1. A clinical data management system (CDMS) is used to manage data from clinical trials by storing data entered in case report forms (CRFs) by investigators.
2. Data management involves planning, collection, entry, validation, manipulation, backup and documentation of data to create a high quality database. Commonly used CDMS tools include Oracle Clinical, ClinTrial, Macro and eClinical Suite.
3. Open source CDMS tools include OpenClinica, openCDMS, TrialDB and PhOSCo which are free. All CDMS tools ensure an audit trail and management of discrepancies according to roles and user access levels.
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.
The safety monitoring in a clinical trail accompanies by common practices in safety monitoring, communicating safety information among stakeholders in a clinical trail.
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: 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.
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.
The safety monitoring in a clinical trail accompanies by common practices in safety monitoring, communicating safety information among stakeholders in a clinical trail.
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: 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.
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 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.
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 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.
A Clinical Research Associate (CRA) is a professional who plays a crucial role in the management and monitoring of clinical trials. CRAs are typically employed by pharmaceutical companies, contract research organizations (CROs), or academic research institutions. Their primary responsibility is to ensure that clinical trials are conducted in compliance with the study protocol, applicable regulations, and Good Clinical Practice (GCP) guidelines
INSTITUTIONAL REVIEW BOARD (IRB/IEC).pptxRAHUL PAL
The International Council on Harmonisation (ICH) defines an institutional review board (IRB) as a group formally designated to protect the rights, safety and well-being of humans involved in a clinical trial by reviewing all aspects of the trial and approving its startup. IRBs can also be called independent ethics committees (IECs).
An IRB/IEC reviews the appropriateness of the clinical trial protocol as well as the risks and benefits to study participants. It ensures that clinical trial participants are exposed to minimal risk in relation to any benefits that might result from the research.
IRB/IEC members should be collectively qualified to review the scientific, medical and ethical aspects of the trial.
Per the FDA, an IRB/IEC should have:
At least five members.
Members with varying backgrounds.
At least one member who represents a non-scientific area (a lay member).
At least one member who is not affiliated with the institution or the trial site (an independent member).
Competent members who are able to review and evaluate the science, medical aspects and ethics of the proposed trial.
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 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.
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 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.
A Clinical Research Associate (CRA) is a professional who plays a crucial role in the management and monitoring of clinical trials. CRAs are typically employed by pharmaceutical companies, contract research organizations (CROs), or academic research institutions. Their primary responsibility is to ensure that clinical trials are conducted in compliance with the study protocol, applicable regulations, and Good Clinical Practice (GCP) guidelines
INSTITUTIONAL REVIEW BOARD (IRB/IEC).pptxRAHUL PAL
The International Council on Harmonisation (ICH) defines an institutional review board (IRB) as a group formally designated to protect the rights, safety and well-being of humans involved in a clinical trial by reviewing all aspects of the trial and approving its startup. IRBs can also be called independent ethics committees (IECs).
An IRB/IEC reviews the appropriateness of the clinical trial protocol as well as the risks and benefits to study participants. It ensures that clinical trial participants are exposed to minimal risk in relation to any benefits that might result from the research.
IRB/IEC members should be collectively qualified to review the scientific, medical and ethical aspects of the trial.
Per the FDA, an IRB/IEC should have:
At least five members.
Members with varying backgrounds.
At least one member who represents a non-scientific area (a lay member).
At least one member who is not affiliated with the institution or the trial site (an independent member).
Competent members who are able to review and evaluate the science, medical aspects and ethics of the proposed trial.
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.
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.
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
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
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 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.
What’s inside the DMP?
It includes all elements of Data management process
It specifies:
• What is the work to be performed?
• Who is responsible for work?
• Which SOP’s or guidelines will be applicable?
• What documentation and output will be collected or produces from trial?
Topics to cover in DMP
• CRF/eCRF creation
• Database design and structure
• Edit Check specification
• Study database testing and release
• Data or paper workflow
• Reports and Metrics
• Query management
• Managing lab data
• Management of other non-crf data
• Coding of reported terms
• Handling of SAE’s
• Transferring data
• Study database lock
DMP provides:
• Clear history for long term studies which has to go through complex lifecycle
• Provides location for documenting details on computer system to collect trial data recommended by FDA guidance document.
• As per the FDA guidance document: for “computerised system used in clinical investigation.”
• The section IV. F recommends:
• For each study the documentation should identify what software and hardware will be used to create, modify, maintain, archive, retrieve or transmit clinical data.
• This is not submitted to FDA but retained as part of study record.
• It needs to be made available for inspection by FDA
• Some companies have detailed DMP’s while some have concise with pointer to reference documents
Authorisation of DMP
• For internal CDM groups the lead, Clinical data manager, or senior data managers for study creates documents and signs it.
• Companies having contract between CDM group and other groups will have their representatives reviewing and DMP along with lead DM.
Revision of DMP
• During the course of an average phase II and phase III study, some critical data management process or a key computer application may change.
• DMP can be revised whenever there is a significant change.
• Any revision in DMP needs to be reviewed and verified by authorizing official.
DMP’s with CRO
• Sponsor may outsource CRO for some or all parts of DMP can be used.
• CRO’s may have more comprehensive DMP as compared to sponsor and most of the times the CRO’s DMP is used.
• An experienced DM from sponsor is supposed to review the DMP by sponsors.
• CRO collectively works with sponsor for any revisions in DMP.
• Sponsor should provide resources for creating DMP.
T R I A L M O N I T O R I N GQuality Remote Monitoring .docxssuserf9c51d
T R I A L M O N I T O R I N G
Quality Remote Monitoring:
The Tools of the Game
Penelope Manasco, MD
Outlining those technologies best able to raise the
data and process quality of risk-based monitoring.
A
critical aspect of risk-based monitoring
(RBM) is rapid access to a site’s clinical
data. In 2013, industry median values from
2009-2012 Phase II and III clinical trials (see
Figure 1 on facing page) showed that the
median time from electronic case report (eCRF)
entry to data manager query opened was 59 to 89
days. This is even more extraordinary when one
considers that the median time from subject visit
to query close (all queries including automatically
generated queries) ranged from 30 to 36 days.'
These findings emphasize that direct data en
try, either into the electronic data capture (EDC)
or eSource systems, provides significant value in
overseeing study conduct quality. Mitchel et al.2
reported on their experience implementing direct
data entry (DDE) and RBM in a clinical trial of 18
investigative sites in the U.S. and Canada study
ing 180 research subjects. In that trial, 92% of the
data was entered within one day of the subject
visit and 98% within eight days. Data review was
also faster with 50% of the data reviewed within 13
hours of data entry. Source data verification (SDV)
was completed at the site for approximately 20%
of the data within the EDC. There were changes on
0.8% of the pages, with the majority in three areas:
concomitant medications, medical history, and
clinical laboratory results.
The evidence above, coupled with the find
ing that SDV was not an adequate approach to
ensure trial quality,5 illustrates the importance of
technology and process changes that should be
implemented to enhance remote trial oversight
as envisioned by the FDA,2 European Medicines
Agency (EMA),5 and International Conference on
Harmonization (1CH) guidance6 documents on
RBM and quality management. The following tech
nology solutions can provide significant benefits
to implementing RBM and remote trial manage
ment.
Technology solutions
EDC and eSource
Direct data entry can be accomplished though
web-based EDC solutions and tablets, it is imper
ative that sites have adequate Internet access to
use tablets for direct data entry. Sites benefit from
eliminating transcription of documents. Moni
tors and data managers also benefit from having
immediate access to the data. Questions that
document good clinical practice (GCP) compli
ance can be incorporated into the EDC or eSource.
These fields (e.g„ detailing timing for vital signs,
informed consent processes) enable monitors to
conduct source data review remotely. Many data
managers may not be familiar with the additional
questions the monitor will want to have docu
mented, so cross-functional input into the EDC
is needed during design. Tablet setup and testing
ensures tablets work as needed by the site. The
initiation visit should i ...
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.
Electronic Data Capture (EDC) Systems: Streamlining Data CollectionClinosolIndia
Electronic Data Capture (EDC) systems are software platforms designed to streamline and enhance the process of collecting, managing, and monitoring clinical trial data electronically. EDC systems replace traditional paper-based data collection methods, offering numerous advantages for clinical research. Here's how EDC systems streamline data collection in clinical trials
Data mapping is the process of identifying and linking data from different sources in a clinical trial. This process is critical for ensuring that the data collected in a study is accurate, consistent, and reliable.
In a clinical trial, data may be collected from a variety of sources, including electronic medical records, case report forms, laboratory results, and patient-reported outcomes. Data mapping involves identifying the different types of data that will be collected in the study, determining how they will be collected, and creating a plan for linking the data together.
One of the key benefits of data mapping is that it helps to ensure data consistency across the study. By mapping out the data sources and creating a plan for how they will be linked, researchers can ensure that the data is accurate and consistent across all study participants and sites.
Data mapping is also important for ensuring that data can be analyzed and reported in a meaningful way. By linking the different types of data together, researchers can create a comprehensive dataset that can be analyzed using statistical methods to answer research questions and generate insights
VAT Registration Outlined In UAE: Benefits and Requirementsuae taxgpt
Vat Registration is a legal obligation for businesses meeting the threshold requirement, helping companies avoid fines and ramifications. Contact now!
https://viralsocialtrends.com/vat-registration-outlined-in-uae/
B2B payments are rapidly changing. Find out the 5 key questions you need to be asking yourself to be sure you are mastering B2B payments today. Learn more at www.BlueSnap.com.
[Note: This is a partial preview. To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
Sustainability has become an increasingly critical topic as the world recognizes the need to protect our planet and its resources for future generations. Sustainability means meeting our current needs without compromising the ability of future generations to meet theirs. It involves long-term planning and consideration of the consequences of our actions. The goal is to create strategies that ensure the long-term viability of People, Planet, and Profit.
Leading companies such as Nike, Toyota, and Siemens are prioritizing sustainable innovation in their business models, setting an example for others to follow. In this Sustainability training presentation, you will learn key concepts, principles, and practices of sustainability applicable across industries. This training aims to create awareness and educate employees, senior executives, consultants, and other key stakeholders, including investors, policymakers, and supply chain partners, on the importance and implementation of sustainability.
LEARNING OBJECTIVES
1. Develop a comprehensive understanding of the fundamental principles and concepts that form the foundation of sustainability within corporate environments.
2. Explore the sustainability implementation model, focusing on effective measures and reporting strategies to track and communicate sustainability efforts.
3. Identify and define best practices and critical success factors essential for achieving sustainability goals within organizations.
CONTENTS
1. Introduction and Key Concepts of Sustainability
2. Principles and Practices of Sustainability
3. Measures and Reporting in Sustainability
4. Sustainability Implementation & Best Practices
To download the complete presentation, visit: https://www.oeconsulting.com.sg/training-presentations
Falcon stands out as a top-tier P2P Invoice Discounting platform in India, bridging esteemed blue-chip companies and eager investors. Our goal is to transform the investment landscape in India by establishing a comprehensive destination for borrowers and investors with diverse profiles and needs, all while minimizing risk. What sets Falcon apart is the elimination of intermediaries such as commercial banks and depository institutions, allowing investors to enjoy higher yields.
What is the TDS Return Filing Due Date for FY 2024-25.pdfseoforlegalpillers
It is crucial for the taxpayers to understand about the TDS Return Filing Due Date, so that they can fulfill your TDS obligations efficiently. Taxpayers can avoid penalties by sticking to the deadlines and by accurate filing of TDS. Timely filing of TDS will make sure about the availability of tax credits. You can also seek the professional guidance of experts like Legal Pillers for timely filing of the TDS Return.
Putting the SPARK into Virtual Training.pptxCynthia Clay
This 60-minute webinar, sponsored by Adobe, was delivered for the Training Mag Network. It explored the five elements of SPARK: Storytelling, Purpose, Action, Relationships, and Kudos. Knowing how to tell a well-structured story is key to building long-term memory. Stating a clear purpose that doesn't take away from the discovery learning process is critical. Ensuring that people move from theory to practical application is imperative. Creating strong social learning is the key to commitment and engagement. Validating and affirming participants' comments is the way to create a positive learning environment.
Business Valuation Principles for EntrepreneursBen Wann
This insightful presentation is designed to equip entrepreneurs with the essential knowledge and tools needed to accurately value their businesses. Understanding business valuation is crucial for making informed decisions, whether you're seeking investment, planning to sell, or simply want to gauge your company's worth.
The key differences between the MDR and IVDR in the EUAllensmith572606
In the European Union (EU), two significant regulations have been introduced to enhance the safety and effectiveness of medical devices – the In Vitro Diagnostic Regulation (IVDR) and the Medical Device Regulation (MDR).
https://mavenprofserv.com/comparison-and-highlighting-of-the-key-differences-between-the-mdr-and-ivdr-in-the-eu/
RMD24 | Retail media: hoe zet je dit in als je geen AH of Unilever bent? Heid...BBPMedia1
Grote partijen zijn al een tijdje onderweg met retail media. Ondertussen worden in dit domein ook de kansen zichtbaar voor andere spelers in de markt. Maar met die kansen ontstaan ook vragen: Zelf retail media worden of erop adverteren? In welke fase van de funnel past het en hoe integreer je het in een mediaplan? Wat is nu precies het verschil met marketplaces en Programmatic ads? In dit half uur beslechten we de dilemma's en krijg je antwoorden op wanneer het voor jou tijd is om de volgende stap te zetten.
Improving profitability for small businessBen Wann
In this comprehensive presentation, we will explore strategies and practical tips for enhancing profitability in small businesses. Tailored to meet the unique challenges faced by small enterprises, this session covers various aspects that directly impact the bottom line. Attendees will learn how to optimize operational efficiency, manage expenses, and increase revenue through innovative marketing and customer engagement techniques.
The world of search engine optimization (SEO) is buzzing with discussions after Google confirmed that around 2,500 leaked internal documents related to its Search feature are indeed authentic. The revelation has sparked significant concerns within the SEO community. The leaked documents were initially reported by SEO experts Rand Fishkin and Mike King, igniting widespread analysis and discourse. For More Info:- https://news.arihantwebtech.com/search-disrupted-googles-leaked-documents-rock-the-seo-world/
Digital Transformation and IT Strategy Toolkit and TemplatesAurelien Domont, MBA
This Digital Transformation and IT Strategy Toolkit was created by ex-McKinsey, Deloitte and BCG Management Consultants, after more than 5,000 hours of work. It is considered the world's best & most comprehensive Digital Transformation and IT Strategy Toolkit. It includes all the Frameworks, Best Practices & Templates required to successfully undertake the Digital Transformation of your organization and define a robust IT Strategy.
Editable Toolkit to help you reuse our content: 700 Powerpoint slides | 35 Excel sheets | 84 minutes of Video training
This PowerPoint presentation is only a small preview of our Toolkits. For more details, visit www.domontconsulting.com
2. A Clinical Data Management System or CDMS is used in clinical research to manage the data generated
by the conduction of a clinical trial. The clinical trial data gathered at the investigator site in the Case Report
Form(CRF) is stored in the CDMS.
CRF is a paper or electronic questionnaire, used as a tool by the sponsor of the clinical trial to collect data on
each patient from each participating site.
Data management includes all aspects of data planning, handling, analysis, documentation and
storage, and takes place during all stages of a study. The objective is to create a reliable data base
containing high quality data. Data management is a too often neglected part of study design, and includes:
1. Planning the data needs of the study
2. Data collection
3. Data entry
4. Data validation and checking
5. Data manipulation
6. Data files backup
7. Data documentation
5/21/2019 2DR G.K.SHARMA PHARMD
3. The main element of data management are database files. Database files contain text, numerical,
images, and other data in machine readable form. Such files should be viewed as part of a database
management systems (DBMs) which allows for a broad range of data functions, including data
entry, checking, updating, documentation, and analysis.
Clinical Data Management is the process of handling data from clinical trials. The inherent goal of
any clinical data management system is to produce and maintain quality data.
Paper based
system
Electronic data
capturing system
1. Data is entered to CDMS from CRF
2. Time consuming process
3. Inexpensive
4. DCF send in paper form to the site
1. Data is directly upload to CDMS by investigator
2. Time saving process
3. expensive
4. Electronic alert send to the site if there is any
problem
5/21/2019 3DR G.K.SHARMA PHARMD
4. 1. Many software tools are available for data management, and these are called Clinical Data Management Systems.
2. In multicentric trials, a CDMS has become essential to handle the huge amount of data. Most of the CDMS used
in pharmaceutical companies are commercial, but a few open source tools are available as well.
3. Commonly used CDM tools: are ORACLE CLINICAL, CLINTRIAL, MACRO, RAVE, and eClinical Suite.
these software tools are more or less similar and there is no significant advantage of one system over the other.
These software tools are expensive.
4. Among the open source tools: the most prominent ones are OpenClinica, openCDMS, TrialDB, and PhOSCo.
These CDM software are available free of cost. These open source software can be downloaded from their
respective websites.
5. These CDM tools ensure the audit trail and help in the management of discrepancies.
6. According to the roles and responsibilities, multiple user IDs can be created with access limitation to data entry,
medical coding, database designing, or quality check. This ensures that each user can access only the respective
functionalities allotted to that user ID and cannot make any other change in the database. For responsibilities
where changes are permitted to be made in the data, the software will record the change made, the user ID that
made the change and the time and date of change, for audit purposes (audit trail).
7. During a regulatory audit, the auditors can verify the discrepancy management process; the changes made and can
confirm that no unauthorized or false changes were made.
5/21/2019DR G.K.SHARMA PHARMD 4
5. 1. CDM has guidelines and standards that must be followed.
2. Electronically captured data: for the evaluation of medicines, there is a need to follow good practices in CDM and maintain standards in
electronic data capture. These electronic records have to comply with a Code of Federal Regulations (CFR), 21 CFR Part 11. This regulation
is applicable to records in electronic format that are created, modified, maintained, archived, retrieved, or transmitted. This demands the use
of validated systems to ensure accuracy, reliability, and consistency of data with the use of secure, computer-generated, time-stamped audit
trails to independently record the date and time of operator entries and actions that create, modify, or delete electronic records.
3. Society for Clinical Data Management (SCDM) publishes the Good Clinical Data Management Practices (GCDMP) guidelines, a
document providing the standards of good practice within CDM.
4. GCDMP was initially published in September 2000. The July 2009 version is the currently followed GCDMP document.
5. Clinical Data Interchange Standards Consortium (CDISC), a multidisciplinary non-profit organization, has developed standards to
support acquisition, exchange, submission, and archival of clinical research data and metadata.
6. Metadata: is the data of the data entered. This includes data about the individual who made the entry or a change in the clinical data, the
date and time of entry/change and details of the changes that have been made.
7. Study Data Tabulation Model Implementation Guide (SDTMIG) for Human Clinical Trials and the Clinical Data Acquisition Standards
Harmonization (CDASH) standards, available free of cost from the CDISC website (www.cdisc.org).
8. The SDTMIG standard describes the details of model and standard terminologies for the data and serves as a guide to the organization.
CDASH.
5/21/2019 5DR G.K.SHARMA PHARMD
6. The CDM process, like a clinical trial, begins with the end in mind. This means that the whole
process is designed keeping the deliverable in view. As a clinical trial is designed to answer the
research question, the CDM process is designed to deliver an error-free, valid, and statistically
sound database. To meet this objective, the CDM process starts early, even before the finalization of
the study protocol.
5/21/2019 6DR G.K.SHARMA PHARMD
7. The protocol is reviewed from a database designing perspective, for clarity and consistency. During
this review, the CDM personnel will identify the data items to be collected and the frequency of
collection with respect to the visit schedule.
A Case Report Form (CRF) is designed by the CDM team, as this is the first step in translating the
protocol-specific activities into data being generated. The data fields should be clearly defined and
be consistent throughout.
The type of data to be entered should be evident from the CRF. Similarly, the units in which
measurements have to be made should also be mentioned next to the data field. The CRF should be
concise, self-explanatory, and user-friendly (unless you are the one entering data into the CRF).
Along with the CRF, the filling instructions (called CRF Completion Guidelines) should also be
provided to study investigators for error-free data acquisition.
5/21/2019 7DR G.K.SHARMA PHARMD
8. Databases are the clinical software applications, which are built to facilitate the CDM tasks to carry
out multiple studies. Generally, these tools have built-in compliance with regulatory requirements
and are easy to use.
“System validation” is conducted to ensure data security, during which system specifications, user
requirements, and regulatory compliance are evaluated before implementation. Study details like
objectives, intervals, visits, investigators, sites, and patients are defined in the database and CRF
layouts are designed for data entry. These entry screens are tested with dummy data before moving
them to the real data capture.
Data from clinical trial will be collected and stored in the CDMS. A database is simply a structure
set of data.
5/21/2019 8DR G.K.SHARMA PHARMD
9. Data collection is done using the CRF that may exist in the form of a paper or an electronic
version. The traditional method is to employ paper CRFs to collect the data responses,
which are translated to the database by means of data entry done in-house. These paper
CRFs are filled up by the investigator according to the completion guidelines. In the e-CRF-
based CDM, the investigator or a designee will be logging into the CDM system and
entering the data directly at the site. In e-CRF method, chances of errors are less, and the
resolution of discrepancies happens faster. Since pharmaceutical companies try to reduce the
time taken for drug development processes by enhancing the speed of processes involved,
many pharmaceutical companies are opting for e-CRF options (also called remote data
entry).
5/21/2019 9DR G.K.SHARMA PHARMD
10. The entries made in the CRF will be monitored by the Clinical Research Associate (CRA)
for completeness and filled up CRFs are retrieved and handed over to the CDM team. The
CDM team will track the retrieved CRFs and maintain their record. CRFs are tracked for
missing pages and illegible data manually to assure that the data are not lost. In case of
missing or illegible data, a clarification is obtained from the investigator and the issue is
resolved.
Paper based study. electronic data capture if it is e-CRF
5/21/2019 10DR G.K.SHARMA PHARMD
11. Data entry takes place according to the guidelines prepared along with the DMP. This is applicable
only in the case of paper CRF retrieved from the sites.
Data entry is a process of entering or transferring data from case record form to the clinical data
management system. data entry
Single data entry
Double data entry: performed wherein the data is entered by two operators separately. (entry made
by the second person) helps in verification and reconciliation by identifying the transcription errors
and discrepancies caused by illegible data.
Double data entry helps in getting a cleaner database compared to a single data entry. Earlier studies
have shown that double data entry ensures better consistency with paper CRF as denoted by a lesser
error rate.
5/21/2019 11DR G.K.SHARMA PHARMD
12. 1. Data validation is the process of testing the validity of data in accordance with the protocol
specifications.
2. Edit check programs are written to identify the discrepancies in the entered data, which are embedded in
the database, to ensure data validity. These edit check programs are initially tested with dummy data
containing discrepancies.
3. Discrepancy is defined as a data point that fails to pass a validation check.
4. Discrepancy may be due to inconsistent data, missing data, range checks, and deviations from the
protocol.
5. In e-CRF based studies, data validation process will be run frequently for identifying discrepancies.
These discrepancies will be resolved by investigators after logging into the system. Ongoing quality
control of data processing is undertaken at regular intervals during the course of CDM. For example, if
the inclusion criteria specify that the age of the patient should be between 18 and 65 years (both
inclusive), an edit program will be written for two conditions viz. age <18 and >65. If for any patient, the
condition becomes TRUE, a discrepancy will be generated.
6. These discrepancies will be highlighted in the system and Data Clarification Forms (DCFs) can be
generated. DCFs are documents containing queries pertaining to the discrepancies identified.
5/21/2019 12DR G.K.SHARMA PHARMD
13. This is also called query resolution. Discrepancy management includes reviewing discrepancies,
investigating the reason, and resolving them with documentary proof.
Discrepancy management helps in cleaning the data and gathers enough evidence for the deviations
observed in data.
Almost all CDMS have a discrepancy database where all discrepancies will be recorded and stored
with audit trail.
For discrepancies that require clarifications from the investigator, DCFs will be sent to the site. The
CDM tools help in the creation and printing of DCFs. Investigators will write the resolution or
explain the circumstances that led to the discrepancy in data. When a resolution is provided by the
investigator, the same will be updated in the database. In case of e-CRFs, the investigator can access
the discrepancies flagged to him and will be able to provide the resolutions online.
5/21/2019 13DR G.K.SHARMA PHARMD
14. 1. Medical coding helps in identifying and properly classifying the medical terminologies associated with
the clinical trial.
2. For classification of events, medical dictionaries available online are used.
3. Technically, this activity needs the knowledge of medical terminology, understanding of disease entities,
drugs used, and a basic knowledge of the pathological processes involved.
4. Adverse events occurring during the study, prior to and concomitantly administered medications and
pre-or co-existing illnesses are coded using the available medical dictionaries.
5. Commonly, Medical Dictionary for Regulatory Activities (MedDRA) is used for the coding of adverse
events as well as other illnesses and World Health Organization–Drug Dictionary Enhanced (WHO-
DDE) is used for coding the medications.
6. These dictionaries contain the respective classifications of adverse events and drugs in proper classes.
7. Other dictionaries are also available for use in data management (eg, WHO-ART is a dictionary that
deals with adverse reactions terminology).
8. The right coding and classification of adverse events and medication is crucial as an incorrect coding
may lead to masking of safety issues or highlight the wrong safety concerns related to the drug.
5/21/2019 14DR G.K.SHARMA PHARMD
15. 1. After a proper quality check and assurance, the final data validation is run.
2. All data management activities should have been completed prior to database lock.
3. A pre-lock checklist: checklist is used and completion of all activities is confirmed. This is done as
the database cannot be changed in any manner after locking. Once the approval for locking is
obtained from all stakeholders, the database is locked and clean data is extracted for statistical
analysis.
4. Generally, no modification in the database is possible. But in case of a critical issue or for other
important operational reasons, privileged users can modify the data even after the database is
locked.
5. Requires proper documentation and an audit trail has to be maintained with sufficient justification
for updating the locked database.
5/21/2019 15DR G.K.SHARMA PHARMD
16. The minimum educational requirement for a team member in CDM should be graduation in life
science and knowledge of computer applications.
medical coders should be medical graduates. however, in the industry, paramedical graduates are
also recruited as medical coders. Some key roles are essential to all CDM teams. The list of roles
given below can be considered as minimum requirements for a CDM team:
1. Data Manager
2. Database Programmer/Designer
3. Medical Coder
4. Clinical Data Coordinator
5. Quality Control Associate
6. Data Entry Associate
5/21/2019 16DR G.K.SHARMA PHARMD
17. Data Manager
1) Supervising the entire CDM process.
2) The data manager prepares the DMP, approves the CDM procedures and all internal documents
related to CDM activities.
3) Controlling and allocating the database access to team members
Database Programmer/Designer:
Performs the CRF annotation, creates the study database, and programs the edit checks for data
validation.
He/she is also responsible for designing of data entry screens in the database and validating the edit
checks with dummy data.
Medical Coder:
coding for adverse events, medical history, co-illnesses, and concomitant medication administered
during the study.
5/21/2019 17DR G.K.SHARMA PHARMD
18. Clinical Data Coordinator:
designs the CRF, prepares the CRF filling instructions, and is responsible for developing the DVP
and discrepancy management.
All other CDM-related documents, checklists, and guideline documents are prepared by the clinical
data coordinator.
Quality Control Associate:
Checks the accuracy of data entry and conducts data audits.
Quality control associate verifies the documentation pertaining to the procedures being followed.
Data Entry Associate: will be tracking the receipt of CRF pages and performs the data entry into
the database.
5/21/2019 18DR G.K.SHARMA PHARMD