Enhance your PharmaSUG experience by attending optional pre- and post-conference training seminars taught by seasoned experts. Half-day courses are only $125 and full-day courses are $250. You can sign up for classes when you register for the conference. Space is limited!

Saturday, May 13, 2017

Course Title (click for description) Instructor(s) (click for bio) Time
#1 An Introduction to Building Clinical Trial Dashboards Using SAS® Base Software Kirk Paul Lafler
& Roger Muller
& Josh Horstman
8:00 AM - 12:00 PM
#2 Exchanging Data between SAS and EXCEL, Using Basic SAS Techniques William E Benjamin 8:00 AM - 12:00 PM
#3 Rediscovering the DATA _NULL_ for Report Generation David Franklin 8:00 AM - 12:00 PM
#4 Building Dynamic Programs and Applications Using the SAS® Macro Language Art Carpenter 1:00 PM - 5:00 PM
#5 A Quick but Thorough Introduction in R Arthur Li 1:00 PM - 5:00 PM
#6 Define-XML for SAS® Programmers Lex Jansen 1:00 PM - 5:00 PM

Sunday, May 14, 2017

Course Title (click for description) Instructor(s) (click for bio) Time
#7 An Introduction to Shiny, R Markdown, and HTML Widgets for R With Applications in Drug Development Phil Bowsher 8:00 AM - 12:00 PM
#8 FDA Submission Data Requirements David Izard 8:00 AM - 12:00 PM
#9 Clinical Graphs Using SAS® Sanjay Matange 8:00 AM - 12:00 PM
#10 R Graphics Arthur Li 1:00 PM - 5:00 PM
#11 Innovative Tips and Techniques: Doing More in the DATA Step Art Carpenter 1:00 PM - 5:00 PM
#12 ADaM Datasets: Creation and Applications Sandra Minjoe
& Mario Widel
1:00 PM - 5:00 PM

Wednesday, May 17, 2017

Course Title (click for description) Instructor(s) (click for bio) Time
#13 Working with Healthcare Data:  Understanding electronic health record data  Greg Nelson 1:00 PM - 5:00 PM
#14 Medical Device Overview and Its Application to Pharmaceutical Products Carey Smoak 1:00 PM - 5:00 PM
#15 The SDTMIG: Beyond the Modeled Domains and Other Implementation Challenges Fred Wood
& Jerry Salyers
1:00 PM - 5:00 PM

Thursday, May 18, 2017

Course Title (click for description) Instructor(s) (click for bio) Time
#16 From %Macro to %MEND: An Introduction to the SAS® Macro Language Art Carpenter 8:00 AM - 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 2017 conference registration form either by postal mail, fax, or online (preferred).
  3. You may cancel a seminar on or before May 1, 2017, and receive a full refund minus a $25 administration fee per cancelled seminar.
  4. You may add a seminar on or before May 1, 2017 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 May 1, 2017, 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 May 1, 2017, you MAY NOT SWAP seminars; however, a new seminar may be added depending on space and availability.
  7. There will be NO REFUNDS after May 1, 2017. 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 Cecilia Mauldin and Kim Truett, Seminar Coordinators, at This email address is being protected from spambots. You need JavaScript enabled to view it..




Course Descriptions

An Introduction to Building Clinical Trial Dashboards Using SAS® Base Software
Kirk Paul Lafler, Roger Muller, Josh Horstman
Saturday, May 13, 2017, 8:00 AM - 12:00 PM


The concept of dashboards, which serve to display the current status of “point-in-time” metrics and key performance indicators, is often associated with business intelligence. However, such tools can have enormous utility within the clinical trial arena as well. Effectively designed dashboards extract real-time data from multiple sources for the purpose of highlighting important information, numbers, tables, statistics, metrics, performance scorecards and other essential content on a single screen. Applications include monitoring the ongoing conduct of a trial and managing the process of creating and validating statistical outputs for large and complex clinical reporting efforts.

This seminar explores an assortment of best practice programming techniques in the design of highly interactive, filterable, and drill-down dashboards using Base-SAS® software. Attendees learn how to create effective dashboards with a purpose not in weeks or months, but in hours, using Base-SAS® programming techniques including DATA step, PROC FORMAT, PROC PRINT, PROC MEANS, PROC SQL, ODS, ODS Statistical Graphics, PROC SGRENDER, PROC SGPLOT, PROC SGPANEL, and PROC TEMPLATE.


Exchanging Data between SAS and EXCEL, Using Basic SAS Techniques
William E Benjamin
Saturday, May 13, 2017, 8:00 AM - 12:00 PM


This course is the first of two parts and will examine some methods all levels SAS programmers may be familiar with. The detailed examples presented in the course demonstrate movement of data between SAS and Excel file structures and how to change the format of variables read from an Excel file using DATASET options to read specific Excel columns. Several of these features are available with only BASE SAS installed, however others will require the installation of additional SAS/ACCESS products or other SAS software. Each of the examples will be shown and explained in enough detail to allow application by the user when the course is complete. While some of these techniques are common and widely used, they are also feature rich and permit access to Excel files in ways that are not frequently used.

This session the material to be covered will be derived from the Chapters 1 to 8 of “Exchanging Data between SAS and Microsoft Excel: Tips and Techniques to Transfer and Manage Data More Efficiently”.
  1. Conversion of text files to an Excel format using built in Excel data conversion features
  2. PROC EXPORT and PROC IMPORT features and examples
  3. SAS LIBNAME methods and examples
  4. SAS Enterprise Guide methods to access Excel files
  5. ODS Tagset Template output files (CSV, HTLM, MSOFFICE2K, and EXCELXP)
  6. SAS procedures that output Tagset Template files
  7. ODS Excel destination output to Excel files, usage and examples



Rediscovering the DATA _NULL_ for Report Generation
David Franklin
Saturday, May 13, 2017, 8:00 AM - 12:00 PM


Before PROC REPORT existed, most text output files were created by PROC PRINT and PROC TABULATE, or where a report was needed that needed a bit more sophistication, DATA _NULL_ was used. This seminar rediscovers the DATA _NULL_ as it is used for creating a text output file and presents a few macros, tricks and techniques that will make your report shine, including a macro for wrapping text so it can go onto multiple lines, doing “Page x of y” processing, centering or right aligning text, page breaking where you want it, and carrying over group values from one page to another. Finally, the output that is produced from a full example will be transformed into an RTF file by a macro in a single datastep. This is then going to be kicked up a notch to take a look at the DATA _NULL_ and the ODS= option in the FILE statement, and combining this with PROC TEMPLATE, Output Objects and ODS destinations to produce reports that will outwit (nearly) any request for a complicated output. This is a rewritten presentation I did some years ago with a complete new section on using the ODS= option and Output Destinations. There will be lots of examples, a discussion on the merits of DATA _NULL_ vs. PROC REPORT, interactive questions and answers.


Building Dynamic Programs and Applications Using the SAS® Macro Language
Art Carpenter
Saturday, May 13, 2017, 1:00 PM - 5:00 PM


This seminar shows you how to take advantage of SAS Macro Language capabilities that enable you to write dynamic programs and applications. By mastering the concepts and techniques presented in this class your programs will become free of hard-coded data dependencies, thus eliminating the need to re-write the code every time a data set name, variable name, or other data attribute, changes. Let “them” change the project's specifications as often as “they” want your code is ready!

The dynamic programming techniques that you will learn about during this seminar:
  • Are flexible and are easily adaptable to changing data structures, data table names, and variable (field) attributes
  • Reduce maintenance requirements by removing data dependencies from within the programs
  • Provide significant resource savings during program/application development cycles
  • Gives the end-user extensive control over program execution by using tables such as SAS data dictionaries, SAS data sets, and Excel tables
  • Reduce program validation efforts by providing reusable and generalized code that can be applied to many different applications
  • Establish controlled data environments, thus insuring data integrity throughout your organization
This course makes extensive use of example macros that have been gathered from real world applications, and it concentrates on the techniques necessary to make effective use of these tools. Intended Audience Level: Strong understanding of the macro language Delivery Method: Seminar style Class Material: Course notes


A Quick but Thorough Introduction in R
Arthur Li
Saturday, May 13, 2017, 1:00 PM - 5:00 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 might be one of the reasons that intrigue a beginner from mastering the language since he or she doesn’t know where to start and what the essential components are that they need to know to grasp in R language. Similar to other programming languages, one doesn’t need to know all the functionality in a language in order to perform the 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 knowing these differences, manipulating data would become simple to master. Furthermore, a few dozen basic and essential R functions and operators, as well as writing a user-defined function, will also be covered in this seminar.

Prerequisite: None


Define-XML for SAS® Programmers
Lex Jansen
Saturday, May 13, 2017, 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) in order 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 creates 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.


An Introduction to Shiny, R Markdown, and HTML Widgets for R With Applications in Drug Development
Phil Bowsher
Sunday, May 14, 2017, 8:00 AM - 12:00 PM


This is a great opportunity to learn and get inspired about new capabilities for creating compelling analyses of complex datasets with applications in drug development. No prior knowledge of R or RStudio is needed. This short course will provide an introduction to flexible and powerful tools for statistical analysis. The hands-on course will include an overview of how to build Shiny apps, R Markdown documents and visualizations using HTML Widgets for R.

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. Shiny Server Pro adds enterprise grade scaling, security, and admin features to the open source edition. 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 are fully reproducible and can be automatically regenerated whenever underlying R code or data changes. The OpenFDA package will be used for the course examples.

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 via email, Dropbox, etc.

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 wil l be illustrated.


FDA Submission Data Requirements
David Izard
Sunday, May 14, 2017, 8:00 AM - 12:00 PM


We have just started a new era for the submission of clinical and non-clinical 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.  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 clinical assets for inclusion in a regulatory submission.

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. A portion of the seminar will be dedicated to a discussion of “hot off the press” topics, including a review of FDA 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 Japan, the next country to establish electronic submission requirements.

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 requirements, CDISC standards and the future outlook on submission data requirements for other global regulatory bodies would also enjoy this seminar.


Clinical Graphs Using SAS®
Sanjay Matange
Sunday, May 14, 2017, 8:00 AM - 12:00 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 half-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.


R Graphics
Arthur Li
Sunday, May 14, 2017, 1:00 PM - 5:00 PM


One of the reasons the R language has become a popular analytic software is because of its powerful functionality in its plotting systems. One can easily generate a high-quality and publication-ready figure in R without having to put too much effort in adjusting the parameters of R graphic functions. In this seminar, in addition to exploring the different R plotting systems, we will focus on the ggplot2 plotting system, which contains the latest graphing techniques in R.

Prerequisite: Having knowledge of the basics in R language, such as knowing different methods for extracting components of objects.


Innovative Tips and Techniques: Doing More in the DATA Step
Art Carpenter
Sunday, May 14, 2017, 1:00 PM - 5:00 PM


In order for you to use SAS® to write innovative DATA step solutions to complex coding problems, it is necessary for you to have more than a basic understanding of the individual statements. You need to understand how the various statements interact with each other and how their options can be leveraged to provide the kind of DATA step code that will provide innovative solutions to the toughest of problems. Based on Art’s latest book, Carpenter’s Guide to Innovative SAS® Techniques, published in the spring of 2012, this class is a must for the DATA step programmer that wants to take his or her programs to the ‘next’ level.

Topics include:
  • Data set options with impact
  • New functions and old functions with new options
  • Evaluating expressions
  • Working with Data Component Objects - Hash Tables
  • Transposing the data using arrays
  • Using the DOW loop
  • Using double SET statements effectively
  • Look-ahead and Look-back techniques
  • Using Multi-label formats to create running averages
  • Table look-ups in the DATA step
  • and much more
This course is designed to be taken by a student who has a basic understanding of the DATA step, its primary statements, and its basic operation. The seminar will provide a short refresher of these basics, but will concentrate on topics that will allow the user to take full advantage of the power of the DATA step.

The student will leave the seminar with a deeper understanding of the operation of the DATA step and a number of its primary statements. The student will be exposed to a number of advanced techniques that solve difficult programming problems in innovative ways. The relative efficiencies of a number of competing techniques will be discussed along with the methodologies for their implementation.

Intended Audience Level: Beyond Beginner
Delivery Method: Seminar style
Class Material: Copy of the PPT slides


ADaM Datasets: Creation and Applications
Sandra Minjoe, Mario Widel
Sunday, May 14, 2017, 1:00 PM - 5:00 PM


This half-day course shows you how to develop your CDISC ADaM datasets to meet your specific statistical analysis needs, such as t-tests, proportions, time to events, occurrence data (adverse events, concomitant medications) and ANOVA. Students will learn specifics of the published ADaM dataset structures and how to apply them to their own clinical study data. Additionally, we will examine situations that don’t fit the published structures, like multivariate analyses, and describe options for handling them.


Working with Healthcare Data:  Understanding electronic health record data 
Greg Nelson
Wednesday, May 17, 2017, 1:00 PM - 5:00 PM


As pharmaceutical and biotechnology companies expand their use of data to include patient data from electronic health record systems, it is important to recognize that this change from working with randomized clinical trials data to the real-world, observational data requires an updated skillset for clinical and statistical programmers as well as those in data management. 

This workshop will cover a range of topics broken into two parts: (a) understanding and interpreting data found in an EHR and (b) detailed case studies that dive into the realities of working with healthcare data. While this is not intended to be a hands-on workshop, it will provide you with an opportunity for critical thinking and small-group problem solving where you will learn the foundations of working with EHR data and gain the skills needed to start working with healthcare data. 

Course participant will learn the following: 
  • The “language” used to describe data found in the electronic health record 
  • How the clinical workflow is represented in the data and their structures 
  • How coding standards such as ICD-10 and SNOMED are reflected in the patient record 
  • Why laboratory results and other tests are structured the way they are 
  • The tactics of how to create an episode of care that spans multiple patient visits, locations, providers and “encounters” 
  • How common EHR vendors such as Epic structure their product modules and how they tie back to workflows (e.g., inpatient, ambulatory, revenue cycle, labs) 
  • The relationships between various clinical and administrative systems and the importance of interoperability 
  • How data from operational EHR systems can be used in analytic models (including predictive and geospatial analytics) 
  • How Pharmaceutical companies are utilizing patient data and contributing to the Learning Health System 
Even if you are an experience SAS programmer that may be used to working with healthcare claims data, this workshop will equip you with the necessary skills needed to understand and appreciate the world of electronic health records. 


Medical Device Overview and Its Application to Pharmaceutical Products
Carey Smoak
Wednesday, May 17, 2017, 1:00 PM - 5:00 PM


The purpose of this seminar is to provide an overview of the process of getting medical devices approved / cleared, to give an overview of CDISC for medical devices and to give an overview of the overlap between medical devices and drug / biologic products.  With regards to the latter, examples of combination products (device and drug / biologic products) will be illustrated.  Moreover, seventeen out of twenty-four Therapeutic Area User Guides (TAUGs) use all seven of the medical device domains (SDTMIG-MD).  Additionally, the Associated Persons Implementation Guide (SDTMIG-AP) includes examples of device data for non-subjects in a clinical trial.   The intended audience is both people in the medical device industry and the pharmaceutical / biotech (drugs / biologics) industry.  This workshop is not just for medical device professionals.  Pharmaceutical professionals will benefit from this workshop as medical device data is being used in TAUGs, Associated Persons (SDTMIG-AP) and in combination drug-device products.

Course Outline:
  • Introduction
  • An overview of medical devices
    • What are medical devices and how are they different from pharmaceutical products?
    • The seven SDTM domains for medical devices:
      • DI – Device Identifiers
      • DO – Device Properties
      • DU – Device In-Use
      • DX – Device Exposure
      • DE – Device Events
      • DT – Device Tracking and Disposition
      • DR – Device-Subject Relationships
    • The relationship of device and subject data:
      • Example of device domains which do not have any subject data – DI and DO
      • Example of device domains which may have subject data – DU, DT and DE
      • Example of device domains which will have subject data – DX and DR
      • Example of non-device SDTM domains which may have device data – AE
      • Examples of non-device SDTM domains which will not have device data – DM, EX, DS
    • Types of medical device submissions - PMAs, 510(k)s
    • The current status of CDISC standards for medical device submissions
      • The need and benefit of CDISC standards for med ical devices
      • Challenges in developing CDISC standards for medical devices
  • Combination drug/device products
    • Examples: drug eluting heart stents; targeted therapies and companion diagnostics
    • I have worked on three companion diagnostic studies (along with their targeted therapies) which were approved by the FDA
  • Therapeutic Area User Guides (TAUGs) - drug studies which use devices in their studies
    • Currently, seventeen of twenty-four TAUGs use all seven of the SDTM Medical Device domains
    • Examples: imaging devices in Alzheimer’s studies, glucose meters and lancets in diabetes studies, etc.
  • Associated Persons (SDTMIG-AP)
    • Example: Collection of device data in non-subjects in a clinical trial
  • Summary and Conclusions



The SDTMIG: Beyond the Modeled Domains and Other Implementation Challenges
Fred Wood, Jerry Salyers
Wednesday, May 17, 2017, 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, 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 and concepts in SDTMIG 3.2, as well as some planned for SDTMIG v3.3.  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®.


From %Macro to %MEND: An Introduction to the SAS® Macro Language
Art Carpenter
Thursday, May 18, 2017, 8:00 AM - 5:00 PM


This one-day course is designed for the SAS programmer who is new to the Macro Language. We will start at the basics and cover the fundamentals necessary to start applying SAS macros in your programs. By the end of the day you will understand how the Macro Language works, what the Macro Symbol Table is and how values are stored in it, how the SAS System uses Macro Variables, key Macro Language concepts, important SAS Macro Language statements, and how to invoke Macros in your programs. The example Macros shown in the course materials demonstrate the power and flexibility of this part of the SAS System and will enable you to apply the functionality of the Macro Language to your own programs right away.

This session is suited for the SAS user who already has a basic understanding of the Data Step and Procedure Steps, and who is new to the Macro Language facility in SAS System software. It is a beginning-level course that assumes no prior understanding of the SAS Macro Language. It is also suitable for SAS users who want to understand the Macros found in programs that have been "inherited" from other programmers.

Intended Audience Level: Beginner to early intermediate
Delivery Method: Seminar style
Class Material: Course Notes





Instructor Biographies


William E Benjamin

William Benjamin's expertise includes Base SAS® Software, and SAS Macros. William has a BS degree in computer science from Arizona State University and an MBA from Western International University. He has been a SAS software user since 1983 and a computer programmer since 1973. His programming experience spans from vacuum tube mainframes, to current PC computers. William currently owns a consulting company called OWL Computer Consultancy, LLC in Phoenix AZ. His SAS Press book "Exchanging Data between SAS and Microsoft Excel: Tips and Techniques to Transfer and Manage Data More Efficiently" was released in April 2015.

Phil Bowsher

Phil is a Sr. Account Executive in Customer Success at RStudio. His background is in analytical programs and consulting. 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.

Art Carpenter

Art Carpenter’s publications list includes five books, and numerous papers and posters presented at SUGI, SAS Global Forum, and other user group conferences. Art has been using SAS® since 1977 and has served in various leadership positions in local, regional, national, and international user groups. He is a SAS Certified Advanced Programmer and through California Occidental Consultants he teaches SAS courses and provides contract SAS programming support nationwide.

David Franklin

David started programming in SAS in 1985 in the land known now as "Middle Earth". After finding the way to surface he worked in Europe and later found his way to New England which he now calls home. Since 2004 he has been a frequent presenter at user conferences, including PharmaSUG, and currently works at Quintiles Real World Late Phase division in Cambridge, MA as a Programming Manager, and edits a monthly SAS Users Newsletter.

Josh Horstman

Josh Horstman is an independent statistical programmer with 18 years experience using SAS in the pharmaceutical industry. He has experience working for GlaxoSmithKline, Eli Lilly and Company, inVentiv Health, and others. Josh has presented numerous papers at PharmaSUG, SAS Global Forum, and other 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.

Dave 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 Senior Director of Clinical Data Standards at Chiltern.  Before joining Chiltern he first led Octagon Research Solutions'  SDTM practice prior to serving as Clinical Data Consulting Lead at Accenture following Accenture's acquisition of Octagon.  Dave also held 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 Pharmaceutical SAS Users Group (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. 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. Previous to 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.  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 almost 30,000 links to papers that were presented at major SAS User Group conferences.

Kirk Paul Lafler

Kirk Paul Lafler has been programming in SAS® since 1979 and is consultant and founder of Software Intelligence Corporation. Kirk teaches SAS programming classes at UC San Diego Extension, is a SAS Certified Professional, application developer, programmer, data scientist, mentor, and provider of IT consulting services and education to SAS users around the world. As the author of six books including Google® Search Complete! (Odyssey Press. 2014) and PROC SQL: Beyond the Basics Using SAS, Second Edition (SAS Institute. 2013); Kirk has written hundreds of papers and articles; been an Invited speaker at hundreds of SAS International, regional, special-interest, local, and in-house user group conferences/meetings; and is the recipient of 25 “Best” contributed paper, hands-on workshop (HOW), and poster awards.

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 applications. Sanjay has been with SAS for over 20 years and is author of two SAS Press books.

Sandra Minjoe

Sandra Minjoe started programming in the pharma/biotech industry in 1993 and is now a Senior ADaM Consultant at Accenture Life Sciences. A CDISC ADaM team member since 2001, she proposed the ADaM structures that have become ADSL and OCCDS. Sandra continues to help develop ADaM standards, reviews draft ADaM documents with a focus on the fundamental principle of traceability, is an authorized CDISC trainer, and is part of the ADaM Leadership Team (ALT). In addition to her CDISC involvement, Sandra is a PharmaSUG Executive Committee member.

Roger Muller

Roger D. Muller, Ph.D. has been as SAS user for over 40 years, primarily in the life sciences, business marketing, and educational areas. His current interests are in creative display of textual and graphical information using a wide variety of SAS products.

Greg Nelson

Greg is the founder and CEO of ThotWave Technologies, a niche consultancy specializing in healthcare analytics. With certifications in Healthcare IT, Project Management, Six Sigma and Balanced Scorecard, Greg is also a prolific writer and has presented over 200 professional and academic papers in the United States and Europe. Before starting ThotWave, he worked for i3/Ingenix, the Gallup Organization and a boutique research firm in Palo Alto, California.

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.

Carey Smoak

In May of 2006, Carey co-founded the CDISC Medical Device team. He has more than 10 years of experience in medical devices and more than 5 years of experience in pharmaceutical / biotech. The purpose of this seminar is to provide an overview of the process of getting medical devices approved / cleared, to give an overview of CDISC for medical devices and to give an overview of the overlap between medical devices and drug / biologic products. With regards to the latter, examples of combination products (device and drug / biologic products) will be illustrated. Moreover, fourteen out of eighteen Therapeutic Area User Guides (TAUGs) use all seven of the medical device domains (SDTMIG-MD). Additionally, the Associated Persons Implementation Guide (SDTMIG-AP) includes examples of device data for non-subjects in a clinical trial. The intended audience is both people in the medical device industry and the pharmaceutical / biotech (drugs / biologics) industry. This workshop is not just for medical device professionals. Pharmaceutical professionals will benefit from this workshop as device data is being used in TAUGs, Associated Persons (SDTMIG-AP) and in combination drug-device products.

Mario Widel

Mario Widel is a Research Scientist at Eli Lilly and Company.  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 Mario oversees and participates in the metadata definition and creation of  SDTM and ADaM datasets. 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 trainer.

Fred Wood

Fred 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. Fred is a CDISC-authorized instructor for SEND.