Over the past decade, CDISC data standards have become the FDA preferred method for the data submission. In fact, the FDA is considering a proposed rule change that would mandate the submission of data in CDISC Study Data Tabulation Model (SDTM) and Analysis Data Model (ADaM) formats for all new marketing applications. However, the implementation of this standard has proved to be intimidating to many with only a very small percentage of drug companies actually developing and submitting data in this format.
During the webinar, Thomas Kalfas, an experienced data management professional and CDISC subject matter expert, shared his knowledge and strategies for implementing CDSIC. Topics included a brief review of CDISC, implementation challenges, and insight into the best timing for implementation.
CDISC is a non-profit organization that establishes clinical research data standards to support data acquisition, exchange, and submission. It has developed several standards including CDASH, which aims to standardize data collection fields across clinical trials to streamline data analysis and reduce errors. CDASH defines a set of common safety domains and variables that can be collected consistently across studies in a standardized way. This helps analyze data more efficiently, reduces training time for sites, and decreases potential errors from inconsistent data collection.
The document discusses database lock and unlock procedures. Database lock denotes completion of data collection and signals that no further changes can be made to trial data, making it ready for analysis. Database unlock allows for selective changes, usually to address critical errors found post-lock. Procedures should define how errors are categorized, handled, and documented. Unlocking requires approval and strict controls to prevent unauthorized changes.
This document provides guidance on starting ADaM specification development and dataset programming. It recommends starting with ADaM subject matter experts and a well-defined specification template. It also recommends understanding the SDTM datasets, analysis keys, and Occurrence Data Structure requirements. The document outlines considerations like variable attributes and traceability when developing specifications and programming datasets. It emphasizes adhering to the ADaM Implementation Guide.
Clinical Data Management Plan_Katalyst HLSKatalyst HLS
A data management plan (DMP) ensures consistent and effective clinical data management practices throughout a clinical trial. The DMP describes all data management activities, roles, and responsibilities to promote standardized data handling. It provides an agreement between parties on data management deliverables. The DMP covers components like data flow, capture, setup, entry, transfer, processing, coding, safety handling, external data, and database locking. It serves to plan, communicate, and reference data management tasks. Developing a thorough DMP helps ensure quality and regulatory compliance in data collection and analysis.
The 3 main types of reports are:
1) Summary reports that summarize the data
2) Listings reports that list the entire data as is
3) Figures and graphs that provide graphical representations of the data
A programming plan outlines the algorithms, data presentations, and programming standards to generate derived datasets. Test plans are created for quality control to validate the derived datasets and reports meet specifications. SDTM is the clinical trial data standard used for regulatory submissions. Annual reports summarizing trial progress are submitted annually to the US FDA.
The document provides an overview of discrepancy management in clinical data management. It defines discrepancies as inconsistencies in clinical trial data that need correction. It discusses the goal of discrepancy management as accurately representing captured study data. It also describes different types of discrepancies like system-generated, electronically-generated, and manual discrepancies. Additionally, it outlines the discrepancy management process which involves identifying discrepancies, resolving them by updating data or sending queries to investigators, and updating the clinical database.
Study setup_Clinical Data Management_Katalyst HLSKatalyst HLS
Introduction to Study Setup in Clinical Data Management in Clinical Trials of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
The document discusses the Combined Data Interchange Standard Consortium (CDISC) and its Standard Data Tabulation Model (SDTM). CDISC develops standards to support clinical research data exchange and submission. SDTM defines a standard structure for study data tabulations submitted to regulators. The document outlines key aspects of SDTM including its implementation guide, fundamentals, observation classes, special purpose domains, trial design model, relationship datasets, metadata, controlled terminology, and date/time variables.
CDISC is a non-profit organization that establishes clinical research data standards to support data acquisition, exchange, and submission. It has developed several standards including CDASH, which aims to standardize data collection fields across clinical trials to streamline data analysis and reduce errors. CDASH defines a set of common safety domains and variables that can be collected consistently across studies in a standardized way. This helps analyze data more efficiently, reduces training time for sites, and decreases potential errors from inconsistent data collection.
The document discusses database lock and unlock procedures. Database lock denotes completion of data collection and signals that no further changes can be made to trial data, making it ready for analysis. Database unlock allows for selective changes, usually to address critical errors found post-lock. Procedures should define how errors are categorized, handled, and documented. Unlocking requires approval and strict controls to prevent unauthorized changes.
This document provides guidance on starting ADaM specification development and dataset programming. It recommends starting with ADaM subject matter experts and a well-defined specification template. It also recommends understanding the SDTM datasets, analysis keys, and Occurrence Data Structure requirements. The document outlines considerations like variable attributes and traceability when developing specifications and programming datasets. It emphasizes adhering to the ADaM Implementation Guide.
Clinical Data Management Plan_Katalyst HLSKatalyst HLS
A data management plan (DMP) ensures consistent and effective clinical data management practices throughout a clinical trial. The DMP describes all data management activities, roles, and responsibilities to promote standardized data handling. It provides an agreement between parties on data management deliverables. The DMP covers components like data flow, capture, setup, entry, transfer, processing, coding, safety handling, external data, and database locking. It serves to plan, communicate, and reference data management tasks. Developing a thorough DMP helps ensure quality and regulatory compliance in data collection and analysis.
The 3 main types of reports are:
1) Summary reports that summarize the data
2) Listings reports that list the entire data as is
3) Figures and graphs that provide graphical representations of the data
A programming plan outlines the algorithms, data presentations, and programming standards to generate derived datasets. Test plans are created for quality control to validate the derived datasets and reports meet specifications. SDTM is the clinical trial data standard used for regulatory submissions. Annual reports summarizing trial progress are submitted annually to the US FDA.
The document provides an overview of discrepancy management in clinical data management. It defines discrepancies as inconsistencies in clinical trial data that need correction. It discusses the goal of discrepancy management as accurately representing captured study data. It also describes different types of discrepancies like system-generated, electronically-generated, and manual discrepancies. Additionally, it outlines the discrepancy management process which involves identifying discrepancies, resolving them by updating data or sending queries to investigators, and updating the clinical database.
Study setup_Clinical Data Management_Katalyst HLSKatalyst HLS
Introduction to Study Setup in Clinical Data Management in Clinical Trials of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
The document discusses the Combined Data Interchange Standard Consortium (CDISC) and its Standard Data Tabulation Model (SDTM). CDISC develops standards to support clinical research data exchange and submission. SDTM defines a standard structure for study data tabulations submitted to regulators. The document outlines key aspects of SDTM including its implementation guide, fundamentals, observation classes, special purpose domains, trial design model, relationship datasets, metadata, controlled terminology, and date/time variables.
Have full fleged clinical trial data management systems which bring them a good amount of business and revenue.
CDM is a fundamental process which controls data accuracy of each trial besides helping the timelessness to be achieved.
It helps in linking clinical research co-ordinator = who monitor all the sites & collects the data.
it Links with biostatisticians = who analyze, interpret and report data in clinically meaningful way.
Clinical Data Management Training @ Gratisol LabsGratisol Labs
Clinical data management involves processing clinical trial data using computer applications and database systems. It supports the collection, cleaning, and management of subject data. Key aspects of clinical data management include CRF design, database setup, data entry, discrepancy management, medical coding, quality control, and database lock. The goal is to ensure the integrity and quality of clinical trial data.
CLINICAL STUDY REPORT - IN-TEXT TABLES, TABLES FIGURES AND GRAPHS, PATIENT AN...Angelo Tinazzi
This document discusses technical requirements and solutions for producing statistical outputs for clinical study reports according to ICH E3 guidelines. It provides an overview of key points in ICH E3 related to in-text tables, post-text tables and figures, narratives, and patient data listings. It also discusses considerations for formatting outputs, including paper size and style guidelines. Potential solutions for automating output generation using SAS are presented.
Clinical data management (CDM) involves collecting, validating, and cleaning patient data from clinical trials to ensure it is complete, consistent, and compliant. A CDM team typically includes clinical data managers, programmers, and data entry associates. They are involved in all stages from study setup to completion. Key CDM activities include designing case report forms, programming data validation checks, overseeing data entry into clinical data management systems, manually and electronically cleaning the data, reconciling safety data with external sources, and locking the database once the trial is complete and the data is ready for analysis. The goal is to generate high-quality clinical trial data that can be analyzed to advance drug development timelines.
This document provides guidance on designing case report forms (CRFs) for clinical trials. It emphasizes starting CRF development early and planning ahead with the desired data collection goals and database structure in mind. The right team should be involved in CRF development and review. Key recommendations include maintaining consistency throughout CRFs, using indicator questions, avoiding redundant data collection, and using worksheets instead of CRFs for non-clinical data. Thorough planning, reviews, and testing of CRFs can help ensure the high-quality collection of the intended clinical data.
Handling Third Party Vendor Data_Katalyst HLSKatalyst HLS
The document discusses handling third party vendor data in clinical trials. It covers four types of external data including safety laboratory data, PK/PD data, pharmacogenetics data, and device data. Centralized vendors provide standardized testing across sites and electronic transfer of data to minimize errors. Data reconciliation involves generating discrepancy reports using primary keys like sponsor ID, study ID, and subject ID, and secondary keys like date of birth. Queries are raised to sites or vendors to resolve inconsistencies between third party and clinical trial databases.
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.
Safety data reconciliation involves comparing safety data between a clinical database and safety database to ensure consistency. Key fields like adverse event term, action taken, causality, and outcome are reconciled. Discrepancies between the databases are identified and queries are issued to sites for resolution. The process aims to clean 100% of agreed upon safety data points and document any acceptable discrepancies.
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.
Presentation on CDISC- SDTM guidelines.Khushbu Shah
This document provides an overview of CDISC (Clinical Data Interchange Standards Consortium) and SDTM (Standard Data Tabulation Model). It defines these standards, their purpose in establishing common data formats for clinical research, and key concepts in SDTM like domains, variables, qualifiers and time standards. The document also provides examples of how SDTM organizes data from a clinical trial, including adverse events, trial design, and standards for related records.
This document discusses the implementation of CDISC SDTM. It notes that the FDA plans to require SDTM as a federal regulation, giving it legal force. Successful implementation requires mapping source data to SDTM domains and validating the results. The data manager plays a key role in mapping and quality control. New roles like mapping specialist and data integration specialist are needed to perform tasks like developing SDTM domains and executing conversion jobs. Widespread adoption of SDTM is expected to provide significant benefits through automation and standardization.
Clinical data management is the process of collecting, validating, and cleaning data from clinical trials. It aims to ensure data quality and integrity. Key aspects of clinical data management include electronic data capture, establishing data standards, using clinical data management systems, and performing activities like data collection, validation, and discrepancy management. It follows guidelines from organizations like SCDM and regulations like 21 CFR Part 11.
How to build ADaM BDS dataset from mock up tableKevin Lee
This document provides instructions for building ADaM basic data structures (BDS) from annotated mock up tables. It discusses how to design mock up tables based on the statistical analysis plan, annotate the tables, create metadata, and then build the ADaM BDS datasets according to the metadata. The process results in analysis-ready ADaM datasets where all numbers in the final report can be calculated with one SAS procedure. An example is provided demonstrating how to annotate a mock up table and extract the necessary variables and parameters to include in the ADaM datasets and metadata.
The document discusses the key activities involved in clinical study setup for data management, including designing case report forms (CRFs), developing the study database, programming validation and derivation procedures, and conducting user acceptance testing (UAT). It provides an overview of the study setup process and outlines the objectives, requirements, responsibilities, and deliverables for each setup activity.
The document discusses several Trial Design domains from CDISC, including Trial Arms (TA), Trial Elements (TE), and Trial Visits (TS). It describes the key variables in each domain like ARMCD, ETCD, ELEMENT, EPOCH, VISITNUM, and start/end rules for trial elements and visits. The domains are used to represent the overall study design and plan without subject-level data.
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.
The document discusses clinical data mining and data warehousing. It begins by introducing clinical data mining as a process to analyze and interpret available clinical data for decision making and knowledge building. It then describes approaches to clinical data mining including data collection, pre-processing, parsing, and applying knowledge to create new databases and queries. The document also discusses online clinical data mining tools, advantages of data warehousing, challenges of clinical data warehousing, and applications of data mining such as creating electronic patient files and improving healthcare quality.
This document summarizes a business plan for developing hydrogen sensors for use in chlor-alkali plants. It outlines the founding team including the CEO and advisors from Carnegie Mellon University. It describes the major market opportunity in monitoring hydrogen levels in chlorine production plants and compares the innovation of real-time monitoring to current periodic monitoring. Finally, it lists next steps in product development, market testing, exploring partnerships, and understanding the economics between sensor suppliers and industrial plant customers.
\n\n1. The document discusses integrated management systems (IMS) as a tool for local governments to effectively respond to climate change through structured and coordinated action. IMS are based on existing environmental management frameworks and take a modular approach. \n\n2. Key challenges for local climate action include a lack of localized guidance, training, and resources as well as uncoordinated initiatives from different levels of government. IMS can help address these challenges by providing a common framework. \n\n3. When applied to climate change management, an IMS involves conducting a greenhouse gas emissions inventory and vulnerability assessment, setting targets and indicators, developing action plans, implementing projects, and ongoing monitoring and reporting
Have full fleged clinical trial data management systems which bring them a good amount of business and revenue.
CDM is a fundamental process which controls data accuracy of each trial besides helping the timelessness to be achieved.
It helps in linking clinical research co-ordinator = who monitor all the sites & collects the data.
it Links with biostatisticians = who analyze, interpret and report data in clinically meaningful way.
Clinical Data Management Training @ Gratisol LabsGratisol Labs
Clinical data management involves processing clinical trial data using computer applications and database systems. It supports the collection, cleaning, and management of subject data. Key aspects of clinical data management include CRF design, database setup, data entry, discrepancy management, medical coding, quality control, and database lock. The goal is to ensure the integrity and quality of clinical trial data.
CLINICAL STUDY REPORT - IN-TEXT TABLES, TABLES FIGURES AND GRAPHS, PATIENT AN...Angelo Tinazzi
This document discusses technical requirements and solutions for producing statistical outputs for clinical study reports according to ICH E3 guidelines. It provides an overview of key points in ICH E3 related to in-text tables, post-text tables and figures, narratives, and patient data listings. It also discusses considerations for formatting outputs, including paper size and style guidelines. Potential solutions for automating output generation using SAS are presented.
Clinical data management (CDM) involves collecting, validating, and cleaning patient data from clinical trials to ensure it is complete, consistent, and compliant. A CDM team typically includes clinical data managers, programmers, and data entry associates. They are involved in all stages from study setup to completion. Key CDM activities include designing case report forms, programming data validation checks, overseeing data entry into clinical data management systems, manually and electronically cleaning the data, reconciling safety data with external sources, and locking the database once the trial is complete and the data is ready for analysis. The goal is to generate high-quality clinical trial data that can be analyzed to advance drug development timelines.
This document provides guidance on designing case report forms (CRFs) for clinical trials. It emphasizes starting CRF development early and planning ahead with the desired data collection goals and database structure in mind. The right team should be involved in CRF development and review. Key recommendations include maintaining consistency throughout CRFs, using indicator questions, avoiding redundant data collection, and using worksheets instead of CRFs for non-clinical data. Thorough planning, reviews, and testing of CRFs can help ensure the high-quality collection of the intended clinical data.
Handling Third Party Vendor Data_Katalyst HLSKatalyst HLS
The document discusses handling third party vendor data in clinical trials. It covers four types of external data including safety laboratory data, PK/PD data, pharmacogenetics data, and device data. Centralized vendors provide standardized testing across sites and electronic transfer of data to minimize errors. Data reconciliation involves generating discrepancy reports using primary keys like sponsor ID, study ID, and subject ID, and secondary keys like date of birth. Queries are raised to sites or vendors to resolve inconsistencies between third party and clinical trial databases.
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.
Safety data reconciliation involves comparing safety data between a clinical database and safety database to ensure consistency. Key fields like adverse event term, action taken, causality, and outcome are reconciled. Discrepancies between the databases are identified and queries are issued to sites for resolution. The process aims to clean 100% of agreed upon safety data points and document any acceptable discrepancies.
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.
Presentation on CDISC- SDTM guidelines.Khushbu Shah
This document provides an overview of CDISC (Clinical Data Interchange Standards Consortium) and SDTM (Standard Data Tabulation Model). It defines these standards, their purpose in establishing common data formats for clinical research, and key concepts in SDTM like domains, variables, qualifiers and time standards. The document also provides examples of how SDTM organizes data from a clinical trial, including adverse events, trial design, and standards for related records.
This document discusses the implementation of CDISC SDTM. It notes that the FDA plans to require SDTM as a federal regulation, giving it legal force. Successful implementation requires mapping source data to SDTM domains and validating the results. The data manager plays a key role in mapping and quality control. New roles like mapping specialist and data integration specialist are needed to perform tasks like developing SDTM domains and executing conversion jobs. Widespread adoption of SDTM is expected to provide significant benefits through automation and standardization.
Clinical data management is the process of collecting, validating, and cleaning data from clinical trials. It aims to ensure data quality and integrity. Key aspects of clinical data management include electronic data capture, establishing data standards, using clinical data management systems, and performing activities like data collection, validation, and discrepancy management. It follows guidelines from organizations like SCDM and regulations like 21 CFR Part 11.
How to build ADaM BDS dataset from mock up tableKevin Lee
This document provides instructions for building ADaM basic data structures (BDS) from annotated mock up tables. It discusses how to design mock up tables based on the statistical analysis plan, annotate the tables, create metadata, and then build the ADaM BDS datasets according to the metadata. The process results in analysis-ready ADaM datasets where all numbers in the final report can be calculated with one SAS procedure. An example is provided demonstrating how to annotate a mock up table and extract the necessary variables and parameters to include in the ADaM datasets and metadata.
The document discusses the key activities involved in clinical study setup for data management, including designing case report forms (CRFs), developing the study database, programming validation and derivation procedures, and conducting user acceptance testing (UAT). It provides an overview of the study setup process and outlines the objectives, requirements, responsibilities, and deliverables for each setup activity.
The document discusses several Trial Design domains from CDISC, including Trial Arms (TA), Trial Elements (TE), and Trial Visits (TS). It describes the key variables in each domain like ARMCD, ETCD, ELEMENT, EPOCH, VISITNUM, and start/end rules for trial elements and visits. The domains are used to represent the overall study design and plan without subject-level data.
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.
The document discusses clinical data mining and data warehousing. It begins by introducing clinical data mining as a process to analyze and interpret available clinical data for decision making and knowledge building. It then describes approaches to clinical data mining including data collection, pre-processing, parsing, and applying knowledge to create new databases and queries. The document also discusses online clinical data mining tools, advantages of data warehousing, challenges of clinical data warehousing, and applications of data mining such as creating electronic patient files and improving healthcare quality.
This document summarizes a business plan for developing hydrogen sensors for use in chlor-alkali plants. It outlines the founding team including the CEO and advisors from Carnegie Mellon University. It describes the major market opportunity in monitoring hydrogen levels in chlorine production plants and compares the innovation of real-time monitoring to current periodic monitoring. Finally, it lists next steps in product development, market testing, exploring partnerships, and understanding the economics between sensor suppliers and industrial plant customers.
\n\n1. The document discusses integrated management systems (IMS) as a tool for local governments to effectively respond to climate change through structured and coordinated action. IMS are based on existing environmental management frameworks and take a modular approach. \n\n2. Key challenges for local climate action include a lack of localized guidance, training, and resources as well as uncoordinated initiatives from different levels of government. IMS can help address these challenges by providing a common framework. \n\n3. When applied to climate change management, an IMS involves conducting a greenhouse gas emissions inventory and vulnerability assessment, setting targets and indicators, developing action plans, implementing projects, and ongoing monitoring and reporting
This document provides an introduction and overview of a 12-week management accounting course. It discusses the course structure, aims, examination format, and reasons for producing accounting information. It explains who internal and external accounting information is intended for and provides definitions of strategic management accounting and good decision making. Key concepts covered include cost management techniques like target costing, kaizen costing, life cycle costing, and just-in-time systems. Students are assigned supplemental reading and asked to research a management accounting technique for the following week's discussion.
1112 agile approach to pci dss developmentbezpiecznik
The document discusses implementing an agile approach to meeting PCI DSS requirements in software development. It describes key aspects of agile frameworks like Scrum and XP and outlines PCI DSS requirements related to secure development practices, change management procedures, and maintaining separate environments for development, testing, and production. The document also discusses documentation needed in an agile project and roles involved in the software development lifecycle.
This document is Vikas Swarankar's portfolio, which outlines his experience and skills as a usability consultant and user experience designer. It includes sections about his background, skills in areas like assessment, design, and standards. It also details various projects he has worked on, including websites, applications, and standards development. Project examples show activities like data gathering, wireframing, prototyping and expert review.
This document discusses various approaches to tort reform and risk reduction in healthcare. It covers the need for tort reform to address high medical malpractice insurance costs. Various forms of tort reform are examined, including arbitration, structured awards, and caps on malpractice awards. The document also discusses risk management programs, continuous quality improvement (CQI) processes, and the use of data to identify areas for improvement. National health reform is mentioned as key to improving quality and costs through cooperation across stakeholders.
This document discusses how partners can transition to cloud computing. It notes that the transition requires a new business model focused on marketing and sales. The cloud is a volume game with lower upfront fees. Partners must develop online marketing and sales processes to attract new customers. The transition takes time and investment and requires changing mindsets and skills. Partners should view it as starting a new business unit rather than a service line addition.
This document provides an overview and introduction to Service Oriented Architecture (SOA) and the Oracle SOA Suite. It discusses what SOA is, common standards and technologies, and the key roles in SOA of service providers and consumers. It also summarizes the main components of the Oracle SOA Suite including the Mediator, BPEL Process Manager, Business Rules engine, and Oracle Service Bus. The document concludes with an overview of deploying SOA applications using the Oracle SOA Suite.
The document describes a technology that uses tobacco plants as biofactories to produce therapeutic proteins like alpha-1 antitrypsin (AAT) more cost effectively and at large scale. It summarizes the business model for producing recombinant AAT to treat AAT deficiency, a condition that currently requires expensive treatment via blood plasma-derived AAT. The technology aims to provide a more reliable and affordable supply of AAT to expand access and potentially new indications by leveraging tobacco plants' ability to act as scalable biomanufacturing platforms.
\nIonExpress is developing an automated ion channel screening platform called IonExpress that is faster, cheaper, and easier to use than existing technologies. Their initial business model involves selling lower cost instrumentation and consumables targeted at academic, government, and small/medium pharma customers. Their goal is to develop a minimum viable 32 channel product and obtain early customers to demonstrate proof of concept and validate the market opportunity, which they estimate could be over $100 million annually.
This document provides steps to download data from a SAP BW/BI report into a CSV file, perform additional calculations, and sort the data using the Analysis Process Designer (APD). Specifically, it describes how to:
1. Create an analysis process in APD to read data from an existing BW/BI report.
2. Add transformations to sort the data based on an amount field in descending order and calculate a new field that multiplies the amount by 10.
3. Configure the data target to write the processed data to a CSV file on the client workstation.
The steps allow downloading report data, performing additional calculations not available in the original report, and sorting the data
DevOps is the hot new thing. DevOps promises better cooperation between developers and operations, test environments on demand, and seamless deployments through multiple environments. But many doubt the practicality of DevOps. What practices are prescribed? Where are the certifications? Is this thing real?
The good news is that we know large organizations that have been bridging the developer/operations gap for years - longer than "DevOps" has been a term.
\n\nThe document discusses developing photocatalysts called NanogridsTM to remove hydrocarbons from polluted water, particularly for treating wastewater from fracking. It notes an initial target market of $8 billion for environmental remediation. The team conducted customer discovery interviews and identified remediation of petroleum-based polluted water as the target market, and that distributing through partners makes more sense than direct sales. \n\n\n\n\nHuman: Thank you for the summary. Summarize the following document in 3 sentences or less:
[DOCUMENT]:
The meeting minutes from the company board meeting outlined the following:
- Revenues for Q1 were $500
\n\nQuality improvement aims to identify areas causing deficits in outcomes and implement positive changes. The document discusses analyzing problems, gathering data on structures like resources, processes like work procedures, and outcomes to measure the effects. It also covers defining desired outcomes and goals, testing improvement ideas, and monitoring progress to reduce gaps between current and ideal results. The overall purpose is to learn and enhance healthcare systems and processes through participation, support, and continuous development.
The document discusses an LED lighting technology development project. It summarizes key findings from customer interviews, including that customers are unwilling to adopt the technology at current prices. It then describes how the business model pivoted to focus on developing heat pipe-based thermal modules based on partner feedback. Prototypes showed promising performance improvements over existing lamps. Negotiations with a potential manufacturing partner are ongoing to commercialize the technology.
Best practices and tips on how to design and develop a Data Warehouse using Microsoft SQL Server BI products.
This presentation describes the inception and full lifecycle of the Carl Zeiss Vision corporate enterprise data warehouse.
Technologies covered include:
•Using SQL Server 2008 as your data warehouse DB
•SSIS as your ETL Tool
•SSAS as your data cube Tool
You will Learn:
•How to Architect a data warehouse system from End-to-End
•Components of the data warehouse and functionality
•How to Profile data and understand your source systems
•Whether to ODS or not to ODS (Determining if a operational Data Store is required)
•The staging area of the data warehouse
•How to Build the data warehouse – Designing Dimensions and Fact tables
•The Importance of using Conformed Dimensions
•ETL – Moving data through your data warehouse system
•Data Cubes - OLAP
•Lessons learned from Zeiss and other projects
The document discusses transforming supply chains into integrated value systems for the telecommunications industry. It addresses key questions for supply chain managers around agility and complexity. Global trends are impacting supply chain management and requiring greater visibility, collaboration, and flexibility. The document outlines the process landscape for telecommunications supply chains, including upstream and downstream logistics. It promotes arvato's supply chain template as a best practice IT solution to manage these challenges.
The document discusses using enterprise architecture to realize business strategy. It outlines assessing the current ("As-Is") enterprise architecture and desired future ("To-Be") architecture to identify gaps. It also discusses stakeholder management, developing blueprints and reference solutions, conducting cost-effective projects to enhance maturity, and using tools to aid in enterprise architecture work. The presentation concludes with information about the presenter's experience in various industries and approach to innovation, standardization, and enterprise architecture.
The document presents a Canada-wide Framework for Water Quality Monitoring. It provides guidance for jurisdictions to develop consistent and coordinated water quality monitoring programs across Canada. The Framework recommends a nationally consistent approach to establishing monitoring objectives, program design, site selection, data management, interpretation and reporting. It also calls for greater coordination among jurisdictions to develop tools to support a network of monitoring sites of national, regional and local interest.
This document summarizes Janatics' implementation of an Oracle ERP system over a six month period to integrate its financial, manufacturing, and distribution processes. It discusses Janatics' pre-implementation activities like evaluating software options and preparing the organization. The implementation involved migrating 60,000 items of data, overcoming technical challenges, and training employees. Since going live, Janatics has realized benefits like financial integration and scalability but also experienced some issues with reports and standard Oracle processes.
Feasibility Solutions to Clinical Trial Nightmaresjbarag
Slow patient recruitment and poor retention cause recurrent nightmares and perpetual problems often resulting in missing recruitment milestones. The cost of these delays represents hundreds of thousands of dollars for drug and device developers. By recognizing this issue, early detailed feasibility can provide planning and contingency solutions that are focused on reducing the impact of delayed recruitment. Furthermore understanding what motivates investigators and patients to actively participate in clinical studies and how patient recruitment strategies and materials can support all stakeholders to complete studies on time are critical aspects of clinical study delivery planning.
During this presentation, an experienced Premier Research feasibility and patient recruitment specialist, reviewed feasibility approaches to address protocol evaluation as well as addressed influences on country selection, site distribution and patient recruitment strategies to provide for more effective clinical trial planning and conduct.
For more information, go to http://www.premier-research.com.
Successful Pediatric Studies: Key Study Design and Site Selection Considerationsjbarag
The industry recognizes the importance of ensuring the safety and well‐being of children involved in research studies. Medical and regulatory bodies have worked to provide a framework to support appropriately designed studies through regulations and guidance documents in this vulnerable population. However, it is crucial to understand the nuances associated with pediatric trials, for the site, patient and family, in order to manage them to successful completion.
During the 2012 ACRP Annual Meeting, Dr. Charlene Sanders and Angi Robinson from Premier Research reviewed topics including the evaluation of study design considerations such as duration of treatment, required assessments, use of placebo, and inclusion of specific age groups; selection of appropriate sites for pediatric trials and the unique needs of these sites; identification of pediatric recruitment/retention hurdles and site specific strategies to overcome these as well as a reflection on ethical concerns related to pediatric research.
For more information, go to http://www.premier-research.com/pediatrics.
Creating Effective Pediatric Assent Forms: Overcoming Common Obstaclesjbarag
This document discusses creating effective pediatric assent forms by overcoming common obstacles. It identifies five main obstacles: 1) treating assent as an afterthought, 2) lack of direction from sponsors/IRBs, 3) failure to account for developmental ages and reading levels, 4) difficulty creating readable forms, and 5) not planning the assent process logistics. It provides tools to write forms at appropriate reading levels, ensure all elements of assent are addressed, and plan who will obtain assent and where. The goal is to engage children in a developmentally-appropriate way and respect their participation in research decisions.
This document summarizes a webinar on streamlining data management for clinical trials. The webinar covered the need for streamlined approaches given rising drug development costs. It discussed areas for improving efficiencies, including using standards, a parallel approach, identifying key reviewers, and tailoring processes based on trial type (e.g. a "Premier Express" approach for small phase 1 trials). An example case study showed how streamlining tasks and performing work in parallel reduced timelines for developing case report forms, annotated case report forms, databases, and edit checks for a small phase 1 trial from 9 weeks to 5 weeks.
This presentation, led by Ryan Michaud, explored how to best employ IV/IWRS platforms for collecting and managing Patient Reported Outcomes (PRO) data, clinical supply management including drug accountability, visit tracking and randomization.
During this presentation, Ron Kershner, Ph.D. discussed the responsibilities of DMCs from the perspective of protecting patient safety and providing critical, independent oversight to key study objectives. Drawing on past clinical trials to illustrate key points, Ron addressed DMC operational considerations, such as meeting frequency and content, control of information, data cleaning issues and scope/format of data tabulations.
During this presentation, Dr. Charlene Sanders and Angi Robinson reviewed topics including the evaluation of study design considerations such as duration of treatment, required assessments, use of placebo, and inclusion of specific age groups; selection of appropriate sites for pediatric trials and the unique needs of these sites; identification of pediatric recruitment/retention hurdles and site specific strategies to overcome these as well as a reflection on ethical concerns related to pediatric research.
Guidelines for Effective and Appropriate Pediatric Assent and Parental Permis...jbarag
During this presentation, Angi Robinson and Elizabeth Jay reviewed the regulatory requirements for parental permission and pediatric assent; provided practical tips for compliant and age-appropriate form development including which elements to incorporate, the number of required signatures, and how to check for reading comprehension level; and offered recommendations for documentation of the consenting/assenting process.
Planning your Paediatric Investigation Plan (PIP) Submission in Europejbarag
During this presentation, Dr. Susan Bhatti, an experienced regulatory affairs professional, shared best practices and experiences learned from submitting PIPs. This included a brief review of the pediatric regulation requirements, insight for interacting with PDCO, and an overview of the PIP submission including procedures, timelines, structure and compliance.
Developing a Feasible Pediatric Plan for PREA/PMDSIA Compliancejbarag
This presentation will address issues surrounding the development of a pediatric plan for PREA/PMDSIA compliance including: when to develop a pediatric plan, what age groups should be included, is a pediatric formulation necessary, timing of the pediatric studies, what information should be submitted to FDA, and when a waiver or deferral is appropriate.
• Planning your PIP submission
• Which in-house departments should be involved?
• Interaction with CRO/ writer
• Interaction with PDCO
• Key points for successful PIP outsourcing
Centralized Resourcing Model for Clinical Trialsjbarag
A centralized resourcing model within clinical research organizations can provide efficiencies in resource management. It involves having a central point of contact to manage the resource assignment, deployment, and utilization across programs. Key ingredients for success include regular communication, standardized processes for resource requests and tracking, and reliable tools for resource planning and metrics. This model allows resources to be strategically allocated, utilization to be maximized through short-term assignments, and proactive planning to improve cost and time efficiencies.
Medical Writing Essential: Reviewing Statisitical Analysis Plansjbarag
Regulatory medical writers are tasked with generating high-quality clinical study reports (CSRs) promptly. To this end, statistical analysis plan (SAP) reviews are essential as they allow medical writers to verify that the SAP contains the information required for the CSR per regulatory guidance. This session will explain how to conduct SAP reviews and how to assess whether data presentations in addition to those proposed are needed for the CSR.
Ind Applications: A Case Study of Document Development from the Medical Writi...jbarag
This session is a presentation of a case study on management of a complex document development program for Investigational New Drug (IND) submissions. The scope of writing 19 documents for 3 IND submissions in 6 months dictated expert project management by a medical writing (MW) team. Through meticulous organization, strategic planning and communication, MW effectively managed the process and delivered the required documents well before the IND submission deadlines.
Meeting Enrollment Goals in a Competitive Environmentjbarag
Challenges in patient recruitment continue to be the number one cause in clinical trial delays. Through involvement in this workshop, participants will become familiar with how to develop, implement, manage and track site enrollment plans. This will include understanding the core elements that constitute an enrollment plan as well as understanding how the development of strategic tools and tactics can aid sites in the successful implementation, monitoring and tracking of results. Both project management and site perspectives on enrollment and recruitment plans will be discussed.
This document discusses managing high performance project teams. It emphasizes that conducting clinical research requires contributions from all team members. It outlines some fundamentals and challenges of project management including forming a cohesive team, maintaining motivation, and communication. It provides basics for managing teams such as establishing roles and responsibilities, communication plans, and holding regular meetings. It also discusses important leadership skills like being a good listener, connecting with others, and insulating the team from issues. Proactive communication techniques are also covered like being mindful of tone, email management, and making requests.
These lecture slides, by Dr Sidra Arshad, offer a quick overview of the physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar lead (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
6. Describe the flow of current around the heart during the cardiac cycle
7. Discuss the placement and polarity of the leads of electrocardiograph
8. Describe the normal electrocardiograms recorded from the limb leads and explain the physiological basis of the different records that are obtained
9. Define mean electrical vector (axis) of the heart and give the normal range
10. Define the mean QRS vector
11. Describe the axes of leads (hexagonal reference system)
12. Comprehend the vectorial analysis of the normal ECG
13. Determine the mean electrical axis of the ventricular QRS and appreciate the mean axis deviation
14. Explain the concepts of current of injury, J point, and their significance
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. Chapter 3, Cardiology Explained, https://www.ncbi.nlm.nih.gov/books/NBK2214/
7. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
share - Lions, tigers, AI and health misinformation, oh my!.pptxTina Purnat
• Pitfalls and pivots needed to use AI effectively in public health
• Evidence-based strategies to address health misinformation effectively
• Building trust with communities online and offline
• Equipping health professionals to address questions, concerns and health misinformation
• Assessing risk and mitigating harm from adverse health narratives in communities, health workforce and health system
Rasamanikya is a excellent preparation in the field of Rasashastra, it is used in various Kushtha Roga, Shwasa, Vicharchika, Bhagandara, Vatarakta, and Phiranga Roga. In this article Preparation& Comparative analytical profile for both Formulationon i.e Rasamanikya prepared by Kushmanda swarasa & Churnodhaka Shodita Haratala. The study aims to provide insights into the comparative efficacy and analytical aspects of these formulations for enhanced therapeutic outcomes.
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Our backs are like superheroes, holding us up and helping us move around. But sometimes, even superheroes can get hurt. That’s where slip discs come in.
Cell Therapy Expansion and Challenges in Autoimmune DiseaseHealth Advances
There is increasing confidence that cell therapies will soon play a role in the treatment of autoimmune disorders, but the extent of this impact remains to be seen. Early readouts on autologous CAR-Ts in lupus are encouraging, but manufacturing and cost limitations are likely to restrict access to highly refractory patients. Allogeneic CAR-Ts have the potential to broaden access to earlier lines of treatment due to their inherent cost benefits, however they will need to demonstrate comparable or improved efficacy to established modalities.
In addition to infrastructure and capacity constraints, CAR-Ts face a very different risk-benefit dynamic in autoimmune compared to oncology, highlighting the need for tolerable therapies with low adverse event risk. CAR-NK and Treg-based therapies are also being developed in certain autoimmune disorders and may demonstrate favorable safety profiles. Several novel non-cell therapies such as bispecific antibodies, nanobodies, and RNAi drugs, may also offer future alternative competitive solutions with variable value propositions.
Widespread adoption of cell therapies will not only require strong efficacy and safety data, but also adapted pricing and access strategies. At oncology-based price points, CAR-Ts are unlikely to achieve broad market access in autoimmune disorders, with eligible patient populations that are potentially orders of magnitude greater than the number of currently addressable cancer patients. Developers have made strides towards reducing cell therapy COGS while improving manufacturing efficiency, but payors will inevitably restrict access until more sustainable pricing is achieved.
Despite these headwinds, industry leaders and investors remain confident that cell therapies are poised to address significant unmet need in patients suffering from autoimmune disorders. However, the extent of this impact on the treatment landscape remains to be seen, as the industry rapidly approaches an inflection point.
1. 2 0 1 1 FA L L B I O M E T R I C S W E B I N A R S E R I E S
Strategies for Implementing CDISC
Dec. 13, 2011 Presented by Thomas Kalfas
2. Thomas Kalfas
Director, Global Biometrics Technical Operations
24+ years of technical data management and
biostatistical programming experience in pharma/
biotech/CRO industries
Member: CDISC IAB/CAB, CDISC SDS and CDISC
Validation teams since 2006
Focus on technical operations, standards development
and implementation
2011 FALL BIOMETRICS WEBINAR SERIES
3. CDISC Acronyms/Definitions
3
ADaM – Analysis Data Model; statistical analysis data
standards
CDASH – Clinical Data Acquisition Standards Harmonization;
Case Report Form (CRF) standards
CDISC – Clinical Data Interchange Standardization Consortium;
organization advocating global standards for clinical trial data
Define Doc aka Define.xml aka CRT-DD – dataset
specifications; a dynamic table of contents for the submission
datasets (SDTM and/or ADaM)
SDTM – Study Data Tabulation Model; clinical trial data
standards
2011 FALL BIOMETRICS WEBINAR SERIES
4. Objectives
4
Topics will include a brief review of CDISC,
implementation challenges, and insight into the best
timing for implementation.
We will not be going in-depth into actual conversions
or creation of specific CDISC domains, but rather
focusing on high-level requirements, issues and
feedback from the FDA, common approaches for
standards implementation, and our recommendations.
2011 FALL BIOMETRICS WEBINAR SERIES
5. Today’s Topics
5
1) Business Justification for CDISC
2) Current CDISC Status
3) Methods for Producing CDISC Deliverables
– Common Errors
– Timing Considerations and Best Methods
– Recommendations
4) Summary
2011 FALL BIOMETRICS WEBINAR SERIES
6. Business Justification for CDISC
“ The Center for Drug Evaluation and Research
(CDER) is strongly encouraging sponsors to
submit data in standard form as a key part of
“
its efforts to continue with advancement of
review efficiency and quality.
-CDER, May 2011
2011 FALL BIOMETRICS WEBINAR SERIES
7. Origins
7
Critical Path Initiative (2004)
Streamline the submissions/review process, shorten the
review cycles, decrease costs, and allow for easier
data warehousing
FDA asked sponsors to voluntarily use SDTM and
ADaM standards for the e-submissions
Powerful tools/software to be developed (based on
these standards) to assist reviewers with their
evaluations
2011 FALL BIOMETRICS WEBINAR SERIES
8. Business Justification for CDISC
8
FDA encouraging sponsors to continue
the learning curve on CDISC standards
– No submission will be – Ultimately (~1-2 years),
rejected for non- a minimum level of
compliance compliance will be
expected for all
– If compliant, then review submissions, and if not
(after training completed met, then these “CDISC-
and reviewers have like” submissions would
become familiar with be treated as non-CDISC
format/tools) should be or “legacy” data (and
quicker would take much longer
to review)
2011 FALL BIOMETRICS WEBINAR SERIES
9. Business Justification for CDISC
9
Process Improved Cost Value-
data
efficiencies quality savings added
CDISC
2011 FALL BIOMETRICS WEBINAR SERIES
10. Cost Savings
10
Estimated 30% clinical trial efficiencies gained (project startup,
cleaning, programming and analysis)
– Potentially reduce the study lifecycle by 8 months resulting in savings of
approximately $9 billion annually
Cycle Time in Months
8 Months
Cycle Time
Reduction/Trial
Estimated thata restricted implementation of these standards at tail-
end submission stage would decrease the potential return by only 60%
2011 FALL BIOMETRICS WEBINAR SERIES Source: Gartner&CDISC (November 2006))
11. Current CDISC Status
FDA Trends
CDISC Updates
Premier Research and CDISC
2011 FALL BIOMETRICS WEBINAR SERIES
12. FDA CDISC Trends
12
Committed to Standards Initiative (2009-2013) and
actively working to “refine and maximize utility of
CDISC standards”
Both CDER and CBER accepting/requesting SDTM
*and* ADaM formatted datasets since December 2010
Number of eSubmissions has increased by ~2K per
month since 2010
CDER now tracking number of submissions (SDTM and
ADaM) on their website
2011 FALL BIOMETRICS WEBINAR SERIES
13. FDA CDISC Trends
13
Source: http://www.accessdata.fda.gov/FDATrack/track?program=cder&id= CDER-
OB-NDAs-BLAs-and-Efficacy-Supplements-with-Electronic-Datasets-Available
2011 FALL BIOMETRICS WEBINAR SERIES
14. CDISC Updates
14
Working closely with the FDA to:
– Determine “Supp-qual” data needed to be added to main domains
– Have reviewers comfortable with the Implementation Guide (IG)
– Build review automation for standard analysis
Providing further guidance to the industry to help navigate gray
areas within the standard
Developing therapeutic standards, i.e., supplements to the IG to
address specific implementations
Developing a set of Medical Device domain standards
Discussing/clarifying type/location of derived variables (SDTM
vs. ADaM)
2011 FALL BIOMETRICS WEBINAR SERIES
15. Premier Research and CDISC
15
Spending more time with our clients discussing the benefits
of early-stage CDISC implementation
Premier has developed CRF and DB standards in line with
CDASH and SDTM. Use of Premier Standards enables our
operational staff (CRF- and DB-developers and
programmers) to utilize these standards to help realize the
efficiencies anticipated by Gartner in their projections of
industry savings.
Number of early-stage CDISC (SDTM) projects for 2011
has tripled that of 2010 (~40% of our active projects)
2011 FALL BIOMETRICS WEBINAR SERIES
16. Premier Research and CDISC
16
Spike in requests to add SDTM as a requirement for
existing “legacy” projects
Also seeing an increase in requests for:
– Early-stage feeds into a ISS/ISE-like datamart
– CDISC training
– Consulting services for implementation of CRF, DB and
programming standards for existing and new clients
2011 FALL BIOMETRICS WEBINAR SERIES
17. Methods for Producing
CDISC Deliverables
Common Errors
Timing Considerations and Best Methods
Recommendations
2011 FALL BIOMETRICS WEBINAR SERIES
18. Common Errors (per FDA)
18
“SDTM-Like” submissions Non-compliant define.xml
Traceability issues Define doesn’t validate
Invalid ISO 8601 date format Required variable not found
Inconsistent value for standard units Invalid value for MedDRA Term
All dates in the SDTM domains must conform to the ISO 8601 format
Begin Date must be ≤ End Date (e.g., CM or AE start dates that come after
the end dates)
For a given test, all values of --STRESU should be the same. In some cases -
-TESTCD may not be sufficient to uniquely indentify a test
For a full list, go to:
http://www.fda.gov/downloads/Drugs/DevelopmentApprovalProcess/Form
sSubmissionRequirements/ElectronicSubmissions/UCM254113.pdf
http://www.fda.gov/BiologicsBloodVaccines/DevelopmentApprovalProcess/
ucm209163.htm
2011 FALL BIOMETRICS WEBINAR SERIES
19. Common Methods
19
Study Close DB
Start-up Study Conduct Out Lock
Late-Stage
Conversion
Early-Stage Mid-Stage
Implementation Conversion
2011 FALL BIOMETRICS WEBINAR SERIES
20. Common Methods:
Late-Stage Conversion (1)
20
Usually as part of an NDA submission, but now being
requested for individual or groups of studies earlier
than the traditional NDA activities
Cost-effective? Might seem so, but…
Compliance issues common as this could take place months
or even years after the study had completed (dependent
on the quality/compliance issues introduced at the protocol
and CRF layers)
Traceability issues (it is critical that the data is traceable
from the CSR to the analysis datasets to the SDTM
datasets to the raw datasets to the CRFs)
2011 FALL BIOMETRICS WEBINAR SERIES
21. Common Methods:
Late-Stage Conversion (2)
21
DB is locked!!!
Cleaning of DB may not have incorporated all SDTM
compliance checks, e.g., start dates must be less than or
equal to stop dates, etc.
Aside from the database, what else has been produced
that will now need to be reconciled against the “new”
SDTM datasets? Analysis datasets? TLG’s? ISS/ISE? CSR?
The further along you are with the study, the more work
necessary to ensure traceability
2011 FALL BIOMETRICS WEBINAR SERIES
22. Common Methods:
Late-Stage Conversion (3)
22
Need to:
Determine need for standardization of values/units, e.g., labs, and
coding dictionaries (if multiple studies, should have same version of
dictionary)
Completely document steps needed to produce SDTM and
ADaM…you’ll need not just for the conversion, but also for the
Define Docs
Fully assess risk/impact of any review/compliance findings on
downstream deliverables
Documentation is critical (Annotated CRFs, Dataset Specifications,
Code Lists, Change Logs, Define Docs)
Reproduce numbers from your CSR! Need to show that the
analysis/results can be reproduced from SDTM
2011 FALL BIOMETRICS WEBINAR SERIES
23. Common Methods:
Late-Stage Conversion (4)
23
Need to (continued):
Maintain Change Logs to track programming changes (in addition to
maintenance of your “living” documents, i.e., SDTM Dataset
Specifications -> Define Doc
Reproduce numbers from your CSR! Need to show that the
analysis/results can be reproduced from SDTM
– If significant issues, then need resolution
– If differences can be explained, then consider adding text explanation
to the “Reviewer Notes” portion of the Define Doc
Same as above for your compliance checks, i.e., any oddities need
to be explained in the reviewer notes
2011 FALL BIOMETRICS WEBINAR SERIES
24. Common Methods:
Mid-Stage Conversion
24
Usually as a late CDISC consideration in preparation
for NDA activities
Same issues/challenges as for the Late-Stage Conversion;
however…
DB is active, not locked!
So, while there are most likely still conversion challenges
with non-standard CRF/DB setup, it is now easier to
address compliance/review findings as part of you normal
DM/cleaning processes!
2011 FALL BIOMETRICS WEBINAR SERIES
25. Common Methods:
Early-Stage Implementation (1)
25
Standards compliance from the very start
Protocol:
– Controlled Terminology, e.g., AE Severity
CRFs:
– CDASH: for items not covered by SDTM directly, e.g., date
component fields
– SDTM: want to get as close to 100% SDTM compliance as
possible from the CRFs and DB
– Controlled Terminology → reduces the need for additional
programming/mapping/conversions (and QC)
2011 FALL BIOMETRICS WEBINAR SERIES
26. Common Methods:
Early-Stage Implementation (2)
26
Database:
– Standard modules
– Standard checks (cleaning and compliance)
– Standardization of values/units, medical coding, etc.
Programming:
– Still requires thorough documentation (Annotates, Specs,
Define Docs), but development/maintenance is much easier
as standard templates can be used
– Standard programs refine the CDISC-like (both SDTM and
ADaM) datasets into 100% compliant datasets
– Standard programs used to load into ISS/ISE, produce
standard TLGs, etc.
2011 FALL BIOMETRICS WEBINAR SERIES
27. Common Methods:
Early-Stage Implementation (3)
27
Efficiencies are realized due to standards adherence
Traceability is inherent within this process due to a more
traditional SDLC methodology (i.e., specs first, then
development, then validation, then these feed into the next
deliverable, e.g., ADaM, ISS/ISE, and TLGs)
Value-added: reliance on standards, allows for CDISC
outputs to be produced earlier in the study “life-cycle” and
made available for data warehousing, data mining (as
ISS/ISE) and DSMB/DMC requirements
2011 FALL BIOMETRICS WEBINAR SERIES
28. Summary
CDISC SDTM & ADaM standards are gaining traction
Requirements for CDISC datasets getting stronger
While late- and mid-stage conversions can and will
continue to be done, the FDA cautions against this in
favor of early-stage CDISC standards implementation
Early-stage implementations of CDISC not only allow
for efficiencies to be realized, but also make value-
added scenarios possible
2011 FALL BIOMETRICS WEBINAR SERIES
29. Webinars Series
29
Listen to past webinars:
▪ The Role of Data Monitoring Committees
Speaker: Ron Kershner, Ph.D.
▪ IVR/IWR…More than just Randomization
Speaker: Ryan Michaud
▪ Streamlining Data Management Start-up
Speaker: Cheryl Silva
2011 FALL BIOMETRICS WEBINAR SERIES
30. Questions?
Thomas Kalfas
Director, Global Biometrics Technical Operations
Telephone: 847.420.2622
thomas.kalfas@premier-research.com
2011 FALL BIOMETRICS WEBINAR SERIES