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 Tidyverse, Shiny, and R Markdown with Applications in Drug Development By John Wang

This is an overview of Tidyverse, Shiny, and R Markdown 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 of flexible and powerful tools for data wrangling, statistical analysis, reproducible research and interactive visualizations. The hands-on course will include an overview of how to do basic data wrangling, build Shiny apps, generate R Markdown documents and visualizations for R. CDISC formatted datasets examples will be reviewed and generated for each topic.

Tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structure. It can import different source of data, making transformation, cleaning, and realizing visualizations.

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 of R, along with the open source ecosystem for Javascript visualization libraries, thereby allowing one to create highly customized applications. Shiny helps you to turn your analyses into interactive web applications without requiring of 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. A basic data review tool using CDISC datasets via Shiny will be covered. An introduction to databases via R will be reviewed along with how to connect Shiny apps to databases.

R Markdown is an authoring format that enables easy creation of dynamic documents, presentations, and reports based on R. It combines the core syntax of markdown with embedded R code chunks that are runnable, so their output can be included in the final document. R Markdown documents help to support reproducible researches and can be automatically regenerated whenever underlying R code or data changes.

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.


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.

John Wang is Associate Director, Statistical Analysis, at dMed Biopharmaceutical Co., Ltd. He has 10+ years extensive statistical analysis experience in all phases of clinical trials, is familiar with different kinds of programming languages and system tools in clinical research. Before he joined dMed, he was Manager of SAS Programming at Johnson & Johnson China since 2009. Prior to that, he was associate manager of SAS programming for four years at Global Research Services, LLC. He is Vice Chair and team lead for the SDTM group in C3C (China CDISC Coordinating Committee). He has very extensive experience using CDISC fundamental data standards such as CDASH, SDTM, ADaM, Controlled Terminology and define.xml. He became a CDISC authorized SDTM Instructor in early 2016.

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.