Enhance your PharmaSUG experience by attending optional pre- and post-conference training seminars taught by seasoned experts. Half-day courses are only $125. You can sign up for classes when you register for the conference. Space is limited!

Saturday, April 28, 2018

Course Title (click for description) Instructor(s) (click for bio) Time
#1 Define-XML for SAS Programmers Lex Jansen 1:00 PM - 5:00 PM
#2 Clinical Graphs Using SAS Sanjay Matange 1:00 PM - 5:00 PM

Sunday, April 29, 2018

Course Title (click for description) Instructor(s) (click for bio) Time
#3 FDA & PMDA Submission Data Requirements David Izard 8:00 AM - 12:00 PM
#4 A Quick but Thorough Introduction in R Arthur Li 8:00 AM - 12:00 PM
#5 Analysis of Oncology Studies for Programmers and Statisticians Kevin Lee 8:00 AM - 12:00 PM
#6 Introductory ADaM Dataset Development: ADSL, OCCDS, and BDS Nancy Brucken
& Sandra Minjoe
& Mario Widel
8:00 AM - 12:00 PM
#7 A Practical Guide to Manipulating Clinical Trial Data with SAS Josh Horstman 1:00 PM - 5:00 PM
#8 Learning SAS Macro Language via Creating Macro Applications Arthur Li 1:00 PM - 5:00 PM
#9 An Introduction to Shiny, R Markdown, and HTML Widgets for R With Applications in Drug Development Phil Bowsher 1:00 PM - 5:00 PM
#10 Advanced ADaM Dataset Development: Beyond the ADaM IG Nancy Brucken
& Sandra Minjoe
& Mario Widel
1:00 PM - 5:00 PM

Wednesday, May 2, 2018

Course Title (click for description) Instructor(s) (click for bio) Time
#11 Advanced SDTM Topics Jerry Salyers
& Kristin Kelly
1:00 PM - 5:00 PM
#12 Basic and Advanced ODS Graphics Examples Warren Kuhfeld 1:00 PM - 5:00 PM
#13 Techniques for Exchanging Data and Analytical Results between SAS® and Microsoft Excel Vincent DelGobbo 1:00 PM - 5:00 PM

Seminar Registration, Attendance, and Cancellation Policy

  1. You must register for the conference in order to attend a seminar.
  2. You must register for a seminar via the PharmaSUG 2018 conference registration form either by postal mail, fax, or online (preferred).
  3. You may cancel a seminar on or before April 16, 2018, and receive a full refund minus a $25 administration fee per cancelled seminar.
  4. You may add a seminar on or before April 16, 2018 for no additional fee. To sign up for an additional seminar after you have already registered for the conference, please contact the This email address is being protected from spambots. You need JavaScript enabled to view it..
  5. On or before April 16, 2018, you may swap one seminar for another; however, this is considered a change in conference registration and will incur a $25 administration fee.
  6. After April 16, 2018, you MAY NOT SWAP seminars; however, a new seminar may be added depending on space and availability.
  7. There will be NO REFUNDS after April 16, 2018. However, if you are unable to attend, the seminar material will be provided to you (either by postal mail or email) without additional charge.
  8. Should a seminar be cancelled at any time for any reason, the sole liability of PharmaSUG and the instructor is a refund of the seminar fee, and they are NOT liable for any special or consequential damages arising from the cancellation of the seminar.
  9. On-site registration will be permitted based on space and availability, and payable by major credit card (MC, VISA, Discover, AMEX). However, seminar materials may not be available on-site but will be provided later to paid attendees.
  10. You may sign up for seminars occurring at the same time, i.e., you can attend one class and ask for material for another class, bearing in mind that tuition must be paid for both seminars.

For questions about the above seminar policy and availability, please contact Elizabeth Dennis and Kim Truett, Seminar Coordinators, at This email address is being protected from spambots. You need JavaScript enabled to view it..

Course Descriptions

Define-XML for SAS Programmers
Lex Jansen
Saturday, April 28, 2018, 1:00 PM - 5:00 PM

Extensible Markup Language (XML) is a markup language that defines a set of rules for encoding documents in a format that is both human-readable and machine-readable. It is an open standard that was developed by the Worldwide Web Consortium (W3C) to provide a flexible way to create common information formats and share both the format and the data on the World Wide Web, intranets, and elsewhere. CDISC is a global, open, multidisciplinary, non-profit organization that has established standards to support the acquisition, exchange, submission and archive of clinical research data and metadata.

The CDISC XML Technologies Team publishes several CDISC standards in an XML representation. These XML standards include the Operational Data Model (ODM) and several ODM extensions:
  • Define-XML
  • Controlled Terminology in XML (CT-XML)
  • Dataset-XML
  • Analysis Results Metadata for Define-XML 2.0.
The SAS Clinical Standards Toolkit is a framework used by SAS to support Health and Life Sciences industry data model standards. It is targeted at advanced SAS programmers and supports working with several XML based CDISC standards, such as ODM, Define-XML, Dataset-XML and CT-XML. This presentation will first introduce XML and will then give an overview of XML standards that are relevant to Define-XML for validation (XML Schema, Schematron) and transformation (XSL stylesheets). We will also introduce CDISC XML based standards: ODM, Define-XML and the Analysis Results Metadata extension for Define-XML 2.0.

We will then give examples of the way the SAS Clinical Standards Toolkit supports the Define-XML standard (including Analysis Results Metadata for ADaM). A goal of the seminar will be to gain a better technical understanding of CDISC based XML standards that are relevant to Define-XML and what the ways are in SAS to efficiently support those XML standards. We will also discuss features of the upcoming new version of Define-XML: Define-XML v2.1.

Intended audience: SAS programmers familiar with Base SAS and CDISC, and an interest in a technical presentation.
Required: Some knowledge of XML is beneficial, but not needed.

Clinical Graphs Using SAS
Sanjay Matange
Saturday, April 28, 2018, 1:00 PM - 5:00 PM

Graphs used in the Health and Life Sciences domain and Clinical Research have special requirements to display data in a clear and concise manner including raw data and derived statistics. Data needs to be displayed by treatment, visit and other classifiers along with related information such as Subjects at Risk aligned with the horizontal or vertical axis.

This ½ day presentation will introduce you to the key concepts of the SGPLOT and SGPANEL procedures including the layering of plot statements to create the graph you need. We will review the process and features by creating graphs commonly used in the Pharmaceutical industry. These include Mean Change in QTc by Visit, Distribution of ASAT by Time and Treatment, Survival Plot, Forest Plots, Adverse Event Timeline, Waterfall Chart for Change in Tumor Size, Distribution of Maximum LFT Values by Treatment, Swimmer Plot, Panel of LFT Shifts by Treatment and Immunology Profile.

Audience: Graph programmers Required: Basic SAS programming skills.

FDA & PMDA Submission Data Requirements
David Izard
Sunday, April 29, 2018, 8:00 AM - 12:00 PM

We have just started a new era for the submission of study data to the FDA, the binding guidance documents requiring you to provide data and related documentation based on FDA endorsed data standards as part of your electronic submission are in effect for both clinical and non-clinical assets. These documents have moved the needle with respect to Sponsor and CRO organization obligations in terms of how they plan and execute studies as well as prepare study assets for inclusion in a regulatory submission. But it is not just the US FDA when it comes to including data in a submission; Japan's PMDA has moved beyond the pilot phase into the voluntary phase with an eye on requiring submissions based on their endorsed data standards in 2020.

This highly interactive seminar will review each asset, its role in the submission and the impact that these final guidance documents have on how the asset is handled as it weaves its way through the drug development lifecycle on its way to the FDA. Simultaneously we will review the similarities and key differences executing these same tasks when interacting with Japan's PMDA. A portion of the seminar will be dedicated to a discussion of "hot off the press" topics, including a review of FDA & PMDA behavior since these documents have been finalized including Sponsor feedback during the review period. We will also explore how other global regulatory bodies are embracing standards, with a focus on Canada, Europe and China.

Audience Level: Beginner to Intermediate - individuals who are new to the Pharmaceutical industry would benefit greatly for the opportunity to put their hard work creating analysis datasets and TLFs into the context of a regulatory submission. Conversely, experienced professionals who have created submission assets in the past who are looking for a refresher on recent changes to FDA & PMDA requirements, CDISC standards and the future outlook on submission data requirements for other global regulatory bodies would also enjoy this seminar.

A Quick but Thorough Introduction in R
Arthur Li
Sunday, April 29, 2018, 8:00 AM - 12:00 PM

There are thousands of R packages that exist on CRAN (Comprehensive R Archive Network), and each package consists of a large number of functions. This overwhelming number may prevent a beginner from mastering the language since he or she doesn't know where to start and what components are necessary to grasp R language. Similar to other programming languages, one doesn't need to know all the functionality in order to perform daily routine work. This seminar will cover the fundamental components for learning the R language, such as differentiating the attributes across different types of R objects. Once these differences are known, manipulating data becomes much simpler to master. A few dozen basic and essential R functions and operators will be covered in this seminar, as well as writing a user-defined function.

Prerequisite: None

Analysis of Oncology Studies for Programmers and Statisticians
Kevin Lee
Sunday, April 29, 2018, 8:00 AM - 12:00 PM

Compared to other therapeutic studies, oncology studies are generally complex and difficult for programmers and statisticians. There is more to understand and to know such as different clinical study types, specific data collection points and analysis. In this seminar, programmers and statisticians will learn oncology specific knowledge in clinical studies and will understand a holistic view of oncology studies from data collection, CDISC datasets, and analysis. Programmers and statisticians will also find out what makes oncology studies unique and learn how to lead the programming and statistical teams.

The seminar will cover three different sub types and their response criteria guidelines. The first sub type, Solid Tumor study, usually follows RECIST (Response Evaluation Criteria in Solid Tumor) or irRECIST (immune-related RECIST). The second sub type, Lymphoma study, usually follows Cheson. Lastly, Leukemia studies follow study specific guidelines (e.g., IWCLL for Chronic Lymphocytic Leukemia).

Programmers and statisticians will learn how to create SDTM tumor specific datasets (RS, TU, TR), what SDTM domains are used for certain data collection, and what Controlled Terminology (e.g., CR, PR, SD, PD, NE) will be applied. They will also learn how to create time to event ADaM datasets from SDTM domains and how to use ADaM datasets to derive efficacy analysis (e.g., OS, PFS, TTP, ORR, DFS).

Introductory ADaM Dataset Development: ADSL, OCCDS, and BDS
Nancy Brucken, Sandra Minjoe, Mario Widel
Sunday, April 29, 2018, 8:00 AM - 12:00 PM

This half-day seminar introduces attendees to CDISC ADaM and the ADaM documents. We will discuss how ADaM fits into the clinical process, and describe the key principles of ADaM. We will cover how to apply the basic ADaM concepts, rules, recommended best practices, and the four types of ADaM metadata. The seminar then explains the ADSL, OCCDS and BDS models. Submission deliverables like ADRG and ADaM define.xml will be discussed as well. A basic understanding of SDTM and regulatory submission needs is expected.

A Practical Guide to Manipulating Clinical Trial Data with SAS
Josh Horstman
Sunday, April 29, 2018, 1:00 PM - 5:00 PM

The SAS System provides powerful tools for the analysis and reporting of clinical data. However, our data often must be merged, sorted, flipped, flopped, rearranged, or otherwise manipulated before reporting and analysis can proceed.

This half-day course will teach you essential techniques for using SAS to get your data into the format and structure you need for the task at hand. We'll cover common clinical programming situations such as merging datasets, transposing data from horizontal to vertical (or vice-versa), baseline flagging, deriving change from baseline, computing elapsed time since the last dose prior to an event, visit slotting, imputation, multi-level summarization of subject counts, and much more.

Examples will primarily revolve around the creation and use of CDISC ADaM datasets, but the programming methodologies covered are not limited to that context. As we evaluate different methods, we'll also discuss considerations such as efficiency and reusability. This course is suitable for beginning to intermediate SAS programmers who need to work with clinical trial or other medical research data.

Learning SAS Macro Language via Creating Macro Applications
Arthur Li
Sunday, April 29, 2018, 1:00 PM - 5:00 PM

When a novice SAS programmer first learns the SAS macro language, one often realizes that the results created from the macro programs are not what they intended. The problem is mostly due to a lack of understanding how macro processing works, including how SAS statements are transferred from the input stack to the macro processor and the DATA step compiler, what role the macro processor plays during this process, and when best to utilize the interface to interact with the macro facility during the DATA step execution. These issues are addressed in this workshop via creating simple macro applications step-by-step.

Specifically, the following topics will be covered in this workshop: creating macro variables using the %LET statement versus the SYMPUT(X) routine, combining macro variable references with text or other macro references, understanding the difference between the global and local symbol tables, conditionally processing a portion of a macro, and iteratively processing a portion of a macro.

Pre-requisite: Basic knowledge of SAS programming (such as creating SAS data sets and variables by using the DATA step)

An Introduction to Shiny, R Markdown, and HTML Widgets for R With Applications in Drug Development
Phil Bowsher
Sunday, April 29, 2018, 1:00 PM - 5:00 PM

RStudio will be presenting an overview of Shiny, R Markdown and HTML Widgets for the R user community at PharmaSUG. This is a great opportunity to learn and get inspired about new capabilities for creating compelling analyses with applications in drug development. No prior knowledge of R, RStudio or Shiny is needed. This short course will provide an introduction to flexible and powerful tools for statistical analysis, reproducible research and interactive visualizations. The hands-on course will include an overview of how to build Shiny apps, R Markdown documents and visualizations using HTML Widgets for R. Immunogenicity examples will be reviewed and generated for each topic. Shiny is an open source R package that provides an elegant and powerful web framework for building web applications using R. Shiny combines the computational power of R with the interactivity of the modern web. Shiny allows users the flexibility of pulling in whatever package in R needed to solve a problem. There are no limits to the types of applications one can build, and no constraint on the visualizations that can be used. Developers get the benefit of an open source ecosystem for R, along with the open source ecosystem for Javascript visualization libraries, thereby allowing one to create highly custom applications. Shiny helps you turn your analyses into interactive web applications without requiring HTML, CSS, or JavaScript knowledge. This powerful concept allows you to easily deliver results as interactive data explorations instead of static reports to your stakeholders and non R users. Immunogenicity assessments via Shiny will be covered. An introduction to databases via R will be reviewed along with how to connect Shiny apps to databases. An introduction to creating web APIs with your existing R code will also be discussed.

R Markdown is an authoring format that enables easy creation of dynamic documents, presentations, and reports from R. It combines the core syntax of markdown with embedded R code chunks that are run so their output can be included in the final document. R Markdown documents help to support reproducible research and can be automatically regenerated whenever underlying R code or data changes. Various types of R Markdown output will be covered, including blogdown and bookdown. An R Notebook is an R Markdown document with chunks that can be executed independently and interactively, with output visible immediately beneath the input. R Notebooks ca n be thought of as a special execution mode for R Markdown documents. The OpenFDA package and immunogenicity assessments will be used for the course examples regarding R Markdown reports and R Notebooks.

The htmlwidgets package provides a framework for easily creating R bindings to JavaScript libraries. htmlwidgets work just like R plots except they produce interactive web visualizations. htmlwidgets can be used at the R console as well as embedded in R Markdown reports and Shiny web applications or saved as standalone web pages for ad-hoc sharing and hosting anywhere via email, Dropbox, Amazon S3, GitHub etc. Crosstalk will be reviewed for implementing cross-widget interactions (currently, linked brushing and filtering). Immunogenicity ADA visualizations will be generated in the workshop. RStudio will be showcasing several compelling examples as well as learning resources. As part of the short course, some available drug development-related R Shiny apps and R Markdown reports will be illustrated.

Advanced ADaM Dataset Development: Beyond the ADaM IG
Nancy Brucken, Sandra Minjoe, Mario Widel
Sunday, April 29, 2018, 1:00 PM - 5:00 PM

This half-day course takes you beyond the examples in the ADaM Implementation Guide (ADaM IG), and shows you how to create analysis-ready datasets to meet more complex analysis requirements. Among the topics to be discussed are the addition of columns versus rows, approaches for handling multiple baseline definitions, creation of intermediate datasets while maintaining traceability back to SDTM, and avoidance of circular processing logic in ADSL. A working knowledge of basic ADaM structures and principles is expected.

Advanced SDTM Topics
Jerry Salyers, Kristin Kelly
Wednesday, May 2, 2018, 1:00 PM - 5:00 PM

This course will cover aspects of the SDTMIG that, in our experience, frequently present the greatest challenges to sponsors. These include the use of variables in the SDTM but not in domain models, when to create custom domains, and when to use Findings About rather than a custom Findings domain. In addition, considerations around when to create Supplemental Qualifiers and how to relate them back to parent domains via the most efficient IDVAR values will be presented.

Other problematic challenges include the use of relative Timing variables as updated in SDTMIG 3.2 and further detailed in SDTMIG 3.3, the submission of legacy terms in addition to CDISC Controlled Terminology, misuse of variables, not following conventions established for modeled domains when creating custom domains, and the creation of Trial Design datasets. The course will also include examples and exercises that highlight some of the newer domains in SDTMIG 3.3. The exercises will be used 1) to generate discussion, and 2) so that attendees can assess their overall understanding of SDTM as well as the concepts presented in the course material. The exercises will not require the use of computers or the knowledge of SAS.

Basic and Advanced ODS Graphics Examples
Warren Kuhfeld
Wednesday, May 2, 2018, 1:00 PM - 5:00 PM

Using ODS Graphics is simple; over 100 procedures create graphs as easily as they create tables. Using ODS Graphics can be more complex; you can use the graph template language and procedures such as PROC SGPLOT to create custom graphs. This tutorial starts with the basics and works its way through intermediate and advanced topics including creating highly-customized graphs.

In the basic part, you will learn how to: view graphs created by statistical procedures; make immediate changes to your graphs by using a point-and-click editor; make permanent changes to your graphs with template changes; use the SGPLOT, SGPANEL, SGSCATTER, and SGRENDER procedures to create a wide variety of statistical graphs; access and manage your graphs for inclusion in web pages, papers, and presentations; and modify graph styles (colors, fonts, and general appearance). In the intermediate part, you will learn how to: use SG annotation, use PROC SGPLOT to create sophisticated graphs that consist of multiple components, and create axis tables. In the advanced part, you will learn how to: use the ODS document, modify templates by using CALL EXECUTE, and use SG annotation and change dynamic variables to customize graphs that are produced by analytical procedures. Examples and advanced topics include: adverse event plots, Kaplan-Meier plot, forest plot, attribute maps, style attributes, attribute priority, ODS data object modification, and customized legend order.

Techniques for Exchanging Data and Analytical Results between SAS® and Microsoft Excel
Vincent DelGobbo
Wednesday, May 2, 2018, 1:00 PM - 5:00 PM

This course is designed to teach you many techniques for integrating SAS data and analytical results with Microsoft Excel, and bringing Excel data into SAS. You learn how to import Excel data into SAS using the IMPORT procedure, the SAS DATA step, SAS Enterprise Guide, and other methods. Exporting data and analytical results from SAS to Excel is performed using the EXPORT procedure, the SAS DATA step, SAS Enterprise Guide, the SAS Output Delivery System (ODS), and other tools. Working with multi-byte (non-English language) data is also covered. The material is appropriate for all skill levels, and the techniques work with various versions of SAS software running on the Windows, UNIX (including Linux), and z/OS operating systems. Some techniques require only Base SAS and others require the SAS/ACCESS Interface to PC Files. Adequate time provided to answer your questions, allowing the course material to be customized to meet your needs. A robust set of sample SAS code and data are provided to you, as well as printed material.

Instructor Biographies

Phil Bowsher

Phil is the Director of Healthcare and Life Sciences at RStudio. His work focuses on innovation in the pharmaceutical industry, with an emphasis on interactive web applications, reproducible research and open-source education. He is interested in the use of R with applications in drug development and is a contributor to conferences promoting science through open data and software. He has experience at a number of technology and consulting corporations working in data science teams and delivering innovative data products. Phil has over 10 years' experience implementing analytical programs, specializing in interactive web application initiatives and reporting needs for life science companies.

Nancy Brucken

Nancy Brucken is a member of the Data Standards team at INC Research/inVentiv Health. In that role, she reviews project deliverables for SDTM and ADaM compliance, and develops tools for automating the creation of those deliverables. She has been a member of the ADaM team since 2011, contributed to the development of OCCDS v1.0, ADaM IG v1.1 and ADaM IG v1.2, and is currently co-leading the ADQRS sub-team, and working on the upcoming ADaM Traceability publication.

Vincent DelGobbo

Vince DelGobbo is a Senior Software Developer in the Metadata and Execution Services group at SAS. This group's responsibilities include the SAS/IntrNet Application Dispatcher and SAS Stored Processes. He is involved in the development of new Web- and server-based technologies, bringing 3rd-party metadata into SAS, and integrating SAS output with Microsoft Office. He was also involved in the early development of the ExcelXP ODS tagset. Vince has been a SAS Software user since 1982, and joined SAS in 1992.

Josh Horstman

Josh Horstman is an independent statistical programmer based in Indianapolis with 20 years experience in the life sciences industry. He is a SAS Certified Programmer specializing in analyzing clinical trial data. His clients have included major pharmaceutical corporations, biotech companies, and research organizations. Josh loves coding and has enjoyed presenting numerous times at SAS Global Forum, PharmaSUG, and various regional and local SAS User Group meetings. He holds a bachelor's degree in mathematics and computer science, and a master's degree in statistics from Colorado State University.

David Izard

Dave Izard frequently finds himself at the intersection of clinical data standards, regulatory expectations and sponsor organization needs and desires. A pharmaceutical professional since 1997, he currently serves as Programming Director at GlaxoSmithKline, supporting Infectious Disease clinical asset development and GSK’s efforts to expand their regulatory submission capabilities. Earlier opportunities include serving as Senior Director of Clinical Data Standards at Chiltern (Covance), Clinical Data Consulting Lead at Accenture, Head of Octagon Research Solutions' SDTM practice, and a variety of Clinical Programming leadership roles at both GSK and Shire.

He has served as a paper author & presenter, seminar instructor and section chair at industry conferences including the PharmaSUG main conference and Single Day Events, Pharmaceutical Users Software Exchange (PhUSE) Single Day Events, the Society of Clinical Data Management (SCDM) and various local and regional SAS meeting. His current PhUSE efforts include supporting the development of the Study Data Standardization Plan and Legacy Data Conversion Plan & Report templates. He holds Bachelors and Masters of Science Degrees in Computer Science from Bucknell and West Chester University respectively.

Lex Jansen

Lex Jansen is a Principal Software Developer at SAS Institute, Health and Life Sciences R&D. In this role, he develops software that supports data standards in the pharmaceutical industry. He is one of the developers of the SAS Clinical Standards Toolkit. Currently he is one of the developers of the SAS Life Science Analytics Framework. Prior to working at SAS he was a Senior Consultant, Clinical Data Strategies at Octagon Research Solutions, Inc. In this position, Lex worked on client consulting projects dealing with the assessment, design and/or implementation of CDISC standards. Before his employment with Octagon, he held various positions in the 16 years that he worked at the pharmaceutical company Organon. Lex holds a MSc in Mathematics from the Eindhoven University of Technology in the Netherlands. Since 2008 Lex has been a member of the CDISC XML Technologies Team, where he has been active in the development of various CDISC standards: Define-XML 2.0/2.1, Dataset-XML and the Analysis Results Metadata extension for Define-XML 2.0. Lex owns the website (www.lexjansen.com) which is well-known in the SAS community and contains more than 30,000 links to papers that were presented at major SAS User Group conferences.

Kristin Kelly

Kristin Kelly is an Associate Director at Pinnacle 21 in the Clinical Data Standards group. Kristin has over 10 years of experience working in the pharmaceutical industry primarily focused on clinical data standards. Prior to joining Pinnacle, Kristin was an Associate Director at Merck in the Global Clinical Data Standards (GCDS) group. She was also worked a consultant in Data Standards Consulting (DSC) for Accenture (formerly Octagon Research Solutions). She provides guidance on CDASH and SDTM standards to both internal project teams and external clients. Kristin is also involved with the CDISC SDS team, CFAST Initiative TAUG teams, SEND Core Team, as well as several PhUSE CSS Working Groups. Kristin has led several SDTM conversion projects for submission to both CDER and CBER that have resulted in FDA approvals.

Warren Kuhfeld

Warren F. Kuhfeld is a Distinguished Research Statistician Developer in SAS/STAT Research and Development. Kuhfeld received his PhD in psychometrics from the University of North Carolina at Chapel Hill in 1985 and joined SAS in 1987. He has used SAS software since 1979 and has developed SAS procedures since 1984. Kuhfeld wrote the free SAS books Basic ODS Graphics Examples and Advanced ODS Graphics Examples. He is a regular contributor to the Graphically Speaking blog. He has written the following SAS/STAT documentation chapters: Chapter 20, Using the Output Delivery System; Chapter 21, Statistical Graphics Using ODS; Chapter 22, ODS Graphics Template Modification; Chapter 23, Customizing the Kaplan-Meier Survival Plot; and many other chapters. He has presented papers and tutorials over the past 30+ years at SUGI, SAS GF, PharmaSUG, SEUGI, SUGI-K (Korea), Club SAS (France), BENELUX SUG, DCSUG, BASUG, CleSUG, Montreal SUG, WIILSU, SESUG, NESUG, SWSUG, MWSUG, WUSS, JSM, ART Forum, pharmaceutical companies, and elsewhere.

Kevin Lee

Kevin Lee is Director of Data Science at Clindata Insight. Kevin has worked as a programmer/statistician/CDISC expert in pharmaceutical industry about 20 years. Among all the therapeutic area, Kevin always loves oncology studies and an active supporter on oncology specific standards such as CDISC Tumor datasets, control terminology and response criteria on each oncology type. Kevin has presented more than 50 papers at the various conferences including many oncology-related papers. Kevin earned an M.S. in Applied Statistics at Villanova University following a B.S. from University of Pennsylvania. Kevin is a life time learner who loves to learn and share.

Arthur Li

Arthur holds an M.S. in Biostatistics from the University of Southern California. Currently, he is a Biostatistician at the City of Hope National Medical Center. In addition, Arthur developed and taught an introductory SAS course at U.S.C. for the past ten years, as well teaching the Clinical Biostatistics Course at U.C.S.D. extension. As well as teaching and working on cancer-related research, Arthur has written a book titled "Handbook of SAS DATA Step Programming." In 2016, he served as the conference chair for PharmaSUG China in Beijing.

Sanjay Matange

Sanjay Matange is R & D Director in the Data Visualization Division at SAS, responsible for the development and support of ODS Graphics. This includes the Graph Template Language (GTL), Statistical Graphics (SG) procedures, ODS Graphics Designer and other related graphics software. Sanjay has been with SAS for over 25 years, is the co-author of four patents, author of four SAS Press books, and author of Graphically Speaking, a blog on data visualization.

Sandra Minjoe

Sandra Minjoe is a Senior ADaM Consultant at Accenture Life Sciences. She has been part of the CDISC ADaM team since 2001, helping develop ADaM documents and lead ADaM projects. Sandra is the current ADaM Team Lead, a CDISC Global Governance Group (GGG) team member, and is an ADaM trainer for CDISC.

Jerry Salyers

With Accenture, Jerry provides an internal consulting resource to the Data Standards and Integration department as well as the in-house data management group. He also works one-on-one directly with several sponsors in review of mapping specifications (via CRFs and datasets) from source to SDTM-based datasets.. He creates and delivers training classes on both CDASH and the SDTM to internal functions, and custom training on these standards to external clients. Jerry has co-authored and presented papers in each of the past 5 PharmaSUG conferences as well as co-authoring and presenting a paper at the PhUSE conference in London in 2014. He has also presented at numerous PharmaSUG and PhUSE single day events in the past few years. Jerry is an authorized CDISC instructor for both the CDASH and SDTM standards. He has been active in the pharmaceutical industry for 20 years.

Mario Widel

Mario Widel is a Principal Statistical Programmer at Chiltern International. He has been doing statistical programming since 1992 and involved in CDISC related activities since 2007, having a key role in the transition to CDISC standards CDASH, SDTM and ADaM. In his current role as a CDISC SME, Mario establishes CDISC implementation standards and trains staff on CDISC standards and data submission topics, among other tasks. He is a regular presenter at conferences like JSM, PharmaSUG, SAS Global Forum, PhUSE and CDISC, and a member of ASA and the CDISC ADaM team. Mario is an authorized CDISC instructor.