CLINICAL STUDY REPORT - IN-TEXT TABLES, TABLES FIGURES AND GRAPHS, PATIENT AND INDIVIDUAL PATIENT DATA LISTINGS: ICH E3 TECHNICAL REQUISITES AND POSSIBLE SOLUTION IN SAS
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.
CDISC's CDASH and SDTM: Why You Need Both!Kit Howard
CDISC's clinical data standards are widely used for clinical research, but many people wonder why there seem to be two standards for collected data: the Clinical Data Acquisition Standards Harmonization (CDASH) standard and the Study Data Tabulation Model (SDTM) standard. This poster steps through four significant reasons that reflect the differences in philosophy, intermediate goals and broad-scale uses. Examples illustrate each reason and how they affect your studies.
SDTM (Study Data Tabulation Model) defines a standard for organizing and formatting data to streamline processes in collection, management, analysis and reporting of human clinical trial data tabulations and for non-clinical study data tabulations which are to be submitted as part of a product application(IND and NDA) to a regulatory authority such as the United States Food and Drug Administration (FDA) and PMDA (Japan)
CDISC's CDASH and SDTM: Why You Need Both!Kit Howard
CDISC's clinical data standards are widely used for clinical research, but many people wonder why there seem to be two standards for collected data: the Clinical Data Acquisition Standards Harmonization (CDASH) standard and the Study Data Tabulation Model (SDTM) standard. This poster steps through four significant reasons that reflect the differences in philosophy, intermediate goals and broad-scale uses. Examples illustrate each reason and how they affect your studies.
SDTM (Study Data Tabulation Model) defines a standard for organizing and formatting data to streamline processes in collection, management, analysis and reporting of human clinical trial data tabulations and for non-clinical study data tabulations which are to be submitted as part of a product application(IND and NDA) to a regulatory authority such as the United States Food and Drug Administration (FDA) and PMDA (Japan)
SDTM (Study Data Tabulation Model) defines a standard structure for human clinical trial (study) data tabulations and for nonclinical study data tabulations that are to be submitted as part of a product application to a regulatory authority such as the United States Food and Drug Administration (FDA).
In this presentation, Principal Statistical Scientist Ben Vaughn explains how clinical trial data moves from collection in the case report form to its presentation to FDA.
Shannon Labout has more than 17 years of experience in healthcare technologies, project management and clinical research. She is the past Senior Director of Education at CDISC, and has developed and delivered training on CDISC standards for audiences in North America, Europe and Asia since 2007. She has been involved in CDASH since the beginning of the project in 2006, co-led the CDASH team for the past 3-1/2 years, and has been a contributing member of the SDS team since 2007. She has participated in CRF standardization for the past fourteen years, and been involved in data standards development, harmonization and implementation at several CROs and global pharmaceutical companies. She has managed clinical data management teams in both the U.S. and Europe, and is currently the Director Data Management at Statistics & Data Corporation based in Tempe, Arizona.
Source: http://www.arena-international.com/ecdm/shannon-labout/3038.speaker
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.
An overview of the ICH E9 guidance. Easy to follow, and I can provide a live presentation of this to your team! Great for those who are not familiar with statistics.
Literature Surveillance in Pharmacovigilance; Current Trends, Methods and Challenges
Please join Elizabeth E. Garrard, PharmD, founder and CEO of Garrard Safety Solutions, as she reviews key issues in literature surveillance for Pharmacovigilance.
Objectives:
• Understand the regulatory obligations, best sources and procedures for conducting literature surveillance.
• Appreciate some examples of when a safety signal was detected in the literature and its impact on the lifecycle of a drug.
• Understand when to start and where to look for emerging safety information.
• Setting up your search strategy, how to ensure your search strings are well balanced, recognizing the challenges between precision and sensitivity.
• What is the impact of the new literature monitoring by EMA of a number of substances in selected medical literature to identify suspected adverse reactions with medicines authorized in the European Union. Early insights into successes and issues.
• Discuss current methods that can increase the likelihood of early detection of a safety issue and minimize the issues surrounding.
• Realize the challenges we face including wide differences in quality, accuracy, and completeness in the scientific literature and how best to navigate these differences and maintain proper vigilance.
SDTM Training for personnel with Junior and Intermediate level Clinical Trial Experience. Covers summary of most domains. Salient features include order of domain creation, importance of making programming Data/Metadata Driven, Nature of Clinical Raw Data, Summary of the Clinical Trial process with regards to the data flow to arrive at the Study data to be submitted to regulatory authorities like FDA, Importance of deriving ADAM from SDTM and not directly from raw data, Information has been put together from variety of sources including my own programming work.
Argus Screen Shots General Tab - Katalyst HLSKatalyst HLS
Introduction to Argus Screen Shots General Tab - Drug Safety & Pharmacovigilance of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
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 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.
HIS purchase projects in public hospitals of StyriaMiroslav Mađarić
The KAGes (Steiermärkische Krankenanstaltenges.m.b.H) is a company, owned by the province of Styria in Austria, which operates 21 hospitals with about 8.000 beds and 14.000 employees, serving a population of ca. 1.2 million people. KAGes has purchased a new hospital information system (HIS) for its hospitals. Within the strategic IT plan and the "System Structure New" (SSN) project a methodology was developed, for making an effective HIS purchase. Several steps of this project are described in the paper: request for product information, evaluation of vendor proposals, product presentations, test site evaluation, reference site visits and selection of vendor finalists. The authors present the internal project management methodology, including the structure of the project team, project information management through intranet, criteria for different steps of the evaluation and evaluation site organization. Four major HIS vendors with leading HIS products qualified for this stage of the project (evaluation site). About 60 teams with 400 members (end users and IT-experts) have assessed all the products installed, during one or more, repeated, test sessions. The decision on which new HIS to purchase were based on the recommendations derived from this evaluation. After completing SSN-project with the suggestion to KAGes-Top-Management for negotiations with two vendor finalists, the new project named MEDOCS was started mid 1999. Two pilot installations (one general hospital and one teaching hospital department) are nowadays in the pilot implementation and subsequent roll-out (including substitution of the legacy system) is scheduled until the year 2003.
Keywords: Hospital information system; Project management; Evaluation
SDTM (Study Data Tabulation Model) defines a standard structure for human clinical trial (study) data tabulations and for nonclinical study data tabulations that are to be submitted as part of a product application to a regulatory authority such as the United States Food and Drug Administration (FDA).
In this presentation, Principal Statistical Scientist Ben Vaughn explains how clinical trial data moves from collection in the case report form to its presentation to FDA.
Shannon Labout has more than 17 years of experience in healthcare technologies, project management and clinical research. She is the past Senior Director of Education at CDISC, and has developed and delivered training on CDISC standards for audiences in North America, Europe and Asia since 2007. She has been involved in CDASH since the beginning of the project in 2006, co-led the CDASH team for the past 3-1/2 years, and has been a contributing member of the SDS team since 2007. She has participated in CRF standardization for the past fourteen years, and been involved in data standards development, harmonization and implementation at several CROs and global pharmaceutical companies. She has managed clinical data management teams in both the U.S. and Europe, and is currently the Director Data Management at Statistics & Data Corporation based in Tempe, Arizona.
Source: http://www.arena-international.com/ecdm/shannon-labout/3038.speaker
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.
An overview of the ICH E9 guidance. Easy to follow, and I can provide a live presentation of this to your team! Great for those who are not familiar with statistics.
Literature Surveillance in Pharmacovigilance; Current Trends, Methods and Challenges
Please join Elizabeth E. Garrard, PharmD, founder and CEO of Garrard Safety Solutions, as she reviews key issues in literature surveillance for Pharmacovigilance.
Objectives:
• Understand the regulatory obligations, best sources and procedures for conducting literature surveillance.
• Appreciate some examples of when a safety signal was detected in the literature and its impact on the lifecycle of a drug.
• Understand when to start and where to look for emerging safety information.
• Setting up your search strategy, how to ensure your search strings are well balanced, recognizing the challenges between precision and sensitivity.
• What is the impact of the new literature monitoring by EMA of a number of substances in selected medical literature to identify suspected adverse reactions with medicines authorized in the European Union. Early insights into successes and issues.
• Discuss current methods that can increase the likelihood of early detection of a safety issue and minimize the issues surrounding.
• Realize the challenges we face including wide differences in quality, accuracy, and completeness in the scientific literature and how best to navigate these differences and maintain proper vigilance.
SDTM Training for personnel with Junior and Intermediate level Clinical Trial Experience. Covers summary of most domains. Salient features include order of domain creation, importance of making programming Data/Metadata Driven, Nature of Clinical Raw Data, Summary of the Clinical Trial process with regards to the data flow to arrive at the Study data to be submitted to regulatory authorities like FDA, Importance of deriving ADAM from SDTM and not directly from raw data, Information has been put together from variety of sources including my own programming work.
Argus Screen Shots General Tab - Katalyst HLSKatalyst HLS
Introduction to Argus Screen Shots General Tab - Drug Safety & Pharmacovigilance of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
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 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.
Similar to CLINICAL STUDY REPORT - IN-TEXT TABLES, TABLES FIGURES AND GRAPHS, PATIENT AND INDIVIDUAL PATIENT DATA LISTINGS: ICH E3 TECHNICAL REQUISITES AND POSSIBLE SOLUTION IN SAS
HIS purchase projects in public hospitals of StyriaMiroslav Mađarić
The KAGes (Steiermärkische Krankenanstaltenges.m.b.H) is a company, owned by the province of Styria in Austria, which operates 21 hospitals with about 8.000 beds and 14.000 employees, serving a population of ca. 1.2 million people. KAGes has purchased a new hospital information system (HIS) for its hospitals. Within the strategic IT plan and the "System Structure New" (SSN) project a methodology was developed, for making an effective HIS purchase. Several steps of this project are described in the paper: request for product information, evaluation of vendor proposals, product presentations, test site evaluation, reference site visits and selection of vendor finalists. The authors present the internal project management methodology, including the structure of the project team, project information management through intranet, criteria for different steps of the evaluation and evaluation site organization. Four major HIS vendors with leading HIS products qualified for this stage of the project (evaluation site). About 60 teams with 400 members (end users and IT-experts) have assessed all the products installed, during one or more, repeated, test sessions. The decision on which new HIS to purchase were based on the recommendations derived from this evaluation. After completing SSN-project with the suggestion to KAGes-Top-Management for negotiations with two vendor finalists, the new project named MEDOCS was started mid 1999. Two pilot installations (one general hospital and one teaching hospital department) are nowadays in the pilot implementation and subsequent roll-out (including substitution of the legacy system) is scheduled until the year 2003.
Keywords: Hospital information system; Project management; Evaluation
PLM Strategy for Developing Specific Medical DevicesSohailAkbar14
Introduction
Background
Methodology
Results
Guidelines to Build a PDM Framework
Technology Selection
Strategy Conceptual Framework
Case Studies
Technologies for Specific Patients
Lower Limb Prosthesis
The Proposed Strategy
Discussion and Conclusions
Lecture on the role of IS/IT in the healthcare domain. Various types of IS/IT are introduced. Also, the benefits of IS/IT in healthcare are outlined. Various developments in healthcare from a hospital CXO perspective are specified. Finally, I will also presentbenefits and costs associated with the implementation of health information technology.
Why ICT Fails in Healthcare: Software Maintenance and MaintainabilityKoray Atalag
This presentation was for a SERG seminar at the University of Auckland Department of Computer Science. I present why software maintenance is a barrier for adoption of IT in healthcare and the maintainability aspects based on ISO/IEC 9126 software quality standard quality model. I then present the preliminary results of my research here.
Similar to CLINICAL STUDY REPORT - IN-TEXT TABLES, TABLES FIGURES AND GRAPHS, PATIENT AND INDIVIDUAL PATIENT DATA LISTINGS: ICH E3 TECHNICAL REQUISITES AND POSSIBLE SOLUTION IN SAS (20)
The use of Adaptive designs is becoming quite popular and well-perceived by the regulatory agencies such as the FDA in the US. “Adaptation” can occur in different fashion and potentially make studies more efficient (e.g. shorter duration, fewer patients) more likely to demonstrate an effect of the drug if one exists, or more informative (see “Adaptive Design Clinical Trials for Drugs and Biologics” FDA guidance).
The aim of this presentation is to illustrate a case where an adaptive design was used in a Phase III oncology pivotal study having Overall Survival as a primary end-point. The particular adaptation implemented was an un-blinded SSR that applied a promising zone approach.
The main focus will be how the adaptive design impacted the SDTM modelling, the design of some ADaM datasets (e.g. those containing the time-to-event endpoints and therefore using ADTTE ADaM model) and later on how some mapping and analysis decisions were described in both the study and analysis reviewer guide.
The use of Adaptive designs is becoming quite popular and well-perceived by the regulatory agencies such as the FDA in the US. “Adaptation” can occur in different fashion and potentially make studies more efficient (e.g. shorter duration, fewer patients) more likely to demonstrate an effect of the drug if one exists, or more informative (see “Adaptive Design Clinical Trials for Drugs and Biologics” FDA guidance).
The aim of this presentation is to illustrate a case where an adaptive design was used in a Phase III oncology pivotal study having Overall Survival as a primary end-point. The particular adaptation implemented was an un-blinded SSR that applied a promising zone approach.
The main focus will be how the adaptive design impacted the SDTM modelling, the design of some ADaM datasets (e.g. those containing the time-to-event endpoints and therefore using ADTTE ADaM model) and later on how some mapping and analysis decisions were described in both the study and analysis reviewer guide.
While the evolution of information technology is bringing the data closer to customers for their own exploration, the need of a comprehensive understanding of the therapeutic area knowledge for programmers in clinical development is increasing. Starting with a basic understanding on the medical background, special assessment methods, ways of statistically analyzing and displaying the data, to name a few essential ones enables programmers to interact with partners (e.g. scientist, statisticians etc.) on equal par.
In this intent, activities to collect and provide comprehensive information around the Oncology and Rheumatoid Arthritis Therapeutic Areas (TA) via the PhUSE Wiki had started in February 2013 and continued throughout the year. Various PhUSE members have spent time and energy to provide and expand their knowledge and make it available to the entire community.
Today, although there is still much to do to complete and maintain the collected material, the two TA Wikis are a useful tool for Statistical Programmers approaching these TA for the first time or who want to improve their knowledge. Moreover the PhUSE Wiki can be seen as a basic tool for future developments to improve the way professionals in the different TA work. An established working relationship across organizations, pharmaceutical companies or external service providers, will help to support implementation of TA-specific standards from mapping raw data in SDTM, data analysis using ADaM and finally data presentation in standardized outputs. The PhUSE Wiki can be the central place to share important updates such as new CDISC TA standards or the availability of new TA regulatory guidance. On the other hand we see the Wiki as a place to discuss, to stimulate and inspire new initiatives among the “SAS-Programming Community”, be it Statisticians, Programmers, Data Managers or everyone else involved; this may include specific TA working related white papers and/or scripts being part of the FDA Working Groups WG5 “Development of Standard Scripts for Analysis and Programming” Project 08 “Create white papers providing recommended display and analysis including Table, List and Figure shells”.
Presented at PhUSE/FDA CSS 2014 in Silver Spring (US)
Presented at PhUSE 2013
The evaluation of efficacy in oncology studies, in particular for solid tumors, is pretty standard and well defined by several regulatory guidance (e.g. EMA and FDA), including some specific cancer type guidance (e.g. NSCLC from FDA).
Although some references will be also given for non-solid tumors, the paper will mainly focus on solid tumors efficacy
endpoints.
Overall Survival, Best Overall Response as per RECIST criteria, Progression Free Survival (PFS), Time to Progression (TTP), Best Overall Response Rate are some of the key efficacy indicators that will be discussed.
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
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Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
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Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
CLINICAL STUDY REPORT - IN-TEXT TABLES, TABLES FIGURES AND GRAPHS, PATIENT AND INDIVIDUAL PATIENT DATA LISTINGS: ICH E3 TECHNICAL REQUISITES AND POSSIBLE SOLUTION IN SAS
1. Geneva Branch
CLINICAL STUDY REPORT - IN-TEXT
TABLES, TABLES FIGURES AND GRAPHS,
PATIENT AND INDIVIDUAL PATIENT DATA
LISTINGS: ICH E3 TECHNICAL REQUISITES
AND POSSIBLE SOLUTION IN SAS
Data handling and reporting in clinical trials with SAS
Seminario BIAS – Milano 22 / 02 /2013
Angelo Tinazzi
Cytel Inc., Wilmington Del. USA
Succursale de Meyrin – Geneva – Switzerland
angelo.tinazzi@cytel.com
2. Cytel Inc. - Confidential
Agenda
Introduction to ICH E3
Key points in ICH E3 referring to statistical outputs
production
ICH E3 Additional Considerations
Technical Solutions
Software requirements overview
In-house solutions
Facilitate the work of the medical writer
Other possible topics for discussion
References
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
2
Geneva Branch
Geneva Branch
3. Cytel Inc. - Confidential
Agenda
Introduction to ICH E3
Key points in ICH E3 referring to statistical outputs
production
ICH E3 Additional Considerations
Technical Solutions
Software requirements overview
In-house solutions
Facilitate the work of the medical writer
Other possible topics for discussion
References
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
3
Geneva Branch
Geneva Branch
4. Cytel Inc. - Confidential
Introduction to ICH E3
4
Geneva Branch
Geneva Branch
Structure and Content of Clinical Study
Reports
CSRs describe the background, rationale, methodology
and full results for a clinical study
Called integrated reports as they cover clinical and
statistical aspects
Guideline ICH E3 on structure and content of CSRs: 53
pages of ‘guidance’
Other Guidance
ICH E9 Statistical Principles for Clinical Trials
ICH M2 EWG The Electronic Common Techincal Document(eCTD)
FDA Portable Document Format (PDF) Specifications
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
5. Cytel Inc. - Confidential
Introduction to ICH E3
E3 Implemention Working Group Q&A 7 June 2012
It is a guidance not a set of rigid requirements or a
template
Modifications and adaptions that lead better
display and communication of information are
encouraged
Some data in appendices are specific requirements
of individual HA and should be submitted as
appropriate
New sections could be added if appropriate
Repetitions are allowed. E.g. deaths listing vs AE
with fatal outcome
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
5
Geneva Branch
Geneva Branch
6. Cytel Inc. - Confidential
Agenda
Introduction to ICH E3
Key points in ICH E3 referring to statistical outputs
production
ICH E3 Additional Considerations
Technical Solutions
Software requirements overview
In-house solutions
Facilitate the work of the medical writer
Other possible topics for discussion
References
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
6
Geneva Branch
Geneva Branch
7. Cytel Inc. - Confidential
Key points in ICH E3 referring to
statistical outputs production
Obviously the TLFs programmed by biostat department are
the source of information of CSR
In-text tables: statistical outputs inserted in the body of the
CSR, i.e sections 1 to 13 as per ICH E3.
End-text - Section 14: Tables, Figures and Graphs Referred to
but not Included in the text. When the statistical output will be
presented outside the body of the report
Narratives: detailing deaths, other SAE and significant AE in
section 12.3.2
Subject/Patients Data Listings
16.1 Study Information
16.1.6 Listing of patients receiving test drug(s)/investigational
product(s) from specific batches, where more than one batch was
used
16.1.7 Randomisation scheme and codes (patient identification and
treatment assigned)
16.2 Patient Data Listings
16.4 Individual Patient Data Listings
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
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Geneva Branch
8. 8
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Key points in ICH E3 referring to
statistical outputs production
Geneva Branch
Geneva Branch
The guidance gave also some instructions on the required contents
of tables and listings. For example:
12.2.4. Listings of Adverse Events All adverse events for each patient, …..,
should be listed in appendix 16.2.7…the listing should be by investigator and
by treatment….and should include: patient identifier, age, race….the adverse
event (preferred term, reported term) …
12.4.2.2. Laboratory Individual Patient Changes An analysis of invidual patient
changes by treatment should be given e.g. shift tables
Some template for figures, tables and listings are also provided. For example:
Disposition of patients (figure)
Listings of patients who discontinued therapy
Listings of patients and observations excluded from eficacy analysis
Number of patients excluded from the efficacy analysis
The guidance contains also instructions on «expected» statistical
analysis to be taken in consideration for the SAP development (see
also section 16.1.9)
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
9. Cytel Inc. - Confidential
Key points in ICH E3 referring to
statistical outputs production
In-text tables
Merck Serono SDOT Tool
RTF output: a word table that can be easily inserted into the CSR
Include CAPTION for automatic reference once they are inserted in the CSR
Source should be also mentioned (e.g. post-text table/listing)
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
9
Geneva Branch
Geneva Branch
10. Cytel Inc. - Confidential
Key points in ICH E3 referring to
statistical outputs production
Post-text tables
Merck Serono SDOT Tool
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
10
Geneva Branch
Geneva Branch
11. 11
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Key points in ICH E3 referring to
statistical outputs production
Geneva Branch
Geneva Branch
Post-text listings
Alternative solution
can be implemented to
avoid split in several
pages when there are
many information to
report. e.g. adverse
events listing
SAS Proc Report
Proc REPORT Tutorial. C. Zender. WUSS 2010
Beyond the Basic: Advanced REPORT Procedure Tips and Tricks Updated for SAS 9.2. A. McMahill Booth. SAS Global Forum 2011
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
12. 12
Cytel Inc. - Confidential
Key points in ICH E3 referring to
statistical outputs production
Geneva Branch
Geneva Branch
Figures
EMF, EPS, WMF and CGM are
reccomended file formats
Merck Serono Standard Graph Library
Should allow B&W printing without
loosing any information.
Their display should be verified
Merck Serono Standard Graph Library
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
13. 13
Cytel Inc. - Confidential
Key points in ICH E3 referring to
statistical outputs production
Subject Profile
Merck Serono Patient Profile Tool
Extremely useful for medical review but ould be
also provided for the section 16.4
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
Geneva Branch
Geneva Branch
14. Cytel Inc. - Confidential
Key points in ICH E3 referring to
statistical outputs production
Narrative
Generated with JMP® Clinical
Developing a Complete Picture of Patient Safety in Clinical Trials. RC Zink. RD Wolfinger. SESUG 2012
Usually written by the MW, but automation can be implemented
especially for big trials
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
14
Geneva Branch
Geneva Branch
15. Cytel Inc. - Confidential
Key points in ICH E3 referring to
statistical outputs production
As per FDA Portable Document Format (PDF) Specifications – Style
Requirements
US Letter
Margins as recommended by FDA PDF Specification. In
general settings of 1 inch on each side of the page should
be also enough to allow printing on A4 as well
Font sizes ranging from 9 to 12 points
Times New Roman 12-point font is recommended for
narrative text
For tables generally, point sizes 9-10 are recommended for
tables; smaller point sizes should be avoided. Ten point
fonts are recommended for footnotes.
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
15
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Geneva Branch
16. 16
Cytel Inc. - Confidential
Key points in ICH E3 referring to
statistical outputs production
Geneva Branch
Geneva Branch
SAS options/statements for controlling paper size and styles
Paper Size, Orientation and Margins with SAS options
Setting fonts and size by modifying a template
Zoom, Zoom: Get your document to scale on all paper size. D. O’Connor. SAS Global Forum 2010
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
17. Cytel Inc. - Confidential
Key points in ICH E3 referring to
statistical outputs production
SAS options/statements for controlling paper size and styles
ODS Options e.g. the ‘page x of y’ dilemma
It controls special sequence for in-line formatting
(e.g. PDF, RTF, HTML)
The Greatest Hits: ODS Essentials Every User Should Know. C. Zender. NESUG 2011
Advanced RTF Layout with SAS. K. Glab. PhUSE 2007
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
17
Geneva Branch
Geneva Branch
18. 18
Cytel Inc. - Confidential
Key points in ICH E3 referring to
statistical outputs production
Geneva Branch
Geneva Branch
SAS options/statements for controlling paper size and styles
Other ad-hoc style setting within a SAS procedure
e.g. PROC REPORT
Proc REPORT Tutorial. C. Zender. WUSS 2010
Beyond the Basic: Advanced REPORT Procedure Tips and Tricks Updated for SAS 9.2. A. McMahill Booth. SAS Global Forum 2011
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
19. 19
Cytel Inc. - Confidential
Key points in ICH E3 referring to
statistical outputs production
Geneva Branch
Geneva Branch
As per FDA Portable Document Format (PDF) Specifications – Style
Requirements
Black is the recommended font color. Any colors used
should be tested prior to submission by printing
sample pages from the document using a grayscale
printer
Additional rules as per eCTD guidance concerning
File size
File name (e.g. avoid punctuation, underscore, spaces, etc.)
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
20. 20
Cytel Inc. - Confidential
Key points in ICH E3 referring to
statistical outputs production
Geneva Branch
Geneva Branch
Structure / Titles / Numbering for section 14 and 16.x
Standard sections contents/numbering is proposed
A hierarchical structure
Output titles and sub-titles, and their associated bookmarks are
limited to 4 levels as per eCTD guidance.
For example for section 14
14.1 DEMOGRAPHICS DATA
14.2 EFFICACY DATA
14.3 SAFETY DATA
14.3.1 Displays of Adverse Events
14.3.2 Listings o deaths, other SAE and Significant Aes
14.3.3 Narrative Deaths, Other serious……
14.3.4 Abnormal Laboratory Value Listing (Each patient)
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
21. Cytel Inc. - Confidential
Agenda
Introduction to ICH E3
Key points in ICH E3 referring to statistical outputs
production
ICH E3 Additional Considerations
Technical Solutions
Software requirements overview
In-house solutions
Facilitate the work of the medical writer
Other possible topics for discussion
References
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
21
Geneva Branch
Geneva Branch
22. 22
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ICH E3 Additional
Considerations
Geneva Branch
Geneva Branch
Still space for interpretation / individual preferences
e.g. medical writer
Duplication of outputs in section 14 and in-text
16.4 for all trials, 16.4 and Subjects Profiles, 16.4 and SDTM
Duplication of outputs (listings) in section 14 and 16.x, 16.2
and 16.4
Exposure in section 14.3
Concomitant Medications in section 14.1 or 14.3
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
23. Cytel Inc. - Confidential
ICH E3 Additional Considerations
Some reccomendations – We must do it!
Follow the eCTD and FDA PDF Specifications
Paper format including margins setting
Font style and size
Avoid use of coulors
Adhere to key items in E3 structure
14.1 for all demographics / data generated prior to
experimental drug expose
14.2 for efficacy
14.3 for safety including any ‘interventions’ (e.g. exposure)
16.X at least listings explicetely mentioned in the ICH E3
Out of scope of the presentation «non clinical» domains e.g. PK
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
23
Geneva Branch
Geneva Branch
24. Cytel Inc. - Confidential
Agenda
Introduction to ICH E3
Key points in ICH E3 referring to statistical outputs
production
ICH E3 Additional Considerations
Technical Solutions
Software requirements overview
In-house solutions
Facilitate the work of the medical writer
Other possible topics for discussion
References
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
24
Geneva Branch
Geneva Branch
25. 25
Cytel Inc. - Confidential
Techinical Solutions
Geneva Branch
Geneva Branch
Sofware requirements overview
Combine descriptive statistics including p-values for inferential tests
Generates totals and subtotals within specified groups
Full control of the denominator for percentage calculations
Automatic rounding, formatting, and decimal point alignment of results
Manages page changing based on user-defined groupings
Headings span (multiple columns)
Titles and footnote management
Places information from a single record on multiple output lines
Full control of titles and footnotes
Allow creation of styled RTF tables for immediate use in Publishing
software (e.g. WORD)
Table of Contents Generation
Management of template/standard libraries
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
26. Cytel Inc. - Confidential
Techinical Solutions
Sofware requirements overview
SAS
Procedures for output reporting e.g. TABULATE, REPORT,
etc.
Procedures for statistical techniques/methods e.g.
LIFETEST, GLM, etc.
ODS, Proc TEMPLATE, Proc DOCUMENT
Macro
No end-user application, No proc CSR or proc TLF yet
R
Existing library for «R for Clinical Trial Reporting» FE Harrel
(2007)
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
26
Geneva Branch
Geneva Branch
27. Cytel Inc. - Confidential
Techinical Solutions
Sofware requirements overview
Others
Pharmastat APT Analysis Library Toold for Clinical Trials
Report Creation
Dataceutics SAS/IntrNet based platform for Clinical
Reporting
ClinPlus
SAS JMP Clinical
SAS Drug and Device Development and other SAS tools for
Life Science
EntimICE
Oracle Life Science
Still a bit away from the
push_the_bottom_away theory
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
27
Geneva Branch
Geneva Branch
28. Cytel Inc. - Confidential
Agenda
Introduction to ICH E3
Key points in ICH E3 referring to statistical outputs
production
ICH E3 Additional Considerations
Technical Solutions
Software requirements overview
In-house solutions
Facilitate the work of the medical writer
Other possible topics for discussion
References
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
28
Geneva Branch
Geneva Branch
29. 29
Cytel Inc. - Confidential
Technical Solutions
Geneva Branch
Geneva Branch
In-house solutions (at Merck Serono)
Often each organization has its own tools/macro library/process
GBSOS - A Guidance for statistical outputs
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
30. 30
Cytel Inc. - Confidential
Technical Solutions
In-house solutions (at Merck Serono)
Geneva Branch
Geneva Branch
Additional rules / policy for outputs numbering
*
*
*
*
*
*
* Merck Serono addition
16.1.6 Listings of patients receiving test drug(s)/investigational product from specific
batches, where more than one batch was use
16.1.9 (out of scope) SAP or description of key stats items
FDA http://www.fda.gov/ohrms/dockets/ac/09/briefing/2009-4430b1-56%20S01-01US%20Statistical%20Analysis%20Plan.pdf
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
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Cytel Inc. - Confidential
Technical Solutions
Geneva Branch
Geneva Branch
In-house solutions (at Merck Serono)
SDOT - A set of SAS macro to cover standard templates
TABS: Continuos / Categorical Standard Analysis Outputs
AE: Adverse Events and Concomitant Medications
PDF: Ad-hoc outputs
LST2PS: PDF output production with hierarchical bookmarks
Started with excel outputs
.MHTM file
Tried word outputs
PDF preferable solution for section 14 and 16.x
}
Standard SAS .LST file read and transformed to PS rendered to PDF
+ More stable
+ Size of output file
- Less space availabl (monospace font)
- Few styling options
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
32. Cytel Inc. - Confidential
Agenda
Introduction to ICH E3
Key points in ICH E3 referring to statistical outputs
production
ICH E3 Additional Considerations
Technical Solutions
Software requirements overview
In-house solutions
Facilitate the work of the medical writer
Other possible topics for discussion
References
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
32
Geneva Branch
Geneva Branch
33. Cytel Inc. - Confidential
Technical Solutions
33
Geneva Branch
Geneva Branch
Facilitate the work of the medical writer
Provide section 14 and 16.x in PDF format with
bookmarks to facilitate the production of the final CSR
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
34. Cytel Inc. - Confidential
Technical Solutions
Facilitate the work of the medical writer
PDF Bookmark creation – In house solution (at Merck Serono)
Before
Outputs where either generated in
.XLS or RTF
Rendered to PDF
Bookmarks where created manually
by the MW
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
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Geneva Branch
35. Cytel Inc. - Confidential
Technical Solutions
Facilitate the work of the medical writer
PDF Bookmark creation – In house solution (at Merck Serono)
In-house solution (SAS macro)
Standard SAS .LST output
Rules for hierarchical titles
.LST rendered to PDF and hierarchical titles
captured from the .LST
Postscript file with built-in bookmark from
hierarchical titles automaticall rendered to
PDF
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
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Geneva Branch
Geneva Branch
36. Cytel Inc. - Confidential
Technical Solutions
36
Geneva Branch
Geneva Branch
Facilitate the work of the medical writer
PDF Bookmark creation – In house solution (at Merck Serono)
In-house solution (SAS macro)
LST Rules for pagesize and linesize
Example of postscript statements to control bookmarks
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
37. Cytel Inc. - Confidential
Technical Solutions
Facilitate the work of the medical writer
PDF Bookmark creation – Possible solutions with SAS 9.x
Default PDF bookmarked file
ODS PROCLABEL to control standard SAS proc
label (bookmark level 1)
Proc options to control bookmark level 2 e.g.
CONTENTS= in PROC REPORT
DESCRIPTION= in SAS/GRAPH procedures
Some procedures have more than 2 levels e.g. PROC GLM
Control bookmarks through PROC TEMPLATE
Full bookmarks control through PROC DOCUMENT
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
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Geneva Branch
38. Cytel Inc. - Confidential
Technical Solutions
Facilitate the work of the medical writer
PDF Bookmark creation – Possible solutions with SAS 9.x - Example
Create a PDF file with 4 outputs with the following
hierarchical bookmarks:
14.1 DEMOGRAPHICS DATA
14.1.2 Subject Accrual
PROC FREQ
Table 14.1.2.1 ITT Population
14.1.6 Demographics Characteristis
PROC TABULATE
Table 14.1.6.1 ITT Population
Listing 14.1.6.1 Detailed Listing PROC REPORT
14.2 EFFICACY DATA
14.2.1 Primary Endpoint
PROC LOGISTIC
Table 14.2.1.1 ITT Population
with ODS SELECT
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
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Technical Solutions
Facilitate the work of the medical writer
Geneva Branch
Geneva Branch
PDF Bookmark creation – Possible solutions with SAS 9.x - Example
The best result with ODS statements and PROC options
ods PDF file=‘MYFILE.pdf’ style=MyStyle;
ods PROCLABEL='14.1 DEMOGRAPHICS DATA';
proc tabulate data=pts
CONTENTS="14.1.6 Demographics Characteristics";
...
run;
ods PDF close;
Other possible statements controlling bookmarks
generation:
PDFTOC=n
Control the nr. of level to be displayed (ODS option)
NOPTITLE
Suppress standard proc title (ODS option)
/CONTENTS=‘Label’
option of TABLES statement (proc FREQ)
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
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Technical Solutions
Facilitate the work of the medical writer
PDF Bookmark creation – Possible solutions with SAS 9.x - Examples
The best result with ODS statements and PROC options
Bookmarks not controlled through title statement
Hierarchy within PROC
e.g. PROC LOGISTIC
Not easy to control although further improvement are
possible with template control (PROC TEMPLATE)
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
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41. Cytel Inc. - Confidential
Technical Solutions
Facilitate the work of the medical writer
PDF Bookmark creation – Possible solutions with SAS 9.x – The new DOCUMENT concept
SAS prior to v 8
PROC producing «DATA» and defining «STYLE» for only one type of output .LST
SAS v 8
ODS introduced the concept of DATA and STYLE object as OUTPUT object
OUTPUT objects can be not stored
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
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Technical Solutions
Geneva Branch
Geneva Branch
Facilitate the work of the medical writer
PDF Bookmark creation – Possible solutions with SAS 9.x – The new DOCUMENT concept
SAS 9 introduced the concept of «Document»
ODS Output Objects in “raw” form stored in an item
store
Stored as hierarchical files
Transform report without rerunning the analysis or
repeating the database query by modifying and
replaying an item store
Control the report structure
Absolute control over Table of Contents (e.g. PDF
bookmarks)
ODS DOCUMENT, PROC DOCUMENT, ODSDOCUMENT
WINDOW
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
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Technical Solutions
Geneva Branch
Geneva Branch
Facilitate the work of the medical writer
PDF Bookmark creation – Possible solutions with SAS 9.x – The new DOCUMENT concept
ODS DOCUMENT NAME=TLF(WRITE);
<SAS Proc Statement generating outputs>
ODS DOCUMENT CLOSE;
proc document name=TLF;
list / levels =all;
run;quit;
PROC FREQ Output
PROC TABULATE Output
PROC REPORT Output
PROC LOGISTIC Output
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
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Technical Solutions
Facilitate the work of the medical writer
PDF Bookmark creation – Possible solutions with SAS 9.x – The new DOCUMENT concept
An interactive environment to
modify the document
Adding a node
Modifying a node
Rename a node
Move a node
Same actions for a table
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
Geneva Branch
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Technical Solutions
Geneva Branch
Geneva Branch
Facilitate the work of the medical writer
PDF Bookmark creation – Possible solutions with SAS 9.x – The new DOCUMENT concept
Document modified with
ODSDOCUMENT point and
click tool
PDF recreated
ods pdf file="<my file>"
style=MYSTYLE;
proc document name=TLF;
replay ;
run;
ods pdf close;
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
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Technical Solutions
Geneva Branch
Geneva Branch
Facilitate the work of the medical writer
PDF Bookmark creation – Possible solutions with SAS 9.x – The new DOCUMENT concept
The SAS code generated by the «Document Recorder» facility
proc document name=MyDoc.TLF(UPDATE);
/*Move outputs to correct section/level and change the
title*/
SETLABEL Freq#1Table1#1 '14.1.2 Subject Accrual';
DIR Freq#1Table1#1;
SETLABEL CrossTabFreqs#1 'Table 14.1.2.1 Subject Accrual
- ITT Population';
COPY Tabulate#1Report#1 TO Freq#1Report#1;
……
<continue>
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
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Technical Solutions
Facilitate the work of the medical writer
PDF Bookmark creation – Possible solutions with SAS 9.x – The new DOCUMENT concept
The SAS code generated by the «Document Recorder» facility
….
/* Create the missing level 2 for section 14.2 */
DIR Logistic#1;
MAKE Sub14_2_1;
SETLABEL Sub14_2_1 '14.2.1 Primary Endpoint';
COPY ParameterEstimates#1 TO
Sub14_2_1#1ParameterEstimates#1;
…..
quit;
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
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48. Cytel Inc. - Confidential
Technical Solutions
Facilitate the work of the medical writer
PDF Bookmark creation – Possible solutions with SAS 9.x – The new DOCUMENT concept
ODS DOCUMENT from scratch. KD Smith SAS Global Forum 2012
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
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Geneva Branch
Geneva Branch
49. Cytel Inc. - Confidential
Technical Solutions
Facilitate the work of the medical writer
PDF Bookmark creation – Possible solutions with SAS 9.x – The new DOCUMENT concept
ODS DOCUMENT from scratch. KD Smith SAS Global Forum 2012
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
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Agenda
Introduction to ICH E3
Key points in ICH E3 referring to statistical outputs
production
ICH E3 Additional Considerations
Technical Solutions
Software requirements overview
In-house solutions
Facilitate the work of the medical writer
Other possible topics for discussion
References
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
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Geneva Branch
51. Cytel Inc. - Confidential
Other possible topics for discussion
related to statistical outputs production
PhUSE/FDA Working Group (see Wiki Page for
Development of Standard Scripts for Analysis and
Programming)
Layout examples in ADaM AE, TTE and ADaM
examples in commonly used statistical analysis
methods
Analysis Results Metadata
Traceability
Validation / Quality Control
Documentation / Procedures / Templates
ADaM not covered but is should be considered as a
statitical output
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
51
Geneva Branch
Geneva Branch
52. Cytel Inc. - Confidential
Agenda
Introduction to ICH E3
Key points in ICH E3 referring to statistical outputs
production
ICH E3 Additional Considerations
Technical Solutions
Software requirements overview
In-house solutions
Facilitate the work of the medical writer
Other possible topics for discussion
References
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
52
Geneva Branch
Geneva Branch
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References
Geneva Branch
Geneva Branch
Regulatory
ICH E3. Structure and Content of Clinical Study Report
ICH E3. Questions and Answers. 7 June 2012
ICH E9. Statistical Principles for Clinical Trials
ICH M2 EWG. The Electronic Common Technical Document (eCTD)
FDA Portable Document (PDF) Specifications
Preparing Clinical Study Reports for eCTD. J Aitken. DIA 7th Annual Electronic Submission
Conference 2008
SAS and ODS in General
The Greatest Hits: ODS Essentials Every User Should Know. C. Zender. NESUG 2011
A SAS Output Delivery System Menu for All Appetites and Applications. C. Parker. SAS Global
Forum 2009
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
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References
Geneva Branch
Geneva Branch
SAS and Other Useful Hints for Outputs Productions
Developing a Complete Picture of Patient Safety in Clinical Trials. RC Zink. RD
Wolfinger. SESUG 2012
Supplementing Programmed Assisted Patient Narrative (PANs) with Graphs Using
SAS. F. Yeh M. Munsaka. PharmaSUG 2012
Advanced RTF Layout with SAS. K. Glab. PhUSE 2007
Proc REPORT Tutorial. C. Zender. WUSS 2010
Beyond the Basic: Advanced REPORT Procedure Tips and Tricks Updated for SAS 9.2.
A. McMahill Booth. SAS Global Forum 2011
RTF Tagset for Use in Clinical Reporting. M. Andersen. PhUSE SDE 2012
Zoom, Zoom: Get your document to scale on all paper size. D. O’Connor. SAS Global
Forum 2010
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
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References
Geneva Branch
Geneva Branch
Generating figures
Superior gRaphics in Statistical Reports. S. Bamnote. PhUSE 2012
Using SAS GTL to visualize your data when there is too much of it to
visualize. P. Watts N. Derby. SAS Global Forum 2012
Incorporating graphics into Summary Report Tables using ODS and GTL. Q.
Chen. PharmaSUG 2011
Tips and tricks for clinical graphics using ODS Graphics. S. Matange. SAS
Global Forum 2011
Clinical Trial Reporting with SAS ODS Graphics. HR. Pauli DJ. Garbutt.
PhUSE 2009
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
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References
Geneva Branch
Geneva Branch
SAS and Bookmakrs / Table of Contents Generation
Creating a Customized Table of Contents in ODS RTF Documents. E. Small.
NESUG 2006
Let’s Give’Em Something to TOC about: Transforming the Table of Contents
of your PDF file. B. Lawhorn. SAS Global Forum 2011
Using Proc DOCUMENT to Modify PDF Bookmakrs Generated by Proc FREQ.
SM. Dorinski. NESUG 2008
ODS DOCUMENT from scratch. KD Smith SAS Global Forum 2012
Creating Define.PDF with SAS Version 9.3 ODS RTF. E. Li C. Chesbrough.
PharmaSUG 2012
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
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Aknowledgments
Geneva Branch
Geneva Branch
Martin Gregory and Manuel Cornes – Merck Serono – Darmstadt
(DE)
JianJian Wang – Cytel Inc – Cambridge (US), for providing
examples on RTF output
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013
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Questions
Geneva Branch
Geneva Branch
New Geneva offices – November 2012
Clinical Study Report - In-text tables, Tables Figures and Graphs, Patient and Individual Patient Data Listings: ICH E3 technical requisites and possible solution in SAS – A. Tinazzi – Seminario BIAS – Milano 22/02/2013