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 once it opens in January 2020. Space is limited!

Saturday, May 9, 2020

Course Title (click for description) Instructor(s) (click for bio) Time
#11 Understanding Define-XML 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 Oncology Studies Seminar for Statistical Programmers and Statisticians Kevin Lee 1:00 PM - 5:00 PM

Sunday, May 10, 2020

Course Title (click for description) Instructor(s) (click for bio) Time
#21 FDA & PMDA Submission Data Requirements David Izard 8:00 AM - 12:00 PM
#22 CDISC ADaM – Implementation by Example Richann Watson 8:00 AM - 12:00 PM
#23 Python Programming Seminar for Statistical Programmers and Statisticians Part 1 of 2 Kevin Lee 8:00 AM - 12:00 PM
#24 Hands-On Data-Driven Design: Developing More Flexible, Reusable, Configurable SAS Software Troy Hughes 8:00 AM - 12:00 PM
#31 Deep Dive into Electronic Submission Components for Regulatory Submission of Clinical Study Data Prafulla Girase 1:00 PM - 5:00 PM
#32 R & Python for Drug Development Phil Bowsher 1:00 PM - 5:00 PM
#33 Python Programming Seminar for Statistical Programmers and Statisticians Part 2 of 2 Kevin Lee 1:00 PM - 5:00 PM
#34 Clinical Graphs Using SAS Sanjay Matange 1:00 PM - 5:00 PM

Wednesday, May 13, 2020

Course Title (click for description) Instructor(s) (click for bio) Time
#41 Express Yourself with Python in SAS Charu Shankar 1:00 PM - 5:00 PM
#42 SDTM: Beyond the Basics Fred Wood
& Jerry Salyers
1:00 PM - 5:00 PM
#43 Driving Miss Data: Data-Driven Techniques Richann Watson 1:00 PM - 5:00 PM
#44 Reproducible Computation at Scale with Drake: Hands-on Practice with a Machine Learning Project Will Landau 1:00 PM - 5:00 PM



Seminar Registration, Attendance, and Cancellation Policy

  1. You must register for a seminar via the PharmaSUG 2020 conference registration form online.
  2. You may cancel a seminar on or before May 1, 2020, and receive a full refund minus a $25 administration fee per cancelled seminar.
  3. You may add a seminar on or before May 1, 2020 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 May 1, 2020, 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 May 1, 2020, you MAY NOT SWAP seminars; however, a new seminar may be added depending on space and availability.
  6. There will be NO REFUNDS after May 1, 2020. 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 Niraj Pandya, Seminar Coordinators, at This email address is being protected from spambots. You need JavaScript enabled to view it..




Course Descriptions

Understanding Define-XML
Lex Jansen
Saturday, May 9, 2020, 1:00 PM - 5:00 PM


The CDISC Data Exchange Standards Team publishes several CDISC standards in an XML representation. These XML standards include the Operational Data Model (ODM) and several ODM extensions such as:
  • Define-XML
  • Controlled Terminology in XML (CT-XM
  • Dataset-XML
  • Analysis Results Metadata for Define-XML 2.
Define-XML 2.1 is a metadata standard used to describe any tabular dataset structure. The primary use case for Define-XML is to describe CDISC Study Data Tabulation Model (SDTM), Standard for Exchange of Nonclinical Data (SEND), and Analysis Data Model (ADaM) datasets for the purpose of submissions to regulatory authorities, like FDA and PMDA. However, Define-XML also serves as a metadata exchange mechanism for other parties seeking to exchange CDISC-modeled dataset structures as well as proprietary (non-CDISC) dataset structures. This seminar 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 then introduce the Define-XML standard and other CDISC XML based standards that are related to Define-XML, such as ODM XML, Controlled Terminology in ODM-XML and the Analysis Results Metadata extension for Define-XML 2.0.

We will then do a deep dive into the components that make up the metadata that is part of the Define-XML standard. This includes discussing the changes in the new Define-XML v2.1. The seminar will discuss best practices as well as commonly seen issues in Define-XML submissions.

A goal of the seminar will be to gain a better technical understanding of Define-XML and CDISC based XML standards that are relevant to Define-XML.

Pre-requisites: Familiarity with CDISC, and an interest in a technical presentation. Some knowledge of XML is beneficial, but not needed.
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PROC REPORT: Clinical Reports from Top to Bottom
Jane Eslinger
Saturday, May 9, 2020, 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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Oncology Studies Seminar for Statistical Programmers and Statisticians
Kevin Lee
Saturday, May 9, 2020, 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 four different sub types and their response criteria guidelines. The first sub type, Solid Tumor study, usually follows RECIST (Response Evaluation Criteria in Solid Tumor). The second sub type, Immunotherapy study, usually follows irRC (immune-related Response Criteria). The third 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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FDA & PMDA Submission Data Requirements
David Izard
Sunday, May 10, 2020, 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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CDISC ADaM – Implementation by Example
Richann Watson
Sunday, May 10, 2020, 8:00 AM - 12:00 PM


This course will provide a high-level overview of some of basic ADaM concepts. It is assumed that the attendee will have a fundamental knowledge of the different ADaM structures and principles. The primary focus of the course is to illustrate the implementation of some of the concepts found in both the ADaM Implementation Guide (ADaM IG) and the ADaM Structure for Occurrence Data (OCCDS) documents. Items covered include setting up ADSL for common trial designs and walking through the process of creating a basic data structure (BDS) starting from a simple BDS and building on it to structure a data set that will support one or more analyses. Additionally, the course will demonstrate how to implement some variables that are only found in OCCDS, such as the standardized MedDRA query (SMQ) and customized query (CQ) variables.
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Python Programming Seminar for Statistical Programmers and Statisticians Part 1 of 2
Kevin Lee
Sunday, May 10, 2020, 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 back-end web development, data analysis, scientific computing, machine learning and many more.

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 understand how Python programming works.

The morning seminar will cover basic Python programming. It is recommended for those who has a little or no experience in Python programming. It will help SAS programmers and statisticians how to start Python programming and how to use Jupyter Notebook (the most popular python platform).

Agenda for morning seminar: Python Programming Seminar - Basic
  • Introduction of Python for statistical programmers and statisticians
  • Jupyter notebook (Python programming platform) download and implementation
  • Python Variables Type: Number, String, Lists, Dictionaries, Arrays, Data Frames
  • Simple variable manipulation - If & For statements
  • Python Function development and comparison with SAS Macro
  • Import external Modules/Functions
  • Reading and writing external data (excel, SAS datasets, Images)
  • Data manipulation using Python
  • Introduction of NumPy and Array
  • Introduction of Pandas and DataFrame: DataFrame vs SAS datasets
  • Basic data manipulation – merge, sort, variables drop/addition
  • Create SDTM DM dataset using SAS raw datasets

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Hands-On Data-Driven Design: Developing More Flexible, Reusable, Configurable SAS Software
Troy Hughes
Sunday, May 10, 2020, 8:00 AM - 12:00 PM


Attend and receive a FREE copy of the author’s 500-page book SAS® Data-Driven Development: From Abstract Design to Dynamic Functionality, Second Edition. This HANDS-ON workshop installs the student as the new SAS consultant within Scranton, Pennsylvania’s, most infamous paper supply company — charged with improving software functionality and performance through data-driven software design. Navigate office intrigue and antics to gather software requirements, analyze hardcoded, legacy SAS programs, and refactor and improve software through data-driven design. Students can run all examples during the course using either SAS Display Manager or SAS University Edition. Come help Jim, Dwight, Phyllis, and Stanley sell more paper through higher quality data-driven software!

Data-driven design describes software in which configuration items, business rules, data validation rules, data models, report style, and other dynamic elements are maintained in external data structures – NOT in underlying code. Benefits include increased software flexibility, reusability, maintainability, readability, interoperability, extensibility, and configurability.

Topics include:
  • Compare preferred data-driven design with poorer quality hardcoded design
  • Build reusable procedures, functions, and call routines using SAS macros and PROC FCMP (aka, the SAS function compiler)
  • Demonstrate built-in and user-defined data structures (e.g., parameters, macro lists, arrays, control tables, data models, configuration files, data sets, Excel, CSV, CSS)
  • Use SAS components that support data-driven development (e.g., CALL EXECUTE, CNTLIN option in PROC FORMAT, SYSPARM option, SAS dictionary tables, SAS arrays, CSSSTYLE option in PROC REPORT)
  • Create color-coded, “traffic light” quality control reports that use dynamic data dictionaries to identify bad data and standardize good data
  • Configure the style (e.g., format, font, color scheme, graphics) of data products using user-defined SAS formats and CSS files

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Deep Dive into Electronic Submission Components for Regulatory Submission of Clinical Study Data
Prafulla Girase
Sunday, May 10, 2020, 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 Global considerations (i.e. FDA & PMDA) 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 author also plans to share general insights from his practical experience of attending face to face data format consultation meeting with PMDA.
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R & Python for Drug Development
Phil Bowsher
Sunday, May 10, 2020, 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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Python Programming Seminar for Statistical Programmers and Statisticians Part 2 of 2
Kevin Lee
Sunday, May 10, 2020, 1:00 PM - 5:00 PM


The afternoon seminar will cover more advanced Python programming. It is recommended for those who took morning seminar or last year’s Python course, or for those who have some knowledge, but want to learn more advanced Python programming. This seminar will also cover Machine Learning implementation using Python.

Agenda for afternoon seminar – Python Programming Seminar – Advanced with Machine Learning
  • Simple review of morning seminar
  • Metadata analysis (PROC CONTENT)
  • Advanced Programming – transpose, remove duplicate record, group-by
  • Statistical Analysis – Pair t-test, Fisher Exact Test, Survival Analysis
  • Data visualization - Scatter Plot, Histogram, Kaplan Meier Curves
  • Machine Learning Introduction – concepts and theory
  • Machine Learning Algorithm – Regression, Logistic Regression, Decision Tree
  • Deep Learning Algorithm – Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN)
  • Python Machine Learning modules – Sklearn, Tensorflow, Keras
  • Python Machine Learning workshop using image data
  • Through the seminars, programmers and statisticians will be able to achieve the following:
    • Understanding of Python programming
    • Jupyter Notebook download and experience
    • Real time Python coding exercise
    • Difference and similarity with SAS programming
    • Data Manipulation and analysis in Python
    • Machine Learning programming in Python

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    Clinical Graphs Using SAS
    Sanjay Matange
    Sunday, May 10, 2020, 1:00 PM - 5:00 PM


    Graphs for the analysis of Clinical Research data and for Health and Life Science applications can range from single-cell graphs, to classification panels to complex multi-cell graphs with many specific requirements. These graphs can be made by the judicious usage of the Statistical Graphics (SG) Procedures or the Graph Template Language (GTL). This seminar will teach you how to use the appropriate tool to create the graph you need using real world examples.

    In this ½ day presentation we will build single-cell graphs such as the Mean Change from Baseline, Survival Plot, Swimmer plot and Waterfall Charts using the SGPLOT procedure. We will build Panels of LFT Shift by Type and displays for Lab Values using the SGPANEL procedure. Finally, we will build complex multi-cell graphs for Most Frequent Adverse Events, and a combined display for Tumor Size Change + Duration of Treatment + Baseline Tumor Load. We will discuss the advanced features available in the SG procedures and GTL to help build these graphs and how these graphs can be easily extended and customized to your individual requirements.

    Audience: Graph programmers
    Required: Moderate SAS programming skills.
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    Express Yourself with Python in SAS
    Charu Shankar
    Wednesday, May 13, 2020, 1:00 PM - 5:00 PM


    With the entry of several new open source languages, users feel the need to learn them and understand the differences and commonalities between them. Learn how to express your data needs by writing a python panda. Learn how object oriented programming python stacks up with procedural SAS data step code, and do a compare and contrast. Learn to write native python code to perform data access and data manipulation tasks and submit it in your SAS session. Learn how computing principles such as 'order of first occurrence' play out in both languages. Bonus: BYOD (Bring your own device) seminar). Participants will learn the following:
    1. Introduction and Setting Up the Python Environment in SAS
    2. Basic Python Syntax and comparison with SAS datastep
    3. Python Data Manipulation

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    SDTM: Beyond the Basics
    Fred Wood, Jerry Salyers
    Wednesday, May 13, 2020, 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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    Driving Miss Data: Data-Driven Techniques
    Richann Watson
    Wednesday, May 13, 2020, 1:00 PM - 5:00 PM


    We have all been there. We write a program based on the data we have. Then, we get new data and we must update the program. Making these updates can be time consuming. Not only must you update the production version of the program, but someone must also update any associated validation or QC programs. Wouldn’t it be nice if there were ways around this? This is where data-driven techniques come in handy. Using detailed examples, you will learn how to write robust code that is ready to handle an unexpected bend in the road! This half-day course will cover advanced techniques such as: discovering and using information about data sets and variables even if it's not known in advance; generating dynamic formats that are based on the data instead of hard-coded into your program; using complex looping structures to control your program flow based on the data; building code on the fly, even from within a DATA step; and much more!
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    Reproducible Computation at Scale with Drake: Hands-on Practice with a Machine Learning Project
    Will Landau
    Wednesday, May 13, 2020, 1:00 PM - 5:00 PM


    Ambitious workflows in R, such as machine learning analyses, can be difficult to manage. A single round of computation can take several hours to complete, and routine updates to the code and data tend to invalidate hard-earned results. You can enhance the maintainability, hygiene, speed, scale, and reproducibility of such projects with the drake R package. drake resolves the dependency structure of your analysis pipeline, skips tasks that are already up to date, executes the rest with optional distributed computing, and cleanly stores the output for smooth retrieval. In this hands-on interactive workshop, you will use drake-powered automation to create and maintain a machine learning project.
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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.


    Jane Eslinger

    Jane Eslinger is a Senior Technical Training Consultant at SAS Headquarters in Cary, North Carolina. 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. Her SAS certifications include Advanced Programmer for SAS®9, SAS® Certified Data Scientist, and SAS® Certified Advanced Visual Business Analyst.


    Prafulla Girase

    Prafulla Girase has 19 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 an Associate Director 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 recently co-led “Define-XML 2.0 Completion Guidelines” working group at PhUSE.


    Troy Hughes

    Troy Martin Hughes 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 Advanced Programmer, SAS Certified Base Programmer, SAS Certified Clinical Trials Programmer, and SAS Professional V8. Since 2013, he has given more than 100 presentations, trainings, and hands-on workshops at SAS conferences, including at SAS Global Forum, SAS Analytics Experience, WUSS, SCSUG, SESUG, MWSUG, PharmaSUG, BASAS, and BASUG. He has authored two groundbreaking books that model software design and development best practices:
    • SAS® Data Analytic Development: Dimensions of Software Quality (2016)
    • SAS® Data-Driven Development: From Abstract Design to Dynamic Functionality, Second Edition (coming in 2020!)
    Troy has an MBA in information systems management as well as other credentials, including: PMP, PMI-RMP, PMI-PBA, PMI-ACP, CISSP, CSSLP, ITIL, CSM, CSD, CSPO, CSP-SM, and CSP-PO. 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. Past 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 Solution 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 33,000 links to papers that were presented at major SAS User Group conferences.


    Will Landau

    Will Landau earned his PhD in Statistics at Iowa State University in 2016, where his dissertation research applied Bayesian methods, hierarchical models, and GPU computing to the analysis of RNA-seq data. He currently works at Eli Lilly and Company, where he develops capabilities for clinical statisticians, and he is the creator and maintainer of the drake R package.

    Kevin Lee

    Kevin Lee is Data Scientist, statistician, Machine Learning working group lead, corporate/university trainer and evangelist in new technology. Kevin supports Pharmaceutical industry as AVP of AI/Machine Learning Consultant at Genpact. Among all the therapeutic area, Kevin always loves oncology studies, and he is an active supporter on oncology-specific standards such as CDISC Tumor datasets, control terminology and response criteria on each study type. Kevin wants to innovate pharmaceutical industry with AI/Machine Learning technology, and he currently leads AI/Machine Learning working group in PhUSE. He also teaches Machine Learning and Python programming in University and corporations. Kevin has presented about 100 papers at the various conferences including many oncology-related 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.


    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.


    Charu Shankar

    SAS Senior Technical Training Consultant, 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.


    Richann Watson

    Richann Watson is an independent statistical programmer and CDISC consultant based in Ohio. She has been using SAS since 1996 with most of her experience being in the life sciences industry. She specializes in analyzing clinical trial data and implementing CDISC standards. Additionally, she is a member of the CDISC ADaM team and various sub-teams. Richann loves to code and is an active participant and leader in the SAS User Group community. She has presented numerous papers, posters, and training seminars at SAS Global Forum, PharmaSUG, and various regional and local SAS user group meetings. Richann holds a bachelor’s degree in mathematics and computer science from Northern Kentucky University and master’s degree in statistics from Miami University.


    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.