The PharmaSUG 2016 RTP Single-Day Event has now concluded. The slides are available for download below. Thanks to the North Carolina Biotechnology Center for hosting the event, and to all who presented and attended. Don't forget that all paid registrants will receive a $75 discount for our annual conference coming in Baltimore in May 2017!
|Presentation (click for abstract)||Presenter (click for bio)||Slides|
|SEND (The Standard for the Exchange of Nonclinical Data): History, Basics, and Comparisons with Clinical Data||Fred Wood||Slides (PDF, 251KB)|
|Associated Persons Domains – Who? What? Where? When? Why? How?||Michael Stackhouse||Slides (PDF, 2.99MB)|
|The Protocol Representation Model: The Forgotten CDISC Model||Jeffrey Abolafia||Slides (PDF, 1.39MB)|
|CDISC: Interoperability and Beyond…||David Butler||Slides (PDF, 2.93MB)|
|My Attempt to Rid the Clinical World of Excel||Mike Molter||Slides (PDF, 2.31MB)|
|ADaM Grouping: Groups, Categories, and Criteria. Which Way Should I Go?||Jack Shostak||Slides (PDF, 715KB)|
Presentation AbstractsSEND (The Standard for the Exchange of Nonclinical Data): History, Basics, and Comparisons with Clinical Data
Fred Wood, Senior Manager and Lead, Data Standards Consulting Group, Accenture
The CDISC Standard for the Exchange of Nonclinical Data (SEND) Implementation Guide (SENDIG) contains domains for general toxicology and pharmacology, and carcinogenicity, studies. A separate implementation guide (SEND-DART) contains domains for reproductive toxicology studies. The first SEND Model was developed in 2002, utilizing domains described in the CDER 1999 Guidance. In 2007, an effort began to completely align the SENDIG with the SDTM Implementation Guide (SDTMIG), with the first such version (v3.0) published in 2011. Since that time, the SEND Team has been working add more examples, clarify existing text and examples, add new domains. Version 3.1, which actually underwent two public reviews (2014 and 2015), is expected to be posted in Q2 of this year. This paper will provide an overview of the history of SEND and its close ties with the development of the SDTM and the SDTMIG. It will also cover some of the basics of the SEND model and how the nonclinical implementation of the SDTM compares with the clinical implementation.
Associated Persons Domains – Who? What? Where? When? Why? How?
Michael Stackhouse, Assistant Manager, Statistical Programming, Chiltern
Many types of clinical studies collect information on people other than the person in the study. Family medical history – MH or a custom domain? What about organ donor data? Care-giver information? Until now we have there was no home in the CDISC SDTM structure to standardize this information. With the publication of the Associated Persons SDTM IG this standardization has arrived! However, since it is so new, many don’t know that it exists and/or how to apply it. This presentation will explore the Associated Persons SDTM Implementation Guide by exploring six investigational questions to make the application of the AP SDTM IG very logical and even easy. During the presentation we will discuss “What” the Associated Person IG is and for “Who” are these domains intended? “When” do these domains apply in a study and “Where” do I put all the information? One of the biggest questions that will be discussed is “How” to apply the information from the IG. Finally, we will discuss “Why” these domains are a necessary addition and why they should be applied to all studies wherever they are applicable.
The Protocol Representation Model: The Forgotten CDISC Model
Jeffrey Abolafia, Chief Strategist, Data Standards, Rho, Inc.
Recent FDA guidances have made CDISC models the standard for submissions. When implemented properly, CDISC standards have tremendous potential for significant cost savings throughout the project life cycle. However, when implemented poorly, producing CDISC deliverables can actually increase the time and cost of drug development. Most organizations have implemented standards with the goal of getting the FDA what they want, focusing predominantly on SDTM and ADaM and to a lesser extent on CDASH. As a result, the time and cost of producing submission deliverables has increased. In order to gain the maximum value from CDISC standards, implementation can be extended all the way upstream to protocol development. To that end, the CDISC Protocol Representation Model (PRM), which has been largely ignored, has the potential to streamline clinical research throughout the entire product development life cycle.
This paper is an overview of PRM. It describes what PRM is; discusses the value and business case for implementing PRM; discusses how Rho has implemented and extended the PRM; presents use cases of how Rho has used the RPM throughout the life cycle of product development to improve clinical research operations; and describes strategies for collecting and storing the information contained in PRM. The paper should give the reader an appreciation of both the content and scope of the PRM and its usage beyond storing protocol related concepts as metadata.
CDISC: Interoperability and Beyond….
David Butler, Principal Consultant and Life Science Consulting Lead, Teradata
Data is a strategic asset in any organization. Value-add strategic data analysis, as well as CDISC-compliant data capture and exchange, are vital to a strong research and development initiative. Come learn how data from across many systems can be efficiently gathered, stored and shared in a manner that is both standards compliant and minimizes efforts spent gathering and transforming data, thus allowing more time for strategic, tactical and operational analytics. In addition, we will share some of the common best practices and use cases in the pharmaceutical industry.
My Attempt to Rid the Clinical World of Excel
Mike Molter, Director of Statistical Programming and Technology, Wright Avenue
As the CDISC standards continue to grow, mature, and even stabilize, several aspects of clinical trial data standardization remain a mystery. While more data management and statistical programming personnel are grasping the content, we’re still struggling with how to implement it. In spite of the availability of standards in more machine-readable formats from the CDISC SHARE team, companies involved in the production of clinical trial artifacts such as tabulation and analysis data sets, tables, listings, figures, and define.xml are still resorting to the use of Excel and SAS to manage programming specifications and metadata. Lab information such as normal range definitions, toxicity grade calculations, and unit conversions are maintained in SAS data sets or macros stored somewhere on network drives. In this presentation we open the box to explore alternatives outside of Excel and SAS. I will begin with a demonstration of a proof-of-concept web-based tool that accepts mapping specification and metadata input from a user and stores it in what is called a “graph database”. After the demonstration, we’ll take a peek inside the graph database, and we’ll see how to use other scripting languages and HTML forms to interact with it.
ADaM Grouping: Groups, Categories, and Criteria. Which Way Should I Go?
Jack Shostak, Associate Director of Statistics, Duke Clinical Research Institute (DCRI)
ADaM has variables that allow you to cluster, group, or categorize information for analysis purposes. Sometimes it isn't entirely clear which variable you should be using. The goal of this talk is to help to provide some guidance around what ADaM grouping variables are available, what is appropriate when, and then to discuss when more than one technique will work for a given analysis situation. The talk focus will be primarily on BDS grouping variables, although others will be mentioned. The following ADaM variables will be examined in detail: *GRy(*Gy), *CATy, CRITy, and MCRITy. These will be compared and contrasted for various analysis situations.
Presenter BiographiesJeffrey Abolafia
Jeff Abolafia is currently Chief Strategist of Data Standards at Rho, Incorporated. Previously Jeff was a member of the faculty in the Department of Biostatistics at the University of North Carolina. Jeff has been involved with public health research and data standards for over twenty five years and is a frequent contributor and presenter at PhUSE,PharmaSUG, SAS Global Forum, and CDISC conferences. Jeff co-founded the RTP CDISC User's Group and is a member of the CDISC ADaM and ADaM Metadata teams. His areas of interest include data standards, submissions, statistical computing, and bioinformatics.
Dr. Butler is Senior Business Consultant II for Teradata Healthcare. He brings a successful history of accomplishments and work experiences that span multiple fields, including clinical practice, database management systems, academia, research and business. He has worked in community, hospital, clinical, managed care, e-commerce pharmacy and drug information. His role is to help create solutions for healthcare companies and organizations utilizing an integrated information architecture built to better apply actionable, usable business intelligence for strategic initiatives and operational plans improving patient care, outcomes, operations, logistics and financials.
Mike Molter is the Director of Statistical Programming and Technology with Wright Avenue Partners in Cary. At Wright Avenue, Mike is responsible for providing statistical programming support, as well as placing and managing Wright Avenue resources inside pharmaceutical, biotech, and CRO clients. He also oversees the management and development of new technologies aimed at streamlining the flow of clinical trial metadata through an organization. Mike has been involved in SAS programming since 1999, in clinical trials since 2003, and in industry data standards since 2005. His professional interests are centered around the use of cutting edge technologies to optimize the use of clinical trial metadata throughout the lifecycle of a clinical trial. Personal interests include cycling, swimming, reading, and Michigan football and basketball.
Jack Shostak manages a group of statistical programmers and is an Associate Director of Statistics at the Duke Clinical Research Institute. He is the author of the book SAS Programming in the Pharmaceutical Industry, and coauthor of the books Common Statistical Methods for Clinical Research with SAS Examples, Third Edition as well as Implementing CDISC Using SAS: An End-to-End Guide. Jack has been active in CDISC since 2002 primarily as a contributor to ADaM model development. Jack serves as a member of the ADaM leadership team, and has been a CDISC ADaM instructor for industry and the FDA for many years.
Michael Stackhouse graduated from Arcadia University with a Bachelor’s degree in Business Administration with a concentration in Economics and a minor in statistics. Michael has CDISC experience in both SDTM and ADaM standards and creation of Define.xml. Past therapeutic areas of experience for Michael include Asthma, COPD, psychiatric disorders, oncology and rare disease. Michael is an Assistant Manager at Chiltern.
Fred Wood leads the Data Standards Consulting Group for Accenture Accelerated R&D Services. He is one of the principal contributors to the SDTM and created the first SEND domains in 2002. Fred is a founding member of the CDISC SDS, SEND, and Devices Teams, and has led or co-led these for many years; he currently serves on the Leadership Teams of all three. Fred is a member of the CDISC Standards Review Council, the SDTM Governance Committee, and the Technical Leadership Committee. He has been on many other CDISC teams since 1999.