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.
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
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.
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
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.
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.
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.
Explaining the importance of a database lock in clinical researchTrialJoin
One of the most crucial aspects of research is clinical data management or CDM. Proper CDM will generate results with excellent quality, integrity, and reliability. Quality data is essential in order to support the final conclusions of a certain study.
The person responsible for this area of research is called a clinical data manager. This job position can be filled by a PI, a study coordinator, or a CRA. No matter who fills this position at your site, data management has to be done promptly and correctly in order to generate the best results. Aside from all the other reasons why data management is so important, it’s also what determines the future IP (investigational product) development.
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.
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.
High variability in PK can be a characteristic of certain drug products which require different from ordinary strategies and study designs for establishing bioequivalence.
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.
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.
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.
Explaining the importance of a database lock in clinical researchTrialJoin
One of the most crucial aspects of research is clinical data management or CDM. Proper CDM will generate results with excellent quality, integrity, and reliability. Quality data is essential in order to support the final conclusions of a certain study.
The person responsible for this area of research is called a clinical data manager. This job position can be filled by a PI, a study coordinator, or a CRA. No matter who fills this position at your site, data management has to be done promptly and correctly in order to generate the best results. Aside from all the other reasons why data management is so important, it’s also what determines the future IP (investigational product) development.
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.
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.
High variability in PK can be a characteristic of certain drug products which require different from ordinary strategies and study designs for establishing bioequivalence.
Clinical trial data wants to be free: Lessons from the ImmPort Immunology Dat...Barry Smith
Presentation to the Clinical and Research Ethics Seminar, Clinical and Translational Science Center, Buffalo, January 21, 2014
https://immport.niaid.nih.gov/
http://youtu.be/booqxkpvJMg
ANZCTR: Why we need better sharing of clinical trial dataARDC
Presentation by Ailsa Langford, ANZCTR. Presented at the ANDS/Intersect sharing health-y data: challenges and solutions II workshop on 26th October 2016
High variability in PK can be a characteristic of certain drug products which require different from ordinary strategies and study designs for establishing bioequivalence.
Good Regulators of Pharmaceuticals (GRP) 22 October 2014Ajaz Hussain
Sharing thoughts on what makes a Good Regulator of Pharmaceuticals with pharmacy students at the Universities of Minnesota and Iowa. A point of emphasis on "we all are regulators" is explained and three areas for learning - (a) Systems and Integrative Thinking, (b) Argumentation and (c) Behavioral Economics described.
I hope you, the viewers, will also find some value in reviewing these slides. If you are a student and have some questions please feel free to drop me a email (a2zpharmsci@msn.com).
Educating the Next Generation Pharmacist for Industry. The Panjab University ...Ajaz Hussain
The Panjab University Pharmaceutical Science Oration 2014: Educating the Next Generation Pharmacist for Industry.
“The dream begins with a teacher who believes in you, who tugs and pushes and leads you to the next plateau, sometimes poking you with a sharp stick called ‘truth’.“
Plato, the Republic
What are the most influential ideas, concepts, and developments introduced by ‘pharmaceutical scientists’ over the last 50 years?
How have these ideas/concepts introduced into practice?
How can we improve?
Redefining ETL Pipelines with Apache Technologies to Accelerate Decision-Maki...Eran Chinthaka Withana
Pharmaceutical and medical device makers spend over $130bn each year collecting and analyzing new data, mostly through clinical trials. It costs over $1.8bn to bring a new drug to market, and over $4bn when factoring in the cost of failures. By more efficiently understanding and analyzing this data, new drugs can reach patients quicker, safer, and at a lower cost.
In this presentation, Eran will discuss how ETL pipelines can be built using the Apache and other open source projects to improve clinical trial development. We will examine how the system is built, the challenges we faced and how we are able to reduce cost, accelerate execution time, and improve results. We will also demonstrate how reliable resource allocation, scalable data ingestion adapters, on-demand and fault tolerant job deployments, and monitoring benefit clinical trial decision-making and execution.
January 23, 2017
The Fifth Annual Health Law Year in P/Review symposium featured leading experts discussing major developments during 2016 and what to watch out for in 2017. The discussion at this day-long event covered hot topics in such areas as health policy under the new administration, regulatory issues in clinical research, law at the end-of-life, patient rights and advocacy, pharmaceutical policy, reproductive health, and public health law.
The Fifth Annual Health Law Year in P/Review was sponsored by the Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School, Harvard Health Publications at Harvard Medical School, Health Affairs, the Hastings Center, the Program On Regulation, Therapeutics, And Law (PORTAL) in the Division of Pharmacoepidemiology and Pharmacoeconomics at Brigham and Women’s Hospital, and the Center for Bioethics at Harvard Medical School, with support from the Oswald DeN. Cammann Fund.
Learn more on our website: http://petrieflom.law.harvard.edu/events/details/5th-annual-health-law-year-in-p-review
Slides from Society for Clinical Trials, The goal of this CTTI-sponsored project was to describe current clinical monitoring methods for a range of clinical trial types, and to explore the rationale for the use of those methods.
A thoughtful presentation on participation in clinical trials from the Thomas Jefferson University team at the 2017 CURE OM Patient & Caregiver Symposium.
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.
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.
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
Who needs fast data? - Journal for Clinical Studies KCR
How “no news” during the life of a trial is bad news, and what data management (among other things) can do to help when ensuring access to fast data? Get to know this and more about smart e-solutions in the newest article of Kaia Koppel, Associate Director, Biometrics & Clinical Trial Data Execution Systems at KCR, in the recent issue of Journal for Clinical Studies (p.40-21).
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.
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.
A Pharma/CRO Partnership in the Design and Execution of Paperless Clinical Tr...Target Health, Inc.
DIA 2019 presentation by Dr. Jules Mitchel with Michelle Eli (Lilly) and Tom Haag (ex-Novartis) based on their experience with Lilly collaborating on Target Health's paperless clinical trial system.
Based off of project findings, this presentation highlights the process of identifying KRIs for site quality for digestive disease studies and demonstrates the practical application of surrogate KRIs in risk-based monitoring.
Data Integrity in Decentralized Clinical Trials (DCTs)InsideScientific
Experts expand on the need for a comprehensive understanding of all sources of data in DCTs, and the need to evaluate those data centrally in real time to mitigate the risks associated with their capture (including data capture at the edge of the network (wearables)).
Every disruptive innovation must be complemented by adapted procedures, and this also applies to decentralized clinical trials (DCTs). Traditionally, sites entered clinical trial data in an Electronic Data Capture (EDC) system and these source data were verified at the site to confirm accuracy. Risk based monitoring focused on site level metrics such as screen failure rates, query rates, Serious Adverse Events (SAEs) reported, missed/late visits, etc. With DCTs, as source data are collected directly from participants this is no longer an option and a different approach is required to ensure the quality and integrity of the data. As a rule, a comprehensive understanding of all sources for data capture in a clinical trial and the process for centralization is essential. Also, it is important to evaluate the data collected in real time to allow early interventions that will ensure data integrity for regulatory submission.
In this webinar, Chitra Lele describes how centralized monitoring strategies can help aggregate and analyze data in real time and provide insights to a variety of functional teams across the trial continuum. Daniel Gutierrez describes how the Clinerion platform can boost data integrity in DCTs. The technology transforms global data sources to one query-able data model for structured medical data, while ensuring that the data keep its full resolution and integrity during aggregated queries.
Pierre Etienne talks about the expanding role of mobile Health Care Professionals (HCPs) and their crucial role in protecting data integrity. Clifton Chow finishes with a comparison of several artificial intelligence (AI) based binary classifiers for detecting the integrity of data obtained from Internet of Things (IoT) enabled wearable sensors.
Role of Clinical Data Management in Risk-Based MonitoringClinosolIndia
Clinical Data Management (CDM) plays a significant role in the implementation of Risk-Based Monitoring (RBM) within clinical trials. RBM is an approach that focuses monitoring efforts on areas of highest risk, thereby optimizing resource allocation, enhancing data quality, and ensuring patient safety. Here's how CDM contributes to RBM
The Role of Technology in Streamlining Clinical Trial ProcessesClinosolIndia
Technology plays a significant role in streamlining clinical trial processes, enhancing efficiency, data quality, and participant engagement. Here are some key areas where technology contributes to the optimization of clinical trials
Building a Next Generation Clinical and Scientific Data Management SolutionSaama
Srini Anandakumar, Senior Director of Clinical Analytics Innovations for Saama Technologies, discussions next-generation data management solutions at the Drug Development Networking Summit on April 11, 2019, in Bridgewater, New Jersey.
In the course of any clinical trial, there are risks associated with specific activities and tasks. This webinar will highlight some of these key risk areas and provide guidance on combining technology with best practices to help mitigate risks.
How To Optimize Your EDC Solution For Risk Based Monitoringwww.datatrak.com
This presentation presents best training practices to leverage EDC technology and risk-based monitoring to effectively and efficiently monitor clinical research.
Our focus is on the practical process of preparing your team to optimize the tools made available through an EDC solution.
This presentation is applicable to CRA’s, clinical project managers, clinical data managers, regulatory compliance professionals, and those involved in the design and implementation of risked-based monitoring plans.
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.
As per EU MDR, Post Marketing Clinical Follow-up (PMCF) is a continuous process where device manufacturers need to proactively collect and evaluate clinical data of the device when it is used as per the intended purpose. EU MDR gives more emphasize on PMCF data to confirm the safety and performance of the device throughout its expected lifetime, ensure continued acceptability of identified risks and detect emerging risks based on factual evidence.
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This presentation gives effective solutions to outliers issue in bioequivalence trials. It described what would be acceptable to Regulatory agencies as well as some new approaches.
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Scientific integrity calls for some basic originality. Plagiarism can destroy this original creativity and ideation. This presentation defines plagiarism (stealing from others' works) and some of the creative and systematic remedies.
There are several dimensions in Pharmaceutical ethics -- Practice-, research- and community oriented. This presentation mainly deals with Clinical research oriented Ethics.
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Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
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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
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Study Resources:
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2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
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4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
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Developing Protocols & Procedures for CT Data Integrity
1. PROTOCOLS &
PROCEDURES FOR
CLINICAL TRIAL DATA
INTEGRITY
Dr. Bhaswat S. Chakraborty
Senior Vice President, R&D
Cadila Pharmaceuticals Ltd.
1
Presented at the International Conference
on CRO/Sponsor Summit on “Data Integrity in Clinical
Research” at Hyderabad, India, 21-22 July, 2016
2. CONTENTS
Clinical trial data
Approaches to build and maintain data integrity
CT Data management system
On-site monitoring
Centralized monitoring
Alternate monitoring
Risk based monitoring (RBM)
Data & safety monitoring plan (DSMP)
Data & safety monitoring board (DSMB)
Training & inspection-readiness
Concluding remarks
2
3. Investigational
Sites
Product
Management
Project
Management
Drug & Clinical Trial Development
Extended Picture
IRB Regulatory
Documents
Relationship
Building
eMails
Partners &
Affiliates
Meetings
CROs
Contracts
Knowledge
Information
Safety
Communication
Resource
Management
Data Capture
Data Management
Multidirectional Flow of Data and Decisions
3
4. CLINICAL TRIAL DATA AND
DOCUMENTS
Study and site feasibility documents
Protocol
Inclusion/Exclusion criteria
Informed Consent
Investigators brochure
Training documents and data
Data
Randomisation, Blinding
CRF/ECRF (Demographics and site visit data)
Primary and secondary outcome variables (end points)
Clinical procedures and study conduct data
Investigational products: Supply, Inventory, Handling & Usage, Retention
Safety monitoring and signal detection
Subject withdrawal and retention data
Data and safety monitoring committee (activities, data, reports)
Data management and data monitoring including SDV by Sponsor/CRO
Data recording and reporting
Statistical analysis
Study reporting
..
4
5. WHY DO WE NEED A DATA
MANAGEMENT & DATA INTEGRITY
SYSTEM?
Enormous volumes of data
Example, a Phase-III trial in 10 centres with
100 patients each
60 pages of CRF for each recruited patient
20 fields each page
40 pages of screening form for each candidate
patient
20 fields each page
[1000 (60 x 20)] + [1500 (40 x 20)]
= 12, 00000 + 12, 00000
= 24,00000 specific data points 5
6. CLINICAL TRIAL DATA
Useful only if it is clean & accurate
Data processing must be
real-time
subject randomization
management of clinical trials materials
laboratory uploads
patient diary data
Integrated
Consistent
Accurate
Data structures must be
Standard
Validated
Data transfer method must be
Standard
Validated
6
7. DATA INTEGRITY IN
RESEARCH
Research integrity depends on data integrity
Includes all aspects of collection, use, storage and sharing
of data.
Data integrity is a shared responsibility
Everyone involved in the research is responsible.
The ultimate responsibility belongs to the PI.
However, there is a broader role and responsibility for the
institute and scientific community.
Transparency of the research data is required
7
Free and accurate information exchange is
fundamental to scientific progress
Van Eyk J., JHU NHLBI Innovative Proteomics Center on Heart Failure
8. SOURCES OF DATA INTEGRITY
& ITS LACK
Data integrity is based on accurate and traceable:
Collection
Organization & Storage
Analysis
Reporting
Data integrity can be compromised numerous ways:
Malicious or unethical intentions
Human mistakes and naivety
Technical error
Misinterpretation
8
Accurate & complete data = High quality data = QbD &
RBM data = IRB+DSMP+DSMB data
9. CONSEQUENCES OF NON-
INTEGRITY
Personal loss
Blocked scientific
progression
Impaired technology
development
Damage to the institution,
sponsors or CRO
Tarnished public
perception of science
Damage to or loss of patent
protection
9
Van Eyk J., JHU NHLBI Innovative Proteomics Center on Heart Failure
10. APPROACHES TO BUILD AND
MAINTAIN DATA INTEGRITY
Monitoring, monitoring ,,,, monitoring
CTDM Systems
On site monitoring
Centralized monitoring
Clinical trial quality assurance units (QAUs)
Sponsors often use internal or external QAUs
Not required by regulation
QbD and Risk based monitoring
Building QbD
Risk identification & assessment
Critical attributes and riskcategorization thereof
Plans and processes
Targeted monitoring 10
11. CLINICAL DATA MANAGEMENT
SYSTEM (CDMS)
Data Capture Strategy
Remote Data Capture
Portal Data Capture
Processes
Adverse Event Monitoring System
Compliance (GCP/GLP) Monitoring
Workflow Monitoring
Analytical Data Processing
Statistical Data Processing
Systems
Data Extraction
GLIB
TMS/Dictionaries
Reports
Validation
12
13. FIRST & SECOND ENTRY
DATABASES
First entry (first database)
For accurately capturing the data to be cleaned & analyzed
by SAS datasets, data entry personnel entered data from
paper or eCRFs
Second entry (second database)
Errors can due to data transcription
Thus all data are entered a second time into a second, but
identical, database by a second personnel
Entered values are compared between the two databases
If the two entries match, the entered value was accepted
If not matched, the personnel makes a decision whether the
initial or second data entry value is correct.
14
14. CLINICAL DATA MANAGEMENT
SYSTEM (CDMS)..
Data Capture Strategy
Remote Data Capture
Portal Data Capture
Processes
Adverse Event Monitoring System
Compliance (GCP/GLP) Monitoring
Workflow Monitoring
Analytical Data Processing
Statistical Data Processing
Systems
Data Extraction
GLIB
TMS/Dictionaries
Reports
Validation
15. Raw
Data
DATA EXTRACTION, CLEANING
& LOCKING
Real time query
Are the
queries answered?
Yes
No
Data cleaning
1. Detecting & diagnosing errors
2. Editing incorrect data
3. Integrated data passage
4. Outlier determination
5. Robust estimation of analytical parameters
Clean data
Approval required
Can this data
be locked?
Yes
NoRepeat
Observation/
Omission
Locked data Analysis
16. ON-SITE MONITORING FOR
INTEGRITY
Effective monitoring is critical to:
Human subject protection
Conduct of high quality studies‐
Data Integrity
On-site monitoring
An in-person evaluation at the investigational sites
Can identify
data entry errors
missing data in source records or CRFs
provide assurance that study documentation exists
assess compliance with the protocol and investigational product
quality of the overall conduct of the trialat that site
particularly helpful early in a study, especially if protocol is complex and
includes novel procedures
lead to meaningful training efforts 17
17. CENTRALIZED MONITORING
A remote evaluation carried out by sponsor personnel or CRO
By clinical monitors, data management personnel, or statisticians
At a location other than the sites
Can provide many of the capabilities of on-site monitoring as well as
value additions
Success of centralized monitoring depend on various factors
Use of electronic systems
Access to subjects’ electronic records
Timeliness of data entry from paper CRFs
Must ensure that record keeping, data entry, and reporting and
supporting source data are well-defined & accessible
Must also identify in their monitoring plan when one or more on-site
monitoring visits are required
18
Alternative monitoring techniques can exist
18. ALTERNATE MONITORING
Monitor/review data quality
missing data, inconsistent data, data outliers, and potential protocol
deviations
Conduct statistical analyses to identify data trends not easily detected by
onsite monitoring, such as
checks of range, consistency, completeness, unusual distribution of data within
and between sites
Analyze site characteristics, performance metrics
high screen failure, withdrawal rates, high eligibility violations, delays in
reporting ..
Verify critical source data remotely
where accessible; CRF data are according to the protocol?
Complete administrative and regulatory tasks
IRB approvals, IP accountability, randomization and CRF data, previously
requested CRF corrections are made
Communication with Study Site Staff
Teleconferences, videoconferencing, email
Review site’s processes, procedures, and records technique
19
19. RISK-BASED MONITORING
Basis: Monitoring activities prevent or mitigate important and
likely sources of error in conduct, collection, and reporting of
critical data and processes necessary for human subject
protection and trial integrity
Importance of Critical Quality Factors:
Procedures critical to collecting reliable data for study endpoints
Consistency across sites or in a highly specific manner in some sites
Procedures that won’t significantly impact data analysis or subject safety
1. Identify Critical Data and Processes to be Monitored:
IC verification, adherence to protocol eligibility criteria, accountability
and administration of IP, conduct, documentation & assessments related
to study endpoints & red safety assessments
Procedures essential to trial integrity, e.g., blinding is maintained, both at
the site level and at the sponsor level
20
Some types of errors in CT is more important than others
(error in age v/s error in endpoint)
20. RISK-BASED MONITORING..
2. Risk Assessment:
Risk identification based on trial design or investigational product
Risks assessed and prioritized by considering the following:
the likelihood of errors occurring
the impact of such errors on human subject protection and trial integrity
the extent to which such errors would be detectable
3. Factors to consider while developing a monitoring plan:
Complexity of the study design may require increased frequency and extent of
review (adaptive designs, stratified designs, complex dose titrations..)
4. Monitoring Plan:
Description of each monitoring method & how it will be used to address important
risks and ensure the validity of critical data
Criteria for determining the timing, frequency, and extent of planned monitoring
activities
5. Documentation of monitoring:
Date of the activity and the individual(s) conducting and participating in it
Summary of the data or activities reviewed
Description of noncompliances, potential noncompliance, data irregularities..
A description of any actions taken
21
Use the results of risk assessment in developing monitoring
plan and type and intensity of monitoring to address this risks
21. DATA & SAFETY MONITORING PLAN
DECISION TREE
22
Clinical Research?
Yes No
Clinical Trial?
Phase III or Multicenter
Blinded, high risk interventions or vulnerable populations
Yes No IRB
Yes No
IRB + DSMP
IRB + DSMP + DSMB
Yes No
Source: NIDA
22. MONITORING REQUIRED IN
DIFFERENT PHASES OF STUDY OF
CLINICAL TRIAL
Phase I trial involves relatively high risk to a small N
Usually the study investigator performs continuous monitoring of
safety
Phase II trial follows phase I with more N
Toxicity and outcomes are confounded by disease process
Monitoring similar to that of a phase I trial or additional monitoring by
experts or DSMB
Phase III trials frequently compares a new treatment
to a standard treatment or to no treatment
Large N
Short-term risk is low, but long term effects of IP to achieve significant
safety or efficacy difference from the control
May require a DSMB to perform monitoring functions 23
23. DATA AND SAFETY
MONITORING PLAN (DSMP) &
BOARD (DSMB)
DSMPs promote data integrity as well as safety and protection
of trial subjects
Many common activities with other trials and some unique
activities for a particular trial depending on the risk, size and
complexity
Subject safety, data integrity and validity are the foremost
consideration for a DSMP
For low risk studies an annual safety monitoring (by PI) may be
enough
For higher risks studies, safety monitoring will have to be done
more frequently as required by an established DSMB/DMC
Usually DSMBs are not required for Phase I studies but only for
Phase II and III, but exceptions exists
24
24. WHEN DO YOU NEED A DSMB?
When do you need a DSMB?
To ensure that participants are not exposed to undue risk
To ensure that the study will yield usable results
To do Interim Analyses and/or change protocol study design based on IA
To maintain trial integrity
To ensure unbiased, timely publication of results
Composition:
Researcher(s)/Clinician knowledgeable of the science of the study
Biostatistician and/or Epidemiologist (study design/analysis)
Research subject advocate and/or ethicist (research ethics)
SOP
The DSMB chairman and members (quorum) participate
Frequency at which the study will be evaluated, e.g. annually,
quarterly etc.
Adverse event reporting requirements
A description of interim efficacy analyses, if appropriate
Study stopping rules, if appropriate
The distribution of DSMB reports 25
25. DATA AND SAFETY
MONITORING PLAN (DSMP)
The specifics of a DSMP depend upon the nature, size, complexity,
and risk level
To reduce harm or injury to study individuals
The minimum required content for a DSMP includes:
Assessment of levels of risk
A plan for safety review (by whom and how often)
Anticipated adverse events
AE grading and attributions
A plan for unanticipated and/or SAE reporting
A plan for periodic or annual reporting of AEs
Ensure compliance with the principles and process of IC
Assessment of protocol compliance including violations and/or
deviations
A plan for compliance with privacy related regulations (e.g., HIPAA)
26
26. TRAINING OF CLINICAL
INVESTIGATORS & DATA
PROCESSORS/ANALYSTS
Clinical trial monitors train the CI and site staff during a study
Training on the conduct of a study and appropriate instruction during the study
(e.g., when changes are made to the protocol)
Teleconferences, webcasts, online training modules and communication methods for
training and feedback & significant change notification
Sponsors who plan less frequent monitoring should consider
Monitoring plus discussion of CI’s and site staff’s responsibilities, feedback, and
additional training
Train the trainers, data managers, analysts & report writers
Delegating monitoring to a CRO
Need written transfer of any obligations from a sponsor to a CRO & CRO requires to
comply with the regulations
Sponsors should evaluate CRO compliance with regulatory requirements &
contractual obligations
e.g., periodic review of monitoring reports and vendor performance or quality metrics,
communication between the sponsor & CRO regarding monitoring progress and
findings
Sponsors are ultimately responsible for all data & monitoring
27
27. PREPARING AS A SPONSOR OR A
CRO FOR FDA AUDIT
Registration of studies on http://www.clinicaltrials.gov/
For CTs of drugs and biologics other than Phase-I
Selection and monitoring of clinical investigators
TMF & SMFs
Sponsor/CRO provide the investigators with all necessary information prior to initiation
of CT
Serious deviations from the approved investigational plan (which includes the study
protocol) or FDA regulations
Terminated sites and the circumstances
Non-compliant investigator still continuing in the study
Selection of Monitors
Criteria for selecting monitors and how monitors meet those criteria
How more than one person handles monitoring
Monitoring procedures and activities
Procedures, frequency, scope, and process to monitor the progress of the clinical
investigation
Relevant SOPs are followed
Activities
Review of Site Records
28
28. PREPARING AS A SPONSOR OR A
CRO FOR FDA AUDIT..
QAUs (CT QAUs are not required by regulation)
However, if the sponsor/CRO has a QAU, its organization & operation should be
described in SOPs; separation of functions between QA auditing and monitoring of
clinical trials
List of audited studies
Safety/AE reporting
Safety reports of potential serious risks within 15 calendar days; within 7 calendar days if
unexpected fatal or life-threatening suspected AE
AE reported to participating investigators (and to reviewing IRBs for device studies) as
required by the regulations.
Procedures (e.g., frequency, scope) the sponsor/CRO uses for the receipt, evaluation, and
monitoring of safety information/unanticipated AEs
All Study Tabulations
All Investigator Tabulations
All pertinent studies (form 1572) included in the above two categories
Reasons for non-inclusion
Data tabulations on each subject in each clinical trial in an NDA
29
29. PREPARING AS A SPONSOR OR A
CRO FOR FDA AUDIT ...
Electronic records and electronic signatures
Regulatory requirements for the clinical data are the same regardless of the
type of computer system used by the clinical site
Data must be reliable and usable for evaluating the safety and/or effectiveness
of FDA-regulated products
Electronic Records (CFR Part 11) and electronic signatures are maintained in electronic
format (in some cases in addition to paper format); SOPs for procedures
Data collection:
Procedures for collection, retention, and transmission of data at each clinical site; File
transfer protocols to and from processing center to and from site
Original data entries and changes cannot be made by anyone other than the clinical
investigators
How electronic data are reviewed during monitoring visits; SOPs for such reviews
Data Security;
Clear, justified authority to access the system
How the paper & computerized systems are accessed (e.g., password protected, access
privileges, user identification
How information is captured related to the creation, modification, or deletion of electronic
records (e.g., audit trails, date/time stamps); backup, disaster recovery, and/or
contingency plans; error messages or system failures handled/corrected
How the system and data are handled during site closure
30
30. CONCLUDING REMARKS
A CT is as good as the quality of its data
In an effort to ensure the integrity of clinical trial data, the FDA
has released requirements
Monitoring of data collection, review and analysis is essential to
ensure data integrity
Traditionally monitoring required an in-depth and comprehensive
examination of all collected data.
The risk-based monitoring (RBM) system is fundamentally
different as to how data managers review clinical data
Does not mandate a specific methodology but requireds an ideal strategy that
allows for faster time to market, reduces site monitoring costs and frees up
time and resources for value-added tasks
For complex Phase III (sometimes Phase II) trials require a
DSMP as well as a DSMB for overall data integrity or to stop the
trial
Training and audit (FDA/Client) readiness for data integrity
ensures high success rates
31
GLIB: global library is an organization wide central repository for containing standardized data definitions. TMS: thesaurus management system; e.g., Oracle TMS provides terminology services for Oracle Clinical, Oracle Remote Data Capture, Oracle Adverse Event Reporting System, and Oracle Life Sciences Data Hub. Allows access to any number of dictionaries, including multiple versions of the same dictionary ; supports any number of hierarchy levels
and supports custom or commonly used dictionaries, such as MedDRA, MedDRAJ, MedDRA SMQs, SNOMED, ICD9, WHO-ART, and WHO-Drug.
MedDRA or Medical Dictionary for Regulatory Activities is a clinically validated international medical terminology used by regulatory authorities and the regulated biopharmaceutical industry during the regulatory process, from pre-marketing to post-marketing activities, and for data entry, retrieval, evaluation, and presentation. In addition, it is the adverse event classification dictionary endorsed by the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH). MedDRA is used in the United States, European Union, and Japan. Its use is currently mandated in Europe and Japan for safety reporting.
Bullet 1 - that may be indicative of systemic or significant errors in data collection and reporting at a site