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 29, 2019. The cost is USD $130/ Yuan 910 which is an additional fee from registration
Thursday morning – 8:30 am -12:30 pm
1. e-Submission Ready Data Package for New Drug Applications By James Wu
Why e-Submission ready data? What are e-Submission ready data? How to prepare the e-Submission ready data and When to prepare the e-Submission ready data?
I will explain why 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 presentation will cover all the details of e-submission ready data packages including datasets, CRF annotation, define file and reviewer’s guides, 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.
2. Tutorials of using Python Pandas , Dash and Flask to build visual analytics of clinical data By Haiping Yu
Python is a multi-purpose and open source programming language which has become very popular in data science due to its active community and rich libraries. Pandas is one of the most popular Python package for data science, it offers powerful, expressive and flexible data structures that make data manipulation and analysis easy. Dash is Python framework built on top of Flask, Plotly.js and React. It is a very powerful tool to quickly building web-based visualized analytical tools without having a lot of knowledge of web technologies. This tutorial is prepared for those who have experience of any programming language and are new to Python Pandas and Dash . In this tutorial, we will learn how to use Pandas to prepare the datasets from clinical trial, then use Dash to build app rendering interactive charts to support safety and efficacy review. After completing this tutorial, you will understand how productive it is to build a data visualization and business intelligence platform by using pure Python.
Thursday afternoon – 01:30 - 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. 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.
4. Advanced Multi Cell Clinical Graphs Using GTL By Sanjay Matange
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, some graphs need complex layouts and plot features to get the graph just right. To create such graphs, we need to use the Graph Template Language (GTL).
This ½ day presentation will show you how to create complex, multi-cell clinical graphs using GTL. In this presentation we will review the basic and advanced features of GTL that will help you make your graphs scalable and flexible for usage with different data. We will review the building of complex clinical graphs using multi-cell layouts, with axis aligned statistics using SAS 9.4 GTL features. We will review in detail how to create complex Forest Plot with Subgroups and custom headers, Survival Plots, Swimmer plots and some new multi-cell graphs for display of tumor response and treatment history data.
Audience: Graph programmers.Required: Moderate to advanced SAS programming skills.
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.
Haiping Yu has 14+ years of experiences in new drug R&D industry, especially in software development in drug discovery and clinical research. He joined dMed in 2017, he and his team developed many Python programs for automation, web services & applications, data Analysis and task queue, to fuel innovation at every level of clinical data management. Before he joined dMed, he was Manager, Technical Process Lead of Clinical Data Management at Pfizer China R&D Center since 2009, in this position, he was responsible for clinical trial data standards implementation and software technology innovation, wrote many programs using VBA and VB.NET. Prior to that, he was Research assistant at Chinese National Center of Drug Screening, Chinese Academy of Sciences. During this time he developed web applications for management of high-throughput compound screening and lab materials, in order to achieve this, he started to learning PHP and MySQL and soon became an expert. Haiping is the Associate Director, Techinical Operation Lead of Clinical Data Management at dMed Biopharmaceutical Co., Ltd.
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 an expert in the field of data visualization using SAS graphics software including the SG procedures and GTL. Sanjay worked at SAS for 29 years where he was responsible for the development of ODS Graphics. Sanjay is co-author of four patents and the author of four SAS Press books. Sanjay was the main author of Graphicalliy Speaking SAS blog for 8 years and is now the author of Make-A-Graph, blogs on data visualization.