The document summarizes a systematic review of publications about the implementation of the ADaM model. Over 100 papers were identified that discussed ADaM implementation, with the majority coming from CRO authors. Several areas of interpretation in the ADaM guidelines were identified from the literature, including how to classify parameters in BDS, derive rows versus columns, and determine what constitutes an "analysis-ready" dataset. The review concluded that feedback from users would help the CDISC team further develop and clarify the ADaM guidelines.
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.
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.
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 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).
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.
According to FDA Draft Guidance for Industry in Electronic Submission and Study Data Technical Conformance Guide, the pharmaceutical companies will need to provide CDISC Electronic submission to FDA. The paper will explain Data Standard Catalog which will dictate FDA Standards. The paper will discuss how to prepare CDISC electronic submission and what to prepare in CDISC electronic submission.
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
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 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).
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.
According to FDA Draft Guidance for Industry in Electronic Submission and Study Data Technical Conformance Guide, the pharmaceutical companies will need to provide CDISC Electronic submission to FDA. The paper will explain Data Standard Catalog which will dictate FDA Standards. The paper will discuss how to prepare CDISC electronic submission and what to prepare in CDISC electronic submission.
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
The paper is intended for clinical trial SAS® programmers who create graphs using ADaM (Analysis Data Model) datasets. ADaM datasets are analysis-ready, so programmers need to apply CDISC principles and guidelines to create them, while at the same time being mindful of their intent as plotting datasets. The paper will discuss the basic principles of graph creation using ADaM datasets.
We propose the use of PARAM, AVAL, AVISIT and AVISITN for plotting. The paper will show in an example how to use AVAL for the y-axis and AVISITN for the x-axis for simple plotting. We will discuss the role of AVISITN as a numeric representation of AVISIT and propose the use of AVISITN for plotting purposes.
We will also discuss some of the graphs created by features of SAS/STAT, ODS GRAPH and Graph Template Language (GTL). This will eliminate some of the intermediate SAS datasets that SAS programmers need to create for graph creations, which means that the graphs can be created directly from ADaM without the intermediate datasets. Examples using SAS/STAT, ODS GRAPH and GTL will be provided.
Lean is more effective when using Simulation, an ED Case Study from SIMUL8SIMUL8 Corporation
Lean Six Sigma (LSS) programs are more effective when they integrate the use of process simulation into their toolkit.
By doing so, improvement practitioners improve both their technical solutions and adoption of new/redesigned processes.
This Case Study looks at 'Implementing Rapid Clinical Examination (RCE) flow in the Emergency Room of Memorial Medical Centre.
Best practices for implementing and maintaining successful standardsVeeva Systems
Watch the video here: https://bit.ly/3uvar1u
This webinar provides best practices, check-lists and case studies for leveraging standards in clinical trials. From creation and implementation, to governance tools (both internal and with external partners), attendees walk away with actionable insights to leverage with their own organization.
* Understand what to standardize
* Learn several approaches to standards development and when they make sense
* Ensure alignment with key stakeholders
* Maintain and govern standards over time
* Reduce overall configuration time
Who Will Benefit:
* Clinical Data (manager/director/head of) Clinical ops
* Data management
* Biostatistics
* Data science
* Clinical science
* EDC
* Biometrics
* eClinical
* Data standards
* Quantitative sciences
* Informatics
* Data monitoring
* Clinical leads
* Study managers
* Clinical study
* Data manager
* CRA
* CDISC
Meet Your Presenters:
Carla Reis
Director, Client Services, 4G Clinical
Carla Reis, Director of Client Services at 4G Clinical, has over 18 years of experience as an operational leader in developing and implementing RTSM systems in a global pharmaceutical company. Carla was a leader in her organization in establishing vendor management standards and processes. She has helped lead major RTSM process improvement initiatives where she established new and innovated approaches to drug assignment verification and vendor integrations. Carla has presented at industry conferences as a subject matter expert on best practices using RTSM solutions for complex strategies in supply chain management. Carla holds a BS in Neurobiology and Physiology from the University of Connecticut and a certification as Lean Six Sigma Yellow Belt. Carla also holds a Masters in Science in Health Administration with a concentration in Health Informatics from Saint Joseph's University.
Paul MacDonald
Senior Director, Strategy Vault CDMS, Veeva Systems
Paul is Senior Director Vault CDMS, responsible for strategy and direction in data management. With 25+ years experience working in life science at pharma, CRO and technology organisations, Paul brings a strong operational focus in relation to eClinical technology for data management and clinical operations that stretches from EDC, through CTMS to risk based monitoring.
2011-2012 Cloud Assessment Tool (CAT) White Paperaccacloud
The Cloud Assessment Tool (CAT) was developed by the Asia Cloud Computing Association (ACCA). It was refined through extensive and in-depth discussions over a period of 2 years between members of the WG and by looking at relevant cloud and IT specifications.
The CAT defines the requirements placed on IaaS/PaaS solution providers to support stringent cloud applications. However, that perspective was subsequently extended to cover all application requirements. As such, its final realization has broad applicability.
For more information, visit http://www.asiacloudcomputing.org
1010 guide is essentially a flowchart to help software managers choose an efficient software project management methodology based on the metrics they have. Differentiation between critical and non-critical projects which is followed by choosing the pre-defined metrics for the team. Finally a table corresponds to the options chosen and results in a straight forward selection of the appropriate SDLC based on the values given.
Similar to A Systematic Review of ADaM IG Interpretation (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.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
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