Again, we are offering Four half-day seminars, two in the morning and two in the afternoon on the day before the conference on Thursday August 30, 2018. The cost – per seminar - is USD $130/ Yuan 910 which is an additional fee from registration. You must register for the conference in order to attend these seminars. Last date to cancel a seminar and/or switch to another seminar is June 30, 2018 with an administration fee of USD $25/ Yuan 175. Each seminar is about 4 hours and class material is provided.

Thursday August 30, 2018 – 08:30 AM – 12:30 PM

1. A Quick but Thorough Introduction in R By Arthur Li

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.

Intended Audience: Audiences from all industries with different job roles will be benefit by taking this seminar.

2. e-Submission Package with eCTD for NDA By James Wu

It is important to have e-submission ready data for any biostatitics and programming projects. The tabulation datasets and analysis datasets should follow not only the CDISC SDTM and ADaM, but also the eCTD and data specification technical document from regulatory agencies. This seminar will cover all the details of e-submission ready data packages including SDTM/ADaM datasets, CRF annotation, define file and reviewer’s guides, BIMO, as well as, the strategy and approaches to deal with the challenges for integrated and legacy studies.

Intended Audience: Audiences from all industries with different job roles will be benefit by taking this seminar.

Thursday August 30, 2018 – 01:30 PM – 05:30 PM

3. An Introduction to Shiny, R Markdown, and HTML Widgets for R With Applications in Drug Development by Phil Bowsher

RStudio will be presenting an overview of Shiny, R Markdown and HTML Widgets for the R user community at PharmaSUG China on Thursday, Aug 30th 2018. This is a great opportunity to learn and get inspired about new capabilities for creating compelling analyses with applications in drug development. No prior knowledge of R, RStudio or Shiny is needed. This short course will provide an introduction to flexible and powerful tools for statistical analysis, reproducible research and interactive visualizations. The hands-on course will include an overview of how to build Shiny apps, R Markdown documents and visualizations using HTML Widgets for R. Immunogenicity examples will be reviewed and generated for each topic.

Shiny is an open source R package that provides an elegant and powerful web framework for building web applications using R. Shiny combines the computational power of R with the interactivity of the modern web. Shiny allows users the flexibility of pulling in whatever package in R needed to solve a problem. There are no limits to the types of applications one can build, and no constraint on the visualizations that can be used. Developers get the benefit of an open source ecosystem for R, along with the open source ecosystem for Javascript visualization libraries, thereby allowing one to create highly custom applications. Shiny helps you turn your analyses into interactive web applications without requiring HTML, CSS, or JavaScript knowledge. This powerful concept allows you to easily deliver results as interactive data explorations instead of static reports to your stakeholders and non R users. Immunogenicity assessments via Shiny will be covered. An introduction to databases via R will be reviewed along with how to connect Shiny apps to databases. An introduction to creating web APIs with your existing R code will also be discussed.

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

The htmlwidgets package provides a framework for easily creating R bindings to JavaScript libraries. htmlwidgets work just like R plots except they produce interactive web visualizations. htmlwidgets can be used at the R console as well as embedded in R Markdown reports and Shiny web applications or saved as standalone web pages for ad-hoc sharing and hosting anywhere via email, Dropbox, Amazon S3, GitHub etc. Crosstalk will be reviewed for implementing cross-widget interactions (currently, linked brushing and filtering). Immunogenicity ADA visualizations will be generated in the workshop.

RStudio will be showcasing several compelling examples as well as learning resources. As part of the short course, some available drug development-related R Shiny apps and R Markdown reports will be illustrated.

4. Advanced Clinical Graphs Using SAS By Sanjay Matange

Analysis and reporting of the results of clinical research is made more effective when the information is presented in a graphical form as per Ohad Amit, Pharmaceutical Statistics, 2008. Graphical information is easier to understand for the investigators, helps to make comparisons and to suggest new directions of research.

This ½ day presentation will show you how to create complex graphs for the clinical domain. We will start with key concepts of the SGPLOT and SGPANEL procedures for the person who is not familiar with these procedures. We will then quickly turn to creating graphs frequently requested in the Health and Life Sciences domain using real world examples. This will include Survival Plots, Forest Plots, Adverse Event Timelines, Waterfall Charts for change in Tumor Size, Swimmer Plots and Lab Panels. We will also examine how to create a recently requested 3D Water Fall chart and discuss its pros and cons.

Annotation is an advanced tool for customization of graphs. We will show you how to add annotations to graphs using detailed examples. Finally, we will address claims of some R users that some graphs are harder with SAS by creating popular R graphs with SAS.

Audience: Advanced graph programmers Required: Basic SAS programming skills.

Biography

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.

James Wu has 20+ years of statistical programming experience in pharmaceutical industry. James managed several stat programming groups at Merck, Sanofi, MTDA and BDM. James served PharmaSUG as EC member, 2010 PharmaSUG conference chair, PharmaSUG China 2013 conference chair, and Philadelphia University over the past 10+ years as an adjunct instructor for the SAS Programming Certification Program. Currently James is the vice-president, Global Business Operation at BDM Consulting, Inc.

Phil Bowsher 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.

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.