procedures/checks and test the same prior to activation
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23. 23
Study Conduct Process
Activated DB
Data Entry /
Loading (CRF
& external
data)
Discrepancy
Management
Query
Generation
Safety Data
Recon.
Coding terms
Resolution &
update of DB
Manual
Check/ QC
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24. 24
Study Conduct –Roles and
Responsibilities
Data Entry/Data Loaders-Manually enter the data (in case of paper
studies),
load data in case of electronic studies) and external data (Example:
lab, ECG,
subject diaries etc.)
Data Managers -Identify and resolve discrepancies, issue queries to
site
& resolve them, carry out manual checks, lab review and CRF tracking
Safety Data Managers -Perform the safety reconciliation by
comparing the
clinical database with the safety database
Dictionary Coders -Code medical terms collected during clinical
trial.
Example: Medications and Adverse events
25. Data Capture
Regardless of whether you’re running a small, single Phase I trial or many,
complex Phase III trials you look for ways to ensure that your organization
is collecting and managing clinical data reliably, efficiently and in
compliance with industry and government regulations.
25
Electronic
Data Capture
Paper Data
Capture
Remote Data
Capture
Data
Capture
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26. Difference between Data Capture Tools
26
The difference between Paper, Electronic and Remote data
capture is :
Paper
Data is entered on
Paper Case Report
Form
Data Entry
associate will
enter the data in
to the Clinical
Data base
No real time
access to the data
Electronic
• Data is captured in
electronic Case
Report Form
• Investigator enters
the data into the
database
• Real time access to
the data
Remote data entry/ capture
• Data is captured in
electronic Case Report
Form
• RDE systems allow
research staff to enter data
directly at the medical
setting, useful when a
multicenter study is being
conducted with many
institutions participating
• Not web based thus no real
time access to the data
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28. CRF Tracking
Receipt and Tracking of CRF
The tracking process encompass verification of the arrival date & its
acknowledgement & its progress through the process
Checking of quality and completeness of the documents
Tracking missing documents
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29. Data Entry & Verification
Data Entry Processes is of two types as follows:
1.Single Pass Data Entry
→ Single entry with a manual review
→ Single entry without manual review
2.Double Pass Data Entry
→ Double data entry with blind verification, where two people enter the
data independently and any discrepancies between first and second entry are
resolved by the third person based on the verification report on records that
failed data entry verification
→ Double entry with interactive verification where the second entry
operator resolves discrepancies between 1st & 2nd entry and is aware of the
first entered values
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30. Data Review
Why Data Review?
To ensure complex medical data are reviewed and assessed to detect any
discrepancy in the data.
Discrepancy Examples:
Empty fields
Incorrect Range
One value greater/less than/equal to another
Dates not in logical sequence
Inconsistent header information
Any missing visits or pages
Visits not in compliance with protocol
Inclusion/exclusion criteria not met
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31. Data Review –Edit Checks
Consist of computer checks on the data to assure the validity and
accuracy of the data
Validate data manually against predetermined specifications
Primarily used to check the efficacy data unique to the current study
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32. Edit Checks Types
Range checks
To identify inaccurate or invalid data & statistical outliers
To ensure that data outside of permitted range are to be clarified and
verified
E.g. Systolic blood pressure (***) is outside the Critical Range (***).
Consistency checks
To highlight area where the data in the database are inconsistent
E.g. Adverse Event stop date is always after AE start date
Presence checks
To ensure completeness of data
E.g. SEX is missing
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33. Data Query
A query is an official communication to the investigative site to
question on a discrepant data on the case report form.
Subsequent changes in the data must be supported by signed Data
Clarification Form (DCF). EDC Query
Data Clarification Form (DCF)
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34. Medical Coding
It is a process which involves grouping or classifying new and amended
terms like medications, adverse events, medical history medical
procedures, diagnoses, disease conditions with reference to known
standard terms as mentioned in medical dictionary
Importance of coding :
The use of medical coding dictionaries for medical term data such as
adverse event, medical history, medications & treatments/procedures
are valuable from the standpoint of minimizing variability in the way
data are reported and analyzed.
To provide control & consistency, a variety of medical coding
dictionaries may be used to process, analyze and report collected data.
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35. Medical Coding Dictionaries
Coding Dictionaries:
MedDRA
Medical Dictionary for Regulatory Activities, is a standardized dictionary of
medical terminology
WHO: WHOART, drugs
World Health Organization Adverse Reaction Terminology
ICD
International Classification of Diseases
FDA-COSTART
Coding Symbols for a Thesaurus of Adverse Reaction Terms
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36. Safety Data Reconciliation
What is AE : Adverse event means any untoward medical occurrence
associated with the use of a drug in humans, whether or not considered
drug related.
What is a Serious Adverse Event:
Any adverse event that leads to:
Results in death
Is life-threatening
Requires inpatient hospitalization or
Prolongation of existing hospitalization
Results in persistent or significant disability / incapacity
Is a congenital anomaly / birth defect
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37. Safety Data Reconciliation
Reconciliation: It is the comparison of particular data points related to SAEs
that appear in both the Safety and Clinical Databases and must be cleaned
100%, with all acceptable discrepancies documented. All SAEs entered into
the clinical trial database are also entered into the drug safety database and
are reconciled to ensure the consistency between specified data points.
Reason for performing Reconciliation:
It is necessary because SAE data is considered CRITICAL DATA in both ,
the safety and clinical databases. Critical data is made up of dosing,
demography, adverse event and final subject summary pages, all of which
are data points that make up the cases that are reported to the safety
database
It is essential to understand that these data are submitted to Regulatory
Agencies both at end of study and for subsequent aggregate reporting
which occurs well after database lock.
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39. Declaring Clean File & Database Lock
Clean File means that the data generated from clinical trial is clean & ready
for Database Locking/freezing
Clean File can be declared for a study when all required data management
activities (as per the Data Management Plan) have been completed and
documented appropriately
This is a procedure which is done at the end of clinical trial after the last
query is resolved & prior to DB locking/Freezing
This procedure ensures the following points are met:
Data is complete i.e., No missing data
Data is consistent
Data is accurate
Data is reliable
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40. Validated clean data will be transferred to a final
database
Prior to locking the study, the following steps are
completed:
Checklist for Database Lock
40
All expected CRFs are entered
All CRFs have been Verified by the CRA
All data discrepancies are resolved
Final validations are executed with no remaining unresolved discrepancies
All lab data, external and internal (e.g. PK, ECG), are loaded and reconciled
All lab normals are present, loaded and complete
Adverse event coding is complete and approved by the study MD
All other medical coding is complete
The Statistician confirms that the data meet previously agreed acceptance criteria
The Statistician and CDM agree that the database is ready for locking
All approvals are obtained on the Database Lock/Freeze/Unfreeze Approval form
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41. Unlock Scenarios
Can a Database be unlocked?
Ans: Yes
When can Unlocking be Done?
Unlocking of the database is carried out only if corrections to the critical errors
(such as Adverse Event, Medication, Lab, etc.) are required.
For e.g. -Updates to serious adverse events data may require edits to the data.
A request to unlock the study usually requires review of detailed reasons by
higher level management before the database administrator removes the locks.
Appropriate quality control, review and approval will again be required to
unlock the study.
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42. Quality Control
Quality Control (QC): Periodic operational checks within each functional
department to verify that clinical data are generated, collected, handled, analyzed,
and reported according to protocol, SOPs, and GCP.
Example: QC activities performed during the data management process:
Double Data Entry: Accuracy of the initial data entry is verified by an independent
entry of the same data and a subsequent comparison of both sets of data for non-
agreement.
Edit Checks/ Manual Review: The reality of the data is checked with a
preprogrammed logic check program and a subsequent manual review
Final QC: The database entries are then QC'dversus the CRFs
Tables, Listings and Graphs (TLG) inspection: The TLGs that are generated as
part of a statistical analysis of the data are also inspected to ensure their accuracy.
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43. Quality Assurance
Quality Assurance:
“All those planned and systematic actions that are established to ensure
that the trial is performed and the data are generated, documented
(recorded), and reported in compliance with Good Clinical Practice
(GCP) and the applicable regulatory requirement(s).
Involves Inspections and Audits
Inspection is by Governmental Agencies, Health Authorities and the
Drug Regulatory Authorities
Auditing is by pharmaceutical, devices companies, CROs, and others
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44. Audits Types
Internal audit (first party audit):
Carried out by service provider’s Audit Department to ensure
implementing, maintaining and improvement of the system audited.
Customer audit (second party audit):
Carried out by client to evaluate the service providers’ performance and
compliance for standards.
External audit (third party audit):
Carried out by regulators or external auditors contracted by sponsor to
ensure implementing and documenting according to standards.
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45. Benefits of Internal Audit
Audit of processes to identify systemic problems
Identify the root of a problem and plan for corrective and preventive
actions
Review of employee training records
Compliance with SOPs and regulatory requirements
Documented evidence that QC was appropriately conducted on the
output of each internal process
Achieve better allocation of resources
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46. Roles & Responsibilities
Programmers extract data and map the same into specific formats
(reports and listings) as specified by the sponsor to aid the statistical
analysis.
Statisticians use the programmed reports and listings and analyze the
data as per a pre approved statistical plan.
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47. A & R –Tables & Listings snap shot
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48. Roles & Responsibilities
Medical Writers – Generate Clinical Study Report, using the statistical
analysis and other study documents thus summarizing the overall
findings and conclusions of a clinical trial. The CSR is used for
submission to the regulatory authorities
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49. Slide No. 49 • QS CRS Quality
Services / Svend Martin Fransen
• 03.Oct.2002
21CFR11, Overview
Substantive rule from 20 August 1997
Applies to any e-record in any FDA regulated work
including legacy systems
Criteria for e-records and e-signatures:
Trustworthy and reliable
E-signatures = hand-written signatures
Minimum requirements / fraud prevention
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71. Slide No. 71 • QS CRS Quality
Services / Svend Martin Fransen
• 03.Oct.2002
Systems not Applications
• All definitions and
clauses in 21 CFR 11
refer to systems
• Application is not
mentioned
• IT part of the GXP
environment.
• Do they know?
Working environment
Computer based system
Computer system
Application
-software
Platform
- hardware
- system SW
Controlled function
Instructions,
Manuals, etc.
Equipment
COMPUTER RELATED SYSTEM
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72. Slide No. 72 • QS CRS Quality
Services / Svend Martin Fransen
• 03.Oct.2002
21 CFR Part 11, Basics
• Electronic records equivalent with paper records
• Storage, retrieval and copying in full retention period
• Submitting to FDA
• Protection of electronic records
• Security (physical and logical)
• Validation
• Audit trail (who did what, when including reason where req.)
• Permission to use of electronic signature
• Equivalent with handwritten signatures
• Name, date and meaning
• Linking of signature to record
• Unique for an individual
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73. Slide No. 73 • QS CRS Quality
Services / Svend Martin Fransen
• 03.Oct.2002
FDA 21CFR11 inspection questions
(source: : 21CFR11 Compliance Report, Vol.2, No. 4).
Who is allowed to input data?
Who is allowed to change data?
How can you tell who entered the data?
How do you know which data had been changed?
When do you lock down the data input?
Can you do the following actions?
“Show me some data, show me you can see the history of the data,
show me you control the data life cycle.”
Is the system validated and are the requirements met?
Can you show me the results of the validation activities?
Does the validation include: “Pass/fail, signature, date/time stamp”;
and “objective evidence - screen prints or page printouts with a link
to the direction that generated the output.”?
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75. 1. When do the CDM activities start.
2. What is the first activity performed by CDM in
Study Start-up?
3. What are the modes of data collection?
4. What are the different ways of Validating data?
5. What does CSR stand for?
Test Your Understanding
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76. In this session we have understood the following points:
● What is Clinical Research?
● What is Clinical Data Management?
● Importance of CDM
● CDM work flow
● Roles and Responsibilities across all processes
● Activities performed by Data Managers in Clinical Research
Summary
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77. • Practical Guide to Clinical Data Management; Second Edition: by
Susanne Prokscha
• COMPUTERIZED SYSTEMS USED IN CLINICAL TRIALS, U.S.
Department of Health and Human Services, Food and Drug
Administration
• http://en.wikipedia.org/wiki/Clinical_trial
Source
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CDM is consistently being recognized as a primary part of clinical development team & in some instances leads this team. CDM has evolved from a data entry process into a diverse process:
to provide clean data in a useable format in a timely manner
provide a database fit for use
ensuring data is 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
generate & track CRFs & queries
determine protocol violators
interact with site personnel to resolve data issues
Clinical trials often involve patients with specific health conditions who then benefit from receiving otherwise unavailable treatments. In planning a clinical trial, the sponsor or investigator first identifies the medication or device to be tested. In coordination with a panel of expert investigators, the sponsor decides what to compare the new agent with, and what kind of patients might benefit from the medication or device. During the clinical trial, the investigators: recruit patients with the predetermined characteristics, administer the treatment(s), and collect data on the patients' health for a defined time period. These data include measurements like vital signs, concentration of the study drug in the blood, and whether the patient's health improves or not. The data collected is recorded on the CRFs and the lab samples are sent to the laboratory for assessment. The data recorded on the CRF by the investigator is verified by the site monitor against the source documents. Both data from the CRF and laboratory are then sent to the data manager who runs validation checks on the data and performs data cleaning activities. As a result of data cleaning activities, DCFs(data clarification form) are generated which are sent to the investigator for clarification. The database is updated based upon the resolutions received. The clean data is then sent to the statistician who then analyzes the pooled data using statistical tests. Clinical study reports are created by medical writer based upon the statistical analysis results which is submitted to the regulatory agencies for approval.
Data in a clinical trial may be collected through various modes. The most common include Paper CRF, remote and electronic data capture. In addition to these methods some data may also be collected through IxRS (Interactive Voice and Web Response System). The data coming in through the above modes is required to be integrated into one centralized system or the clinical database. Integration of clinical data means to ensure that the data incorporated in the clinical data management systems are correct, consistent and an exact replica of the data received on the paper forms. No data manipulation is expected during the data transfer between one or more modes. The next step in the data management process is the validation of data entered into the system for correctness. All the systems being used and the incoming data should be validated before release. The systems are validated through User Acceptance testing methods (UAT), while the clinical data is validated through electronic programs written by the programmer and manually as well.
Data Management activity begins with the receipt of the final approval protocol. All Data management activities are performed closely in conjunction with the programming and biostatistics team. The programming team helps to program the validation and derivation checks required to identify discrepancies in the data received. When these validation checks are executed on the received data discrepancies are generated in the clinical data management system. The discrepancies are resolved either internally using study conventions or through queries answered by the investigator. The clean data is provided to the biostatistics team at the end of the study. The biostatisticians perform data analysis using different methods. The results of data analysis are used for generation of a clinical study report.
A Separate SAS Programming team works on the SAS checks (more complex checks / checks that involve comparison of multiple data points / or involve comparison of study data with external data). This team is a part of the Setup team handling validation procedures.
Also, there exists a SAS Programming team which is part of Biostatistics. This team works with Biostatisticians to create Tables, Listings and figures for analysis.
CDM is a vital vehicle in Clinical Trials to ensure integrity & quality of data being transferred from trial subjects to a database system.
To provide consistent, accurate, & valid clinical data
To support accuracy of final conclusions & report
The study start up activities include the build-up activities like CRF development, database designing. Data collection instruments like CRF, DB, are to be designed and the validation tools should be ready during this phase. Different documents created by data manager during study start up includes CRF completion guideline, Data Management plan, edit check specification. The very first activity performed by data mangers during study set –up is creation of CRF based on the approved protocol. The start up activities form the base of a clinical trial at the data management end. The roles involved during the study start up phase from the DM team are the CRF designers, DB designers, Programmers, and Data Managers.
Completion of the start up activities are a trigger for the start of conduct activities. Data cleaning is the core objective during the conduct of the study. Data entry is the first step that is performed after which all the data coming in from the CRF, through electronic transfers (lab, ECG) are validated through the programming checks. Medical terminologies are coded to maintain consistency and also as a reporting requirement. Personnel involved during the conduct of a study are:
Data entry associate entre the clinical data into CDMS, Data Managers validate the data, Coders are responsible to coding the medication and adverse events reported in the clinical trial and Safety data managers.
Once the data cleaning activities are complete, all the electronic and manual checks are performed on the data and the data is error-free, it is ready to be frozen & locked. It is essential to confirm that there are no outstanding queries or resolutions that need update to be made in the database. Data manager is responsible for freezing & locking the study.
Data managers can also execute / perform Safety Data Manager's responsibility depending on the project requirement.
Critical Data points are those datapoints which decide the Safety and Efficacy of the Investigational Product. This is mentioned in the Protocol as Primary endpoint.
For eg : On SAE form hospitalization has been reported. However, the subject died subsequently but the data was not updated which needs to be updated in the Database.