Clinical data management involves processing clinical trial data using computer applications and database systems. It supports the collection, cleaning, and management of subject data. Key aspects of clinical data management include CRF design, database setup, data entry, discrepancy management, medical coding, quality control, and database lock. The goal is to ensure the integrity and quality of clinical trial data.
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.
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.
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.
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
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.
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.
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 (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.
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
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.
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
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.
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.
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 (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.
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
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.
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.
Appalla Venkataprabhakar and I presented this at the Oracle\'s Annual Clinical Development and Safety Conference 2010 at Hyderabad, India on 6th October 2010.
Integrating Clinical Operations and Clinical Data Management Through EDCwww.datatrak.com
When electronic data capture was first introduced there was a great deal of discussion surrounding how the technology would alter the roles of those in clinical operations and clinical data management. Through the review of a case study, we will explore how EDC is used as a tool to more tightly integrate clinical operational staffs with those in clinical data management resulting in a more streamlined process from study initiation to database lock.
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This is an introduction to the Microsoft Excel add-in tool which is developed to improve efficiency and user experience for creating Edit Check in Medidata Rave.
Drug Safety & Pharmacovigilance - Introduction - Katalyst HLSKatalyst HLS
Introduction to Drug Safety & Pharmacovigilance in Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
Production Bioinformatics, emphasis on ProductionChris Dwan
Production bioinformatics at Sema4 can be thought of as data ops - a peer to the lab ops organization. We operate 24/7 to deliver correct and timely results on NGS and other data for thousands of samples per week. This deck introduces the Prod BI organization and systems architecture with a focus on what it takes to run bioinformatics in production rather than for R&D or pure research.
Study start up activities in clinical data managementsoumyapottola
Study start-up (SSU) is so much more than a one-time document management exercise. It’s a global, strategic operation that can get new drugs approved faster – and it’s ripe for innovation – from Site Selection to Site Activation and Site Training.
Many SSU tech solutions deployed by sponsors don’t deliver the results promised because they add burden without benefits to clinical research sites. The result? Site staff simply avoid using them.
When that happens, document exchange and tracking falls back to paper, email and Excel formats – with CRAs holding the processes together. The tools that were supposed to solve a problem become part of the problem – and consume preThe implementation and conduct of a study can be a complex process that involves a
team from various disciplines and multiple steps that are dependent on one another. This
document offers guidance for navigating the study start-up processcious clinical trial budget.
A successful clinical study start-up is a crucial first step and an important factor for the overall success of the trial. For this reason, SCRO has experienced study start-up teams, offering customized services depending on your needs, whether it be fuWhile the definition varies across companies, study startup typically includes the process of identifying and qualifying sites, collecting essential documents at the study and site level, and submitting these documents for ethics approval. Successful study startup requires coordination between sites, sponsors, and contract research organizations (CROs) to achieve critical milestones in a compliant manner.ll-service or single activities.
How to achieve better time management in EDC start up
Clinical data management requires strict time management processes, especially in study start up within an electronic data capture (EDC) system. Three steps that clinical data management teams can take to outline the planning and executing of each task that needs to be considered are as follows:
Make a List: Create a daily or weekly task list and schedule when each task will be completed. This strategy will assist you in maintaining focus and staying organized.
Set realist goals: Be realistic about what you can finish in the amount of time you have. When setting unrealistic goals, failure is almost certain to follow.
Explore time-saving techniques: Examples of techniques that could help save time include grouping similar tasks together or using a timer to stay focused.
To help get started, here is a list of EDC considerations for Study Start-Up deadlines:
Protocol finalization and study enrollment
Split go-live considerations
eCRF Specification meetings (this will ensure proper collaboration and minimize any back-and-forth communication)
EDC add-on modules (which will be required and need validation?)
ePRO/eCOA used with licensed questionnaires.
IRB requirements for add-on modules (eConsent/ePRO)
Labmatrix is a software application that manages the operational aspects of collaborative clinical and translational research programs, including patient recruiting, consenting, sample management (biobanking), experimental characterization of the samples and tracking of patient clinical profiles.
Data Warehousing in Pharma: How to Find Bad Data while Meeting Regulatory Req...RTTS
In the U.S., pharmaceutical firms must meet electronic record-keeping regulations set by the Food and Drug Administration (FDA). The regulation is Title 21 CFR Part 11, commonly known as Part 11.
Part 11 requires regulated firms to implement controls for software and systems involved in processing many forms of data as part of business operations and product development.
Enterprise data warehouses are used by the pharmaceutical and medical device industries for storing data covered by Part 11. QuerySurge, the only test tool designed specifically for automating the testing of data warehouses and the ETL process, is the market leader in testing data warehouses used by Part 11-governed companies.
For more on QuerySurge and Pharma, please visit
http://www.querysurge.com/solutions/pharmaceutical-industry
Data Warehouse Testing in the Pharmaceutical IndustryRTTS
In the U.S., pharmaceutical firms and medical device manufacturers must meet electronic record-keeping regulations set by the Food and Drug Administration (FDA). The regulation is Title 21 CFR Part 11, commonly known as Part 11.
Part 11 requires regulated firms to implement controls for software and systems involved in processing many forms of data as part of business operations and product development.
Enterprise data warehouses are used by the pharmaceutical and medical device industries for storing data covered by Part 11 (for example, Safety Data and Clinical Study project data). QuerySurge, the only test tool designed specifically for automating the testing of data warehouses and the ETL process, has been effective in testing data warehouses used by Part 11-governed companies. The purpose of QuerySurge is to assure that your warehouse is not populated with bad data.
In industry surveys, bad data has been found in every database and data warehouse studied and is estimated to cost firms on average $8.2 million annually, according to analyst firm Gartner. Most firms test far less than 10% of their data, leaving at risk the rest of the data they are using for critical audits and compliance reporting. QuerySurge can test up to 100% of your data and help assure your organization that this critical information is accurate.
QuerySurge not only helps in eliminating bad data, but is also designed to support Part 11 compliance.
Learn more at www.QuerySurge.com
Everything related to CDM. Importance of CDM, Flow Activities in Clinical Trials, Data Management Plan, Database Designing, Data Management tools, Essential Characters of the database, Standard Global Dictionaries, Data Review and Validation, Query Generation, Database Lock, Technology in CDM, and Professionals of CDM.
• A competent professional with an experience of 11.5 years in:
Clinical Database Programming Data/Team Management Clinical Research
Process Improvement Coordination & Liaison Project Management
• Currently designated as Lead Engineer 2 – with Pure Software Solutions and working as Project Consultant for the client, Oracle, Hyderabad.
• Diverse experience of working for world’s leading pharma companies and strong knowledge of best practices followed across these companies for Clinical Data Management
• Deft in interpreting & communicating required information to facilitate decision making process of the top management
• Experienced in planning & executing data capture and management tasks in a timely / accurate manner to ensure high level quality & productivity of Clinical Data Management
• Skilled in effectively managing documents for streamlining systems to facilitate achievement of organizational objectives
• Proficient at carrying out Clinical Research beyond established markets
• Effective at building & maintaining relationships with the stakeholders and with a quick TAT of their queries
• Possess excellent communication, innovative, planning, negotiation, analytical and problem solving skills
• Goal settings, appraisal discussions and identifying key development areas for Team Leads and their respective team members.
• Ensure that all the projects are implemented successfully, by reviewing documents and sending instructions to the implementation engineers for successful installation of project in Customer environment.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
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The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
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In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
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Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
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Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
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A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
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Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
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2. Clinical Data Management is
involved in all aspects of
processing the clinical data,
working with a range of
computer applications,
database systems to support
collection, cleaning and
management of subject or
trial data.
GRATISOL LABS TRAINING
MATERIAL
3. What is Clinical Data
Management
Clinical Data Management is
involved in all aspects of
processing the clinical data,
working with a range of
computer applications, database
systems to support collection,
cleaning and management of
subject or trial data.
GRATISOL LABS TRAINING
MATERIAL
4. Clinical Trial Data
Clinical Data Management is the collection,
integration and validation of clinical trial data
During the clinical trial, the investigators collect
data on the patients' health for a defined time
period. This data is sent to the trial sponsor, who
then analyzes the pooled data using statistical
analysis.
GRATISOL LABS TRAINING
MATERIAL
5. Why CDM
Review & approval of new drugs by
Regulatory Agencies is dependent
upon a trust that clinical trials data
presented are of sufficient integrity
to ensure confidence in results &
conclusions presented by pharma
company
Important to obtaining that trust is
adherence to quality standards &
practices
Hence companies must assure that
all staff involved in the clinical
research are trained & qualified to
perform data management tasks
GRATISOL LABS TRAINING
MATERIAL
6. Key members
The Key members involved in Data
Management:
Clinical Data Manager
Database Administrator
Database Programmer
Clinical Data Coordinator
Clinical Data Associate
GRATISOL LABS TRAINING
MATERIAL
9. Multidisciplinary Teams in Clinical Trials
1. Clinical Investigator 11. Regulatory affairs
2. Site coordinator 12. Clinical Data Management
3. Pharmacologist 13. Clinical Safety Surveillance
4. Trialist/Methodologist Associate (SSA)
5. Biostatistician 14. IT
6. Lab Coordinator 15. IT/IS personnel
7. Reference lab 16. Trial pharmacist
8. Project manager 17. Clinical supply
9. Clinical Research 18. Auditor/Compliance
Manager/Associate
10. Monitor
GRATISOL LABS TRAINING
MATERIAL
10. Responsibilities of CDM
Study Setup
CRF design and development (paper/e-CRF)
Database built and testing
Edit Checks preparation and testing
Study Conduct
Data Entry
Discrepancy Management
Data Coding (using MEDRA and WHODD
dictionaries)
Data review (Ongoing QC)
SAE Reconciliation
Data Transfer
Study Closeout
SAE Reconciliation
Quality Control
Database Lock
Electronic Archival
Database Transfer GRATISOL LABS TRAINING
MATERIAL
11. CDM Process Overview
Startup phase Conduct phase Close out phase
CRF Design Discrepancy
management - Coding of
Query to Database QC
medical terms
Quality check investigator
Protocol
Data entry
design
Database Design
Database
Database Lock
Quality check updates
Edit Checks
Quality check
Database
activated
GRATISOL LABS TRAINING
MATERIAL
12. Study Start Up Process Review
CRF design
Protocol Database
design
Validation/
derivation
Procedures
Activated database
ready to accept
production data
GRATISOL LABS TRAINING
MATERIAL
13. CRF Design/Review
A representation of the study as outlined in the protocol is made
(including CRF completion guidelines if necessary). Therefore a
final protocol needs to be available before this activity can be
initiated.. CRF design usually takes about three rounds: First draft
(rough without detail but correct content), second draft (as good as
we can get it) and final version. We need input from our sponsor to
correct draft versions and to approve the final version.
Traditional Paper Based Case Report Forms
e-CRF (Electronic Case Report Form)- Study information
directly entered into computer.
e-CRF is prepared by using:
ORACLE CLINICAL
CLINTRIAL
GRATISOL LABS TRAINING
MATERIAL
14. Paper CRF e-CRF
GRATISOL LABS TRAINING
MATERIAL
15. How many CRFs do you need?
-Eligibility or Screening
form
-Physical Exam form
-Enrollment form
-Medical History form
-AE form/ SAE form
-Concomitant therapy form
-Blood test form
-Laboratory test form
-Follow-up Visit form LABS TRAINING
GRATISOL
MATERIAL
16. Data Base Design
Data from a clinical trial will
be collected and stored in
some kind of computer
system.
A database is simply a
structured set of data.
A collection of rows and
columns.
--Excel Spreadsheet
--Oracle application
GRATISOL LABS TRAINING
MATERIAL
17. DBMS:
MS Access XP, MS Excel XP
Oracle Clinical
Clintrial
Phaseforward InForm
medidata Rave
GRATISOL LABS TRAINING
MATERIAL
18. CRF Annotation
An annotated CRF is generally defined as a blank CRF
with markings, or annotations, that coordinate each data
point in the form with its corresponding dataset name.
Essentially, an annotated CRF communicates where the
data collected for each question is stored in the database.
CRF Annotation is the first step in translating the CRFs
into a database application.
CDM annotates the CRFs by establishing variable names
for each item to be entered.
Reviewed by CDM and Statistician
GRATISOL LABS TRAINING
MATERIAL
20. Validation Checklist:
Validation Checklist describes in detail which data shall be
checked and queried if necessary. The programming of the
checks occurs according to this checklist. Before the
programming starts, the sponsor will be asked to give
approval of this Validation Checklist.
Test subjects are entered in the database to test the entry
screens and the programming. The exact number of test
subjects is not standard, but every check has to pass and
fail (negative and positive proof) at least once.
GRATISOL LABS TRAINING
MATERIAL
21. Database set up and testing
Database setup and testing are always performed in a
secure, non study data environment. Only when a
database has been reviewed and fully tested, will it be
set in ‘production’, a separate environment where
only study data will be entered. Changes in structure
or programming will always first be performed and
tested in the non study data environment before they
are made effective in the ‘production’ database.
GRATISOL LABS TRAINING
MATERIAL
23. Study Conduct Process Review
Data entry/
Activated DB Data loading Discrepancy
(CRF and management
external data)
Safety data Coding terms
reconciliation
Query
generation
Resolution
and/or update
of database
Manual
checks/QC
GRATISOL LABS TRAINING
MATERIAL
24. CRF Tracking
Logistic way if it is paper based study.
EDC-electronic data capture if it is e-
CRF.
Data Entry
Data entry is a process of
entering/transferring data from case
report form to Clinical Data
Management System (CDMS).
Data Entry:
1) Single data Entry
2) Double Data Entry
GRATISOL LABS TRAINING
MATERIAL
25. Discrepancy Management
Discrepancy management is a
process of cleaning subject data in
the Clinical Data Management
System (CDMS), it includes
manual checks and programmed
checks. Trivial discrepancies are
closed as per self evident
correction method or Universal
ruling and discrepancies which
require response from the site are
queried by raising Data
Clarification Forms (DCF).
GRATISOL LABS TRAINING
MATERIAL
26. Medical Coding
The medical coding for a study is done
as per the project specific protocol
requirement. The dictionaries used for
a study are:
Adverse Events: MedDRA (Medical
Dictionary for Regulatory Activities)
Medications: WHODD (World Health
Organization – Drug Dictionary)
Manual coding is performed using
Thesaurus Management System (TMS)
which is integrated with our Clinical
Data Management System (CDMS).
GRATISOL LABS TRAINING
MATERIAL
27. SAE Reconciliation
• Serious Adverse Event (SAE) data
reconciliation is the comparison of key
safety data variables between Clinical Data
Management System (CDMS) and Master
Drug Safety Database (MDSD).
Reconciliation is performed to ensure that
events residing in both systems are
consistent.
GRATISOL LABS TRAINING
MATERIAL
28. Study Close out Process Review
Discrepancy
management
Safety data
Coding terms
reconciliation
Query
generation
Resolution
and/or update
of database
Manual
checks/QC/
CRF tracking
Database lock
& freeze
GRATISOL LABS TRAINING
MATERIAL
29. Quality Control
• Quality Should be maintained for overall study
by performing Quality checks at intervals for all
data points (Critical & Non-Critical) prior to
database lock.
• QC helps to ensure that all the data processed is
accurate, clean and Correct.
GRATISOL LABS TRAINING
MATERIAL
30. Database Lock
The database lock for a study is done
to ensure no manipulation of study
data during the final analysis.
Database lock for a study is done
once all data management activities
are completed. This includes the
database lock checklist which
ensures the same. Some of the
activities included in database lock
checklist are All discrepancies
closed, DCFs received and updated,
coding complete, SAE
Reconciliation process complete etc.
GRATISOL LABS TRAINING
MATERIAL
31. Analysis & Reporting Process Review
Database Data
release extraction/
Mapping
Statistical
report
generation
E-publishing
Creation of Published
Submission of
Clinical study tables, figures
CSR
report(CSR) and listings
GRATISOL LABS TRAINING
MATERIAL
32. Objectives of CDM
CDM is a vital vehicle in Clinical Trials to ensure:
The Integrity & quality of data being transferred from trial subjects to
a database system
That the collected data is complete and accurate so that results are
correct
That trial database is complete and accurate, and a true
representation of what took place in trial
That trial database is sufficiently clean to support statistical analysis,
and its subsequent presentation and interpretation
GRATISOL LABS TRAINING
MATERIAL
33. Importance of CDM
CDM has evolved from a mere data entry process to a much diverse process today
It provides data and database in a usable format in a timely manner
It ensures clean data and a ‘ready to lock’ database
GRATISOL LABS TRAINING
MATERIAL
34. CDM Professionals
CDM Professionals:
o Pharmacists
o Graduates/Post graduates in Life Sciences, IT, Statistics
o Graduates with post graduation diploma in Clinical Research
o Licensed Medical Practitioners
ICH.E6.5.5.1: Utilize qualified individuals to:
o Supervise overall conduct of trial (Project Manager)
o To handle and verify the data (Data Manager)
o To conduct the statistical analysis (Biostatistician)
o To prepare study reports (Medical Writer)
GRATISOL LABS TRAINING
MATERIAL
35. DM Role in Clinical Research
Data Management Role in Clinical Research:
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
Continued…
GRATISOL LABS TRAINING
MATERIAL
36. DM Role in Clinical Research
Data management starts with the creation of the study protocol
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
GRATISOL LABS TRAINING
MATERIAL
37. Mission of CDM
• Consistency
• Accuracy
• Validity
• Archiving
GRATISOL LABS TRAINING
MATERIAL
38. DATA MANAGEMENT WORKFLOW
Receipt of CRFs
First Pass Second Pass
(CRF Tracking/Filing) Clinical Data Auto
Entry Entry
Management Coding
Batch
Validation
Data
Clarification
Form Thesaurus
(DCF)
DCF Discrepancy Manual
Resolutions Management Coding
SAE
Quality Control
Reconciliation
Plan
Database
Lock
GRATISOL LABS TRAINING Electronic
MATERIAL Archival
39. For further info & Guidance
A Training Divison of Gratisol Labs,
Plot No-70, Road No.10,
Banjara Hills, Banjara Hills,
Hyderabad-500 034
Ph: +91 40 65741017, 64615852
Mobile: +91 8885198390 , 9705790302
Web: www.gratisol.com
E-mail – training@gratisol.com
GRATISOL LABS TRAINING
MATERIAL