Enhance your PharmaSUG experience by attending optional pre- and post-conference training seminars taught by seasoned experts. Half-day courses are only $150 with a conference registration, or $250 without a conference registration. You can sign up for classes through the conference registration system. Space is limited!

Saturday, June 15, 2019

Course Title (click for description) Instructor(s) (click for bio) Time
#11 Define-XML for SAS Programmers Lex Jansen 1:00 PM - 5:00 PM
#12 PROC REPORT: Clinical Reports from Top to Bottom Jane Eslinger 1:00 PM - 5:00 PM
#13 Techniques for Exchanging Data and Analytical Results between SAS® and Microsoft Excel Vince DelGobbo 1:00 PM - 5:00 PM
#14 Advanced Clinical Graphs Using GTL Sanjay Matange 1:00 PM - 5:00 PM

Sunday, June 16, 2019

Course Title (click for description) Instructor(s) (click for bio) Time
#21 OOP, Here We Go with DS2 Charu Shankar 8:00 AM - 12:00 PM
#22 Data Manipulations in R Arthur Li 8:00 AM - 12:00 PM
#23 Python Training for Statistical Programmers and Statisticians Kevin Lee 8:00 AM - 12:00 PM
#24 FDA & PMDA Submission Data Requirements David Izard 8:00 AM - 12:00 PM
#25 Introductory ADaM Dataset Development: ADSL, OCCDS, and BDS Sandra Minjoe
& Mario Widel
8:00 AM - 12:00 PM
#31 Data-Driven Design in SAS® and Python: Developing More Dynamic, Flexible, Configurable Software Troy Hughes 1:00 PM - 5:00 PM
#32 Learning the SAS Macro Language Arthur Li 1:00 PM - 5:00 PM
#33 Analysis of Oncology Studies for Programmers and Statisticians Kevin Lee 1:00 PM - 5:00 PM
#34 R for Drug Development: An Introduction to the Tidyverse, Shiny & R Markdown Phil Bowsher 1:00 PM - 5:00 PM
#35 Advanced ADaM Dataset Development: Beyond the ADaMIG Sandra Minjoe
& Mario Widel
1:00 PM - 5:00 PM

Wednesday, June 19, 2019

Course Title (click for description) Instructor(s) (click for bio) Time
#41 The SDTMIG: Beyond the Basics Kristin Kelly
& Jerry Salyers
& Fred Wood
1:00 PM - 5:00 PM
#42 Deep Dive into Electronic Submission Components for Regulatory Submission of Clinical Study Data Prafulla Girase 1:00 PM - 5:00 PM
#43 Living the Dream: A Practical Approach to Automating End to End Standards Steve Kirby
& Mario Widel
1:00 PM - 5:00 PM
#44 Spotfire Training for Statistical/Clinical Programmers Bhavin Busa 1:00 PM - 5:00 PM


Seminar Registration, Attendance, and Cancellation Policy

  1. You must register for seminars using the online PharmaSUG 2019 conference registration system, even if you are not registering for the conference itself.
  2. You may cancel a seminar on or before June 7, 2019, and receive a full refund minus a $25 administration fee per cancelled seminar.
  3. You may add a seminar on or before June 7, 2019 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..
  4. On or before June 7, 2019, you may swap one seminar for another; however, this is considered a change in conference registration and will incur a $25 administration fee.
  5. After June 7, 2019, you MAY NOT SWAP seminars; however, a new seminar may be added depending on space and availability.
  6. There will be NO REFUNDS after June 7, 2019. However, if you are unable to attend, the seminar material will be provided to you (either by postal mail or email) without additional charge.
  7. 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.
  8. 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.
  9. 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 Cecilia Mauldin, 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, June 15, 2019, 1:00 PM - 5:00 PM


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. 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 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.
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PROC REPORT: Clinical Reports from Top to Bottom
Jane Eslinger
Saturday, June 15, 2019, 1:00 PM - 5:00 PM


PROC REPORT is the most powerful procedure in Base SAS for generating reports. It is unique because it allows the programmer to use DATA step-like features, such as IF conditions and DO loops. This seminar will cover basic syntax, ordering and grouping values, creating new columns, using aliases, creating temporary variables, and transposing data. The seminar will also demonstrate how to use PROC REPORT to generate common clinical reports, such as demography tables and adverse event listings, as well as provide tips for style attributes to get the desired borders and page breaks.
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Techniques for Exchanging Data and Analytical Results between SAS® and Microsoft Excel
Vince DelGobbo
Saturday, June 15, 2019, 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.
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Advanced Clinical Graphs Using GTL
Sanjay Matange
Saturday, June 15, 2019, 1:00 PM - 5:00 PM


Many graphs used in the Health and Life Sciences domain and Clinical Research can be created using the SAS SG Procedures. These include the display of data and derived statistics aligned with the X or Y axis. However, many graphs need complex layouts and plot features to get the graph just right. To create such graphs, you will need to use the Graph Template Language (GTL).

This half-day presentation will show you how to create complex, multi-cell graphs using GTL. In this presentation we will review the basic features of GTL, including some advanced features that will help you make your graphs scalable and flexible for usage with different data. We will review the building of many complex graphs using multi-cell layouts, with axis aligned statistics using SAS 9.4 GTL features. We will review in detail how to create Forest Plot with Subgroups and custom headers, Survival Plots, Swimmer plots and some new combined graphs of tumor response and treatment history data.

Audience: Graph programmers
Required: Moderate to advanced SAS programming skills.
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OOP, Here We Go with DS2
Charu Shankar
Sunday, June 16, 2019, 8:00 AM - 12:00 PM


Object-oriented programming is the trendiest construct today with big data programming languages like Python in the news. The programming language of DS2 was introduced as part of the SAS® 9.4 release. The object-oriented programming constructs of DS2 are easily one of its best features. Many SAS programmers are familiar with Procedural programming. However, Object-oriented programming or OOP needs a switch of thinking to be able to understand the concepts. To help guide the OOP learner along the big data path to build a strong foundation of DS2, this seminar will cover the following points:
  1. help compare and contrast between the SAS data step and DS2.
  2. explain and demonstrate OOP concepts of methods, packages, and threads.
  3. show how DS2 represents an intersection between the SAS Data Step and ANSI:SQL:1999.
Ultimately the goal is for the learner to be able to walk away with a sufficient understanding of OOP DS2 so that they can confidently consider choosing it from the vast repertoire of SAS languages.
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Data Manipulations in R
Arthur Li
Sunday, June 16, 2019, 8:00 AM - 12:00 PM


A common job for a programmer is to perform data management, such as subsetting, combining, recoding, and reshaping data. These types of tasks can be easily achieved by using R software. In this seminar, in addition to reviewing the basic data management techniques, we will also learn more advanced techniques of writing user-defined functions, manipulating character data, and reviewing some of the useful functions from the dplyr packages for data manipulations.

Prerequisite: Having knowledge of the basics in R language, such as knowing different methods for extracting components of objects.
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Python Training for Statistical Programmers and Statisticians
Kevin Lee
Sunday, June 16, 2019, 8:00 AM - 12:00 PM


Python is one of the most popular language nowadays. Python can be used to build just about anything, and it is a great language for backend web development, data analysis, scientific computing, application development, especially machine learning and many more. Python is currently featured in 70% of introductory programming courses at US universities and the latest report from Forbes states that Python grew by more than 450 percent in 2017.

The seminar is intended for Statistical Programmers and Statisticians who are familiar with SAS programming. It is not easy for programmers and biostatisticians to learn new language alone. The seminar will provide basic concept and foundation of Python programming, and the seminar will provide its comparison and similarity with SAS programming. Therefore, Statistical Programmers and Statisticians have easier time to learn Python programming and understand how Python programming works.

After the seminar, programmers and statisticians will be able to learn the following:
  • Introduction of Python
  • Introduction of Jupyter notebook, the most popular Python editor
  • Data types in Python - String, Number, List, Dictionary, Array and Data Frame
  • Function (just like SAS macro)
  • Reading and writing external files (e.g., txt, csv, excel, SAS data sets, image)
  • Data Wrangling such sorting, merging, subseting, and transposing
  • Data Visualization - scatter plot, bar, histogram, Kaplan Meier curves
  • Machine Learning using Python
  • SDTM data set (TS, DM) development using Python

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FDA & PMDA Submission Data Requirements
David Izard
Sunday, June 16, 2019, 8:00 AM - 12:00 PM


The binding guidance documents requiring you to provide data and related documentation based on US 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 regulators. 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 outlook on submission data requirements for other global regulatory bodies would also enjoy this seminar.
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Introductory ADaM Dataset Development: ADSL, OCCDS, and BDS
Sandra Minjoe, Mario Widel
Sunday, June 16, 2019, 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.
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Data-Driven Design in SAS® and Python: Developing More Dynamic, Flexible, Configurable Software
Troy Hughes
Sunday, June 16, 2019, 1:00 PM - 5:00 PM


Students will receive a complimentary copy of the author’s 2018 book SAS® Data-Driven Development: From Abstract Design to Dynamic Functionality, a $40 value! The course follows the book’s outline and teaches data-driven techniques in which software customization, configuration, business rules, data models, data cleaning/validation, report style, and other dynamic elements are maintained in external data structures – not in the underlying code. Data-driven techniques allow software to adapt flexibly to various organizations, environments, and objectives. This design facilitates highly configurable (i.e., “codeless”) software whose functionality can be modified by changing only the associated control tables, configuration files, parameters, user-specified options, and other data structures.

Examples are demonstrated in both Base SAS 9.4 and Python 3.7, so the course is ideal for either SAS or Python developers seeking to expand their skills, as well as for developers looking for an introduction to either language. All students will walk away with an understanding of how data-driven design minimizes software maintenance and modification, as well as proven data-driven development techniques that can be immediately implemented.

Learn high-level data-driven design principles and methods:
  • Compare data-driven and code-driven software design
  • Identify and abstract dynamic elements
  • Build interoperable data structures (i.e., control data)
  • Perform quality control on data structures
  • Build dynamic programs/procedures/functions (including Python functions and SAS PROC FCMP)
Implement design principles through data-driven development techniques:
  • Clean, standardize, and categorize data in extract-transform-load (ETL) processes
  • Create quality control reports that rely on dynamic data dictionaries
  • Transform data using dynamic business rules and conditional logic
  • Create control tables that validate program/process success
  • Customize report style with cascading style sheets (CSS)

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Learning the SAS Macro Language
Arthur Li
Sunday, June 16, 2019, 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.

Prerequisite: Basic knowledge of SAS programming (such as creating SAS data sets and variables by using the DATA step)
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Analysis of Oncology Studies for Programmers and Statisticians
Kevin Lee
Sunday, June 16, 2019, 1:00 PM - 5:00 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 oncology study project effectively.

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 irRC (immune-related Response Criteria). The second sub type, Lymphoma study, usually follows Cheson. Lastly, Leukemia studies follow study specific guidelines (e.g., IWCLL for Chronic Lymphocytic Leukemia). The seminar will show how to use response criteria guidelines for data collections and response evaluation.

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) and Kaplan Meier Curves using SAS Procedures such as PROC LIFETEST and PHREG.

Finally, programmers and statistician will understand how to build end-to-end standards driven oncology studies from protocol, study sub-types, response criteria, data collection, SDTM, ADaM to analysis.
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R for Drug Development: An Introduction to the Tidyverse, Shiny & R Markdown
Phil Bowsher
Sunday, June 16, 2019, 1:00 PM - 5:00 PM


RStudio will be presenting an overview of the Tidyverse, Shiny and R Markdown for the R user community. 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 the Tidyverse for clinical data wrangling, how to build Shiny apps and R Markdown documents as well as visualizations using HTML Widgets for R. Immunogenicity assessments and other drug development examples will be reviewed and generated for each topic.

The Tidyverse is a coherent system of packages for data manipulation, exploration and visualization that share a common design philosophy. The workshop will provide an introduction to clinical data wrangling with R that includes an overview of the packages dplyr, magrittr, tidyr and ggplot2. Workshop examples will focus on applications in drug development to help maximize productivity for the main stages of a clinical workflow.

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 helps you turn your analyses into interactive web applications without requiring HTML, CSS, or JavaScript knowledge. An introduction to databases will be reviewed as well as R web APIs.

R Markdown is an authoring format that enables easy creation of dynamic documents, presentations, and reports from R. R Markdown documents help to support reproducible research and can be automatically regenerated whenever underlying R code or data changes. R Notebooks as well as various types of R Markdown output will be covered, including blogdown and bookdown.

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 and Crosstalk will be reviewed for implementing cross-widget interactions. Immunogenicity ADA and other visualizations will be generated in the workshop.
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Advanced ADaM Dataset Development: Beyond the ADaMIG
Sandra Minjoe, Mario Widel
Sunday, June 16, 2019, 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.
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The SDTMIG: Beyond the Basics
Kristin Kelly, Jerry Salyers, Fred Wood
Wednesday, June 19, 2019, 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 following:
  • When to create custom domains, and the need to follow established conventions
  • When to use Findings About rather than a custom Findings domain
  • Considerations around when to create Supplemental Qualifiers, and how to relate them back to parent domains via the most efficient IDVAR values
  • The use of Relative Timing Variables as updated in SDTMIG v3.2, and further detailed in SDTMIG v3.3
  • The use of CDISC Controlled Terminology rather than legacy data values
  • The use of variables in the SDTM, but not in domain models of the SDTMIG
  • Commonly misused variables
  • The creation of Trial Design datasets
  • Newer domains in SDTMIG v3.3, including the expansion of the RS domain to include clinical classifications and the expansion of the tumor domains to include non-tumor lesions

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Deep Dive into Electronic Submission Components for Regulatory Submission of Clinical Study Data
Prafulla Girase
Wednesday, June 19, 2019, 1:00 PM - 5:00 PM


A regulatory submission of clinical study data also needs to be accompanied by various other electronic submission (eSUB) components such as Define-XML, annotated CRF, study data reviewer’s guide, analysis data reviewer’s guide etc. This seminar will take a deep dive into each of these components and educate attendees about key contents, best practices and considerations during preparation of these components. For example, attendees will learn characteristics of a submission-ready annotated CRF (i.e. annotations, validated bookmarks/links, document properties etc.). It will also go over key considerations related to preparation of a whole eSUB package for a submission such as folder structure considerations, PDF validation practices, final package checklist, regulatory hand-off etc. The seminar will also cover few challenging scenarios that come up during eSUB preparation (e.g. submission of support files such as lookup tables).
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Living the Dream: A Practical Approach to Automating End to End Standards
Steve Kirby, Mario Widel
Wednesday, June 19, 2019, 1:00 PM - 5:00 PM


We have all (especially managers) dreamed of a process that seamlessly moves data from collection to analysis and submission. When first working to automate standards implementation, Sponsors are often tempted to strive for a comprehensive solution; but the practical challenges of End-to-End (E2E) standards-based automation (including compliance with diverse regulatory requirements) should not be underestimated.

In this seminar, the presenters will use examples to share how to launch an E2E standards initiative at your company. Additional examples will highlight how build out that proof of concept into a robust process that supports the analysis and submission of key safety data from clinical trials. At the end of this course, participants will leave ready (and able) to start building something useful.
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Spotfire Training for Statistical/Clinical Programmers
Bhavin Busa
Wednesday, June 19, 2019, 1:00 PM - 5:00 PM


“A picture is worth a thousand words” is a commonly used phrase in every industry where there is data involved! In the clinical industry, plotting data and presenting statistical information using graphics is not new. Until most recently, the traditional approach was to “look” at the static output without giving end users an ability to explore and drill-down further. With the rapidly increasing availability of data visualization and analytic tools, the industry landscape is shifting. End users now want to ‘see’ their data more interactively, identify trends, visualize the patient profiles and review results at a high-level while still being able to drill-down to get a complete picture.

In this half day seminar, we will present how a statistical/clinical programmer can leverage their data science and programming background to learn a new analytic tool - TIBCO Spotfire® to meet the current needs of the clinical team to have a more interactive TLFs. We will cover basic entry-level functionality within Spotfire, i.e. to import various data sources, generate interactive visuals, calculated values, limiting expression, filtering schema, trellis, and publishing dashboard. We will demonstrate how one can build clinical dashboards within Spotfire to review clinical data (e.g. demographic, adverse events, laboratory, vitals), generate study CSR TLFs and also publish it on the web! We will discuss how one can leverage standardized structured data (i.e. CDISC SDTM/ADaM) to build interactive dashboards and support end-to-end clinical data & TLF review efforts.

Audience level: Beginner
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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.


Bhavin Busa

Bhavin Busa, is a Director of Stats Prog. & Data Analytics at Vita Data Sciences (VDS). He is a thought leader in the areas of data standards, visualization & regulatory submissions. He is responsible for overseeing the data analytics group at VDS and helping multiple pharmaceuticals & biotechnology with their data visualization and statistical programming needs. He has over 14 years of experience working in the industry as a Statistical Programmer and is very passionate about leveraging standards & technology to expedite data review, analysis and submission processes.


Vince DelGobbo

Vince DelGobbo is a Senior Software Developer in the Platform Research and Development Division at SAS. His 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.


Jane Eslinger

Jane Eslinger is a Senior Technical Support Analyst at SAS Headquarters in Cary, North Carolina.  Jane enjoys supporting SAS customers in using ODS and Base SAS® procedures, with an emphasis on PROC REPORT.  Her SAS certifications include Advanced Programmer for SAS®9, SAS® Certified Data Scientist, and SAS® Certified Advanced Visual Business Analyst.  Jane has authored two books: The SAS® Programmer's PROC REPORT Handbook: Basic to Advanced Reporting Techniques and The SAS® Programmer's PROC REPORT Handbook: ODS Companion.  She has presented at numerous conferences and users’ groups across the US, including SAS Global Forum from 2015 to 2018.  Before joining SAS, Jane served as a statistician and statistical programmer in the social science and clinical research fields.  She earned her BS in Statistics from NC State University.


Prafulla Girase

Prafulla Girase has 18 years of experience in Biotech industry including experience of working as an electronic submission (eSUB) lead or co-lead on five NDA/BLA clinical data submission packages that are currently approved therapies in the market. He currently works as a Principal Analyst in Data Standards and Governance at Biogen where he is responsible for standards and SME support related to ADaM, analysis results, and eSUB. He is an active member of PhUSE and currently co-leading “Define-XML 2.0 Completion Guidelines” working group at PhUSE.


Troy Hughes

Troy has been a SAS practitioner for more than 20 years, has managed SAS projects in support of federal, state, and local government initiatives, and is a SAS Certified Base, Advanced, and Clinical Trials Programmer. In the past five years, he has authored more than 30 white papers and has given more than 70 presentations, hands-on workshops, and trainings at SAS conferences, including at SAS Global Forum, SAS Analytics Experience, WUSS, MWSUG, SCSUG, SESUG, and PharmaSUG. He is the author of two groundbreaking SAS books:
  • SAS® Data Analytic Development: Dimensions of Software Quality (2016)
  • SAS® Data-Driven Development: From Abstract Design to Dynamic Functionality (2018)
Troy has an MBA in Information Systems Management and holds additional certifications, including Project Management Professional (PMP), Risk Management Professional (PMI-RMP), Professional in Business Analysis (PMI-PBA), Agile Certified Professional (PMI-ACP), Certified Information Systems Security Professional (CISSP), Certified Secure Software Lifecycle Professional (CSSLP), ITIL Foundation, Certified ScrumMaster (CSM), Certified Scrum Developer (CSD), Certified Scrum Product Owner (CSPO), and Certified Scrum Professional (CSP). He is a US Navy veteran with two tours of duty in Afghanistan.


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 Solutions Consultant at SAS Institute, Health and Life sciences R&D. In this role, he helps customers implement software that supports data standards in the pharmaceutical industry. Prior to this role he was one of the developers of the SAS Clinical Standards Toolkit. Lex was also one of the Java 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 an active 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 32,000 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 Services group. Kristin has over 10 years of experience working in the pharmaceutical industry primarily focused on clinical data standards. Prior to joining Pinnacle 21, Kristin was an Associate Director at Merck in the Global Clinical Data Standards (GCDS) group. She has also worked as a consultant in Data Standards Consulting (DSC) for Accenture (formerly Octagon Research Solutions).  She provides guidance on CDISC 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.  She is an authorized CDISC instructor for SDTM.  She is a regular presenter at conferences including the CDISC Interchange, PharmaSUG and PhUSE.


Steve Kirby

Steve Kirby, JD, MS, is Associate Director of Statistical Programming at Reata Pharmaceuticals Inc. He has spent his last 10 years in the industry focused on optimizing the implementation of CDISC data standards. He is a member of the CDISC ADaM team, the ADaM PK sub-team and consistently presents at a variety of conferences.


Kevin Lee

Kevin Lee is Data Scientist, SAS expert, statistician, Machine Learning working group lead, corporate trainer and evangelist in new technology. Kevin currently works as Director of Data Science at Clindata Insight. Kevin loves Oncology studies, Machine Learning and new technology such as python, design thinking and cloud technology. 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 wants to innovate pharmaceutical industry with AI/Machine Learning technology, and he currently lead AI/Machine Learning working group and teach Machine Learning and Python programming especially for biometric department. Kevin has presented more than 70 papers at the various conferences including many oncology-related papers and Machine Learning based 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 and ODS Graphics. This includes the Graph Template Language (GTL) and the Statistical Graphics (SG) procedures. Sanjay has been with SAS for over 28 years, is co-author of four patents, author of four SAS Press books and author of Graphically Speaking, a blog on data visualization.


Sandra Minjoe

Sandra is a Senior Principal Clinical Data Standards Consultant at PRA Health Sciences, helping implement ADaM. She is the ADaM Team Lead, part of the ADaM Leadership Team (ALT) and reviews draft ADaM documents with a focus on the fundamental principle of traceability. Previously, Sandra led the ADaM sub-team for the Occurrence Data Structure (OCCDS) v1.0 and co-led the finalization of ADaMIG v1.1. An active ADaM team member since 2001, she proposed structures that later became ADSL and OCCDS. Sandra has worked in the pharma/biotech industry since 1993, at both sponsor and vendor organizations, in a variety of roles, including programmer, statistician, data manager, and filing team lead.


Jerry Salyers

Bio coming soon


Charu Shankar

SAS Senior Technical Training Specialist, Charu Shankar teaches by engaging with logic, visuals and analogies to spark critical thinking. She interviews clients to recommend the right SAS training. She is a frequent blogger for the SAS Training Post. When she’s not teaching technology, she is passionate about helping people come alive with yoga and is a food blogger. Charu has presented at over 100 SAS international user group conferences on topics related to SAS programming, SQL , DS2 programming, big data and Hadoop, tips and tricks with coding, new features of SAS and SAS Enterprise Guide.


Mario Widel

Mario Widel is an independent contractor. He has been involved in CDISC related activities since 2007.  In his current role, Mario focuses on process development for submission data and documentation. He is a member of the ADaM team, a CDISC authorized SDTM and ADaM instructor and has presented at numerous conferences including PharmaSUG, JSM, SAS Global Forum and PhUSE.


Fred Wood

Fred is Vice President for Consulting Services at TalentMine. He leads the Data Standards Consulting Group, and is an SDTM and SEND Implementation Advisor. He has been active in leading the development of CDISC standards since 1999, and is one of the principal contributors to the CDISC Study Data Tabulation Model (SDTM). Fred is a founding member of the SDS Team (1999), the SEND Team (2002), and the Medical Devices Team (2007), and has led or co-led these for many years; he currently serves on the Leadership Teams of all three. Fred served for more than fifteen years on the CDISC Technical Leadership Committee and five years on the CDISC Standards Review Council. He is currently a member of the CDISC Global Governance Group, which oversees the development and publication of all CDISC standards and documents.

Prior to joining TalentMine, Fred led the Data Standards Consulting Group within Accenture's Accelerated R&D Services for 11 years. This includes time as Vice President, Data Standards Consulting at Octagon Research Solutions, which was acquired by Accenture in 2012. Fred joined Octagon in 2006, coming from Procter & Gamble Pharmaceuticals, where he was the Global Data Standards Manager in the Clinical Data Management Department. This position was preceded by many years as a Senior Toxicologist at P&G, supporting Rx and OTC products. Fred has a Ph.D. and an M.S. from the University of Massachusetts in Amherst, and a B.S. from Springfield College in Springfield, Massachusetts.