PharmaSUG Single Day Event
N.C. Biotechnology Center, Research Triangle Park, NC
October 25, 2018
Sharing Solutions of e-Submissions: Making it Happen, Making it Matter

The PharmaSUG 2018 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 in Philadelphia in June 2019!

Sponsored by:
Tibco PRA Rho CSG GreenKey OntoForce AdvancedRecruiting


Presentation (click for abstract)Presenter (click for bio)Slides
Advanced Visualization Using TIBCO Spotfire and SAS Using SDTM DataAjay Gupta, PPDSlides (PDF, 2.75MB)
Simplify and Streamline Using PythonMichael Stackhouse, CovanceSlides (PDF, 405KB)
Streamlining the Metadata Management Process Using SAS Life Science Analytics FrameworkAlex Ford, SASSlides (PDF, 1.52KB)
ADaM 2018: What's New and What's ComingJack Shostak, DCRI and Sandra Minjoe, PRASlides (PDF, 838KB)
ADaM Structures for Integration: A PreviewWayne Zhong, Accretion SoftworksSlides (PDF, 807KB), Paper (PDF, 434KB)
A Framework for Implementing [Conflicting] FDA GuidanceTodd Case, Vertex PharmaceuticalsSlides (PDF, 1.80MB)
Preparing ADaM Datasets and Related Files for FDA SubmissionRagini Hari and Sandra Minjoe, PRASlides (PDF, 618KB)
Define XML Expectations from Various Clients, Tools and Industry GroupsGustav Bernard and Zach Dorman, IQVIASlides (PDF, 1.39MB)
CDISC Standards: Evolving to Meet Submission NeedsDiane Wold, CDISCSlides (PDF, 490KB)
Study Data Topics at FDA/CDERSara Jimenez, FDA/CDER

Thanks to our Co-Chairs, Margaret Hung and Matt Becker!

Presentation Abstracts

Advanced Visualization Using TIBCO Spotfire and SAS Using SDTM Data
Ajay Gupta, PPD

In Pharmaceuticals/CRO industries, you may receive requests from stakeholders for quick access to clinical data to explore the data interactively and to gain a deeper understanding. TIBCO Spotfire is an analytics and business intelligence platform which enables data visualization in an interactive mode. Users can further integrate TIBCO Spotfire with SAS (used for data programming) and create visualizations with powerful functionality e.g. data filters, data flags. These visualizations can help the user to self-review the data in multiple ways and will save a significant amount of time. This paper will demonstrate some advanced visualization from Preclarus Patient Data Dashboard (Preclarus PDD) within PPD created using TIBCO Spotfire and SAS (for the SDTM database) and share our experiences and challenges while creating this visualization.

Simplify and Streamline Using Python
Michael Stackhouse, Covance

A large deal of work goes into preparing for a submission, including the work beyond the analysis. Preparing and maintaining documentation can be tedious, and keeping track of updates can become difficult as deadlines approach. This presentation will explore how Python can simplify and streamline some of these tasks. Topics will include identifying changes in specifications, automating a hyperlinked program table of contents, de-macrotizing SAS programs, and more.

Streamlining the Metadata Management Process Using SAS Life Science Analytics Framework
Alex Ford, SAS

It was not long into my clinical programming career before I discovered that CDISC is truly an acronym for “Can Do It Somewhat Correctly”. Each run of a validation report uncovered new warnings or errors followed by tracking down the source of those issues to log and report for a define.xml. The latest release of the SAS Life Science Analytics Framework (LSAF) provides a centralized framework where standards can be imported and live alongside a study and its data, managed by a graphical user interface. By associating a data standard, controlled terminology, and dictionaries with a study, team leads have the data and information necessary to produce a define.xml at the click of a button. Join us as we explore the metadata management features available in LSAF 5.1 which enable programmers of all levels to manage data standards correctly the first time, saving studies both time and money.

ADaM 2018: What's New and What's Coming
Jack Shostak, Duke Clinical Research Institute and Sandra Minjoe, PRA

ADaM has sub-teams working on documents for public review and finalization within 2018. These include:
  • ADaMIG v1.2
  • ADaM Structures for Integration v1.0
  • ADaMIG for Medical Devices v1.0
  • ADaM OCCDS v1.1
  • ADaM Traceability Examples v1.0
  • ADaM BDS for Non-Compartmental (PK) Analysis v1.0
  • ADaM Structures for Oncology v1.0
  • IACET-approved ADaM Training Courses

ADaM Structures for Integration: A Preview
Wayne Zhong, Accretion Softworks

Integration and analysis of data across all studies in a submission is a vital part of applications for regulatory approval in the pharma industry. The existing ADaM classes (ADSL, BDS, and OCCDS) already support some simple cases of integration analysis. However, there has been a need for an integration standard that supports the more complex cases. To address this need, the ADaM Integration sub-team is developing the upcoming ADaM Integration standards document. This paper introduces the new IADSL, IBDS, and IOCCDS classes found in this document. IADSL allows for multiple records per subject. IBDS and IOCCDS work effectively with the new IADSL class. This paper also discusses the analysis needs that necessitated the creation of the new classes, and provides examples in the form of usage scenarios, data, and metadata. With them, no future integration will prove too complex. PharmaSUG 2018 Best Paper Winner

A Framework for Implementing [Conflicting] FDA Guidance
Todd Case, Vertex Pharmaceuticals

On July 21, 2004 the US Food and Drug Administration (FDA) announced a format, called the Study Data Tabulation Model (SDTM), that sponsors can use to submit data to the agency. Twelve years later (on December 17, 2016) the FDA began enforcing the requirement of standardized electronic data submissions in SDTM format, and now, in addition to SDTM, there are multiple sources (and versions) of data standards which impact data supporting applications to the FDA: the FDA Data Standards Catalog (primary list and source of standards) AND the Study Data Standardization Plan, the SDTM model (Version 1.4), the SDTM Implementation Guide (SDTMIG – Version 3.2), the Analysis Data Model (ADaM) - Version 2.1, the ADaM Implementation Guide (Version 1.1), the FDA Guidance for Industry (April, 2017), the Study Data Technical Conformance Guide (October, 2017) and the Prescription Drug User Fee Act (PDUFA), Version V for Fiscal Years 2013-2017 and VI for Fiscal Years 2018 – 2022. At times, these documents, guidances and laws can be contradictory, and it’s up to the Sponsor (when appropriate) to engage with the FDA to determine which 'standard’ (of the standards) to adapt, which version(s) to use, and when to update versions. PharmaSUG 2018 Best Paper Winner

Preparing ADaM Datasets and Related Files for FDA Submission
Ragini Hari and Sandra Minjoe, PRA

This presentation compiles information from documents produced by the U.S. Food and Drug Administration (FDA), the Clinical Data Interchange Standards Consortium (CDISC), and Computational Sciences Symposium (CSS) workgroups to identify what analysis data and other documentation is to be included in submissions and where it all needs to go. The paper not only describes requirements, but also includes recommendations for things that aren't so cut-and-dried. It applies to New Drug Application (NDA) submissions and the subset of Biologic License Application (BLA) submissions that are described in specific FDA binding guidance documents plus other related FDA documents.

Define XML Expectations from Various Clients, Tools and Industry Groups
Gustav Bernard and Zach Dorman, IQVIA

Define-XML 2.0 has been around for a while and yet we are still struggling with these, due to lack of firm instructions and expectations. During this talk, we would like to discuss different expectations from different pharma companies, what is expected by CDISC and what information is provided by CDISC. We will also present some of the P21 findings that are not in agreement with CDISC Standards, and some that cause confusion, and talk about what we feel is expected by the FDA.

CDISC Standards: Evolving to Meet Submission Needs
Diane Wold, CDISC

Although CDISC teams have sometimes organized pilots that involve submissions to FDA (SDTMl-ADaM Pilot project, SEND Fit-for-Use Pilot), for the most part CDISC is not involved in creating and submitting e-submissions. So how does CDISC contribute to solutions that make e-Submissions happen and make them matter? For the most part, CDISC contributes by developing standards, including implementation guides, rules, and controlled terminology, and improving access to the standards, as with the SHARE exports and API. The CDISC standards evolve to cover more content, to provide more detail, clarify ambiguities, and to fix problems. As CDISC standards have evolved they have proliferated.

Study Data Topics at FDA/CDER
Sara Jimenez, FDA/CDER

A sponsor’s goal should be to collect and submit study data with integrity and the highest possible quality. This presentation will build upon this event’s e-submission solutions theme and focus on the following study data topics at CDER: review issues related to data quality and format, technical rejection criteria for study data, and logically skipped instrument items.

Presenter Biographies

Ajay Gupta

Ajay is a Programming Technical Manager at PPD. He received his master’s degree in Biomedical Engineering from Louisiana Tech University in 2006. Since 2010, he has been a regular presenter at SAS conferences, especially PharmaSUG. He has also been a member of the PharmaSUG conference committee for the past two years, and is interested in topics related to Spotfire, SDTM, Pinnacle21, Visual Basic for Applications, SAS Grid and SAS Application development.

Michael Stackhouse

Michael holds a bachelor’s degree from Arcadia University where he studied Business Administration, Economics, and Statistics. He is currently a MIDS student at UC Berkeley School of Information, studying Data Science. Mr. Stackhouse has extensive CDISC experience, working with both SDTM and ADaM standards, as well as serving as a subject matter expert for Define.xml. Mr. Stackhouse’s favorite career projects include macro and utility development, as well as molding the minds of young programmer Padawans into the programming Jedis of the future. Mr. Stackhouse is currently a Manager of Statistical Programming at Covance, and father to three young, furry, four-legged children.

Alex Ford

Alex is a Pre-Sales Solutions Architect with the SAS Health and Life Sciences team. Prior to joining SAS, Alex spent 3+ years at Quintiles leading statistical programming teams representing some of the largest pharmaceutical companies across the globe. Alex holds a BSPH degree from the UNC Gillings School of Global Public Health focused in Biostatistics, and a second degree in Economics. As a Solutions Architect, he enjoys solving problems and helping business customers derive value from an integrated product suite.

Jack Shostak

Jack is an associate director for statistics at the Duke Clinical Research Institute (DCRI), where he is responsible for managing a team of programmers while consulting on regulatory and standards issues. He has published 3 books through SAS Institute on programming, statistics and CDISC, and occasionally writes papers. He has worked with ADaM for over 15 years, served on the ADaM leadership team, and is a CDISC ADaM teacher for industry and the FDA.

Sandra Minjoe

Sandra is a Senior Principal Clinical Data Standards Consultant at PRA Health Sciences. She has been part of the CDISC ADaM team since 2001, has led and participated in many ADaM sub-teams, and is an ADaM trainer for CDISC. As the current ADaM Team lead, Sandra represents the ADaM team on the CDISC Global Governance Group (GGG) and Technical Leadership Committee (TLC).

Wayne Zhong

Wayne has worked with SAS for 8 years in the Pharmaceutical Industry, creating submission packages for regulatory agencies and developing tools for programming departments. He is also an active member of the CDISC ADaM team, currently leading the ADaM Traceability sub-team and contributing to the ADaM Integration, ADaM Compliance, and Type 1 Diabetes sub-teams.

Todd Case

Todd has worked in roles of varied and increased responsibility in the biotech/pharmaceutical industries for over 17 years. Won team awards for leading and managing Statistical Programming teams to multiple successful FDA (NDA/BLA), EMA (EU), PMDA (Japan) and Rest of World (ROW) filings. Currently leading Data Standards, Strategic Outsourcing and Innovation teams within Biometrics. Previously led Therapeutic Area and Resourcing teams. Initiated and led Team and Departmental Meetings, x-Company Meetings and participated in PhUSE and other Industry Working Groups. Requested presenter and panelist as well as author of numerous papers and presentations at conferences in the US and internationally, including PhUSE, PhUSE SDE, PharmaSUG, PharmaSUG China, PharmaSUG SDE, NESUG, SAS Global Forum, JSM (Joint Statistical Meetings) and the Women's Innovation Network.

Ragini Hari

Ragini is currently working as an Associate Director of Statistical Programming at PRA Health Sciences. She comes with 13+ years of industry experience with both pharma and CRO flavors and has led teams and projects with emphasis on NDA submissions and initiatives. She has volunteered and worked with the CDISC team on the SDTM 3.2 portfolio version, along with specific domains through various SDS teams, and has extensive experience working with and reviewing SDTM, ADaM and define xml packages. She lives in Cary, NC with her husband and her 8-year-old and 4-year-old daughters. Outside of work, she loves to hike, work on community service projects and empower South Asian women to connect and network to build each other through her 8600+-member online group, RTP Desi Moms.

Gustav Bernard

Gustav is an Associate Director at IQVIA who has been with the company for 14 years. His work focuses on the implementation of CDISC Standards (SDTM, ADaM and Define-XML) within the IQVIA Global Biostatistics department. He is currently working on creating an ADaM specification creation/automation tool. He also created the Define-XML 2.0 automation process within IQVIA. Gustav earned a Bachelor of Business in Computer Science from the University of the Orange Free State in South Africa.

Zach Dorman

Zach is a Senior Statistical Programmer at IQVIA. He has been in the CRO industry for 5 years, has been a programming team lead on a number of studies for various clients, and has been involved in internal programming initiatives relating to CDISC and Define standards. He attended the University of Reading, United Kingdom for his BSc degree in Applied Statistics.

Diane Wold

Diane received her Ph.D. in Statistics from the University of North Carolina at Chapel Hill. She worked for Burroughs Wellcome/Glaxo Wellcome/Glaxo Smith Kline in a variety of roles for over 30 years. At the Glaxo Smith Kline merger, she joined the data standards group, and in 2002 she joined the CDISC SDS team. She was also involved in other CDISC teams, including the Protocol Representation Group and SHARE. In 2012 she became involved in the CFAST initiative to develop therapeutic area standards. In 2015 she joined CDISC as an employee.

Sara Jimenez

Sara Jimenez is a mathematical statistician supporting the Division of Gastroenterology and Inborn Errors Products at the FDA’s Center for Drug Evaluation and Research. She performs regulatory reviews of clinical trial submissions and contributes to data standards efforts within the division and with external groups. She has worked for several years in the clinical trials industry as a biostatistician and as a statistical programmer. Sara studied mathematics at the University of Texas at Austin and biostatistics the University of Texas Health Science Center at Houston School of Public Health. Her dissertation examined the effects of treatment switching with randomization as an instrumental variable in a randomized controlled trial.