Enhance your PharmaSUG experience by attending optional pre-conference training seminars taught by seasoned experts. Plan now to arrive in Shanghai early to take advantage of these affordable classes. There is an additional fee for taking a seminar. Half-day courses are only 871 RMB/$130 USD! Space is limited.
Friday, November 12, 2021
|Time||Course Title (click for description)||Instructor(s) (click for bio)|
|#1||8:30 AM - 12:30 PM||eCTD Overview and Implementation||Wei Feng & Alice Zhou|
|#2||8:30 AM - 12:30 PM||Deep Dive into Tidyverse, ggplot2 and Shiny with Real Case Applications in Drug Development (Part 1, Tidyverse Advanced and ggplot2 Intermediate)||John Wang & Emily Cheng|
|#3||1:30 PM - 5:30 PM||Deep Dive into Tidyverse, ggplot2 and Shiny with Real Case Applications in Drug Development (Part 2, ggplot2 Advanced and Shiny Advanced)||Emily Cheng & Jerry Wang|
|#4||1:30 PM - 5:30 PM||Python Programming for Reporting, Data Visualization and Machine Learning||Haiping Yu & Chengjun Lv|
Friday, November 12, 2021, 8:30 am - 12:30 pm
1. eCTD Overview and Implementation By Wei Feng & Alice Zhou
The electronic Common Technical Document (eCTD) is the electronic presentation of the CTD. It allows the electronic submission of dossier in the Common Technical Document (CTD) from applicant to regulator. It also provides a harmonized technical solution to implementing the CTD electronically.
This seminar is suitable for audiences who want to understand eCTD and intend to apply it in drug development and new drug submission activities in China. eCTD has been implemented in Europe and the United States for many years. As member of ICH Management Committee, NMPA has also been promoting the implementation of eCTD in China in recent years. From CTD to eCTD, the transforming is not only process change from paper to electronic. It requires drug research and development personnel to understand regulatory requirements on submission format & technical standard of eCTD submission, which could facilitate the successful and smooth China NDA filing in the near future.
2. Deep Dive into Tidyverse, ggplot2 and Shiny with Real Case Applications in Drug Development (Part 1, Tidyverse Advanced and ggplot2 Intermediate) By John Wang & Emily Cheng
This is an overview and case practices of Tidyverse, ggplot2 and Shiny for users of clinical study community at PharmaSUG China. No prior knowledge of R, RStudio or Shiny is needed. The full-day hands-on course is composed of three parts, how to do basic data wrangling using Tidyverse, create common clinical graphs using ggplot2, and build fancy interactive apps using Shiny. CDISC formatted datasets examples will be provided for each topic. Attendees need to get their hands dirty in class and instructors will be around for assistance. If you’re passionate to explore different methods to achieve your daily goals, this course will surely have benefits and a lot of fun.
The whole course is separated into two independent parts and attendees can join one of them or join both per need.
Part 1, Tidyverse Advanced and ggplot2 Intermediate
- R basics
- Get Started with Tidyverse
- Common Data Wrangling using Tidyverse vs SAS
- Supercharge your efficiency using Pipe %>%
- Get started with ggplot2
- ggplot2: Graphical elements, aesthetics, geometries
- Building a basic plot
|8:30 am – 10:30 am||Tidyverse Advanced||John Wang||2hr|
|10:30 am – 11:00 am||Tea Break|
|11:00 am – 12:30 pm||ggplot2 Intermediate||Emily Cheng||1.5hr|
Friday, November 12, 2021, 01:30 pm - 05:30 pm
3. Deep Dive into Tidyverse, ggplot2 and Shiny with Real Case Applications in Drug Development (Part 2, ggplot2 Advanced and Shiny Advanced) By Emily Cheng & Jerry Wang
This is an overview and case practices of Tidyverse, ggplot2 and Shiny for users of clinical study community at PharmaSUG China. No prior knowledge of R, RStudio or Shiny is needed. The full-day hands-on course is composed of three parts, how to do basic data wrangling using Tidyverse, create common clinical graphs using ggplot2, and build fancy interactive apps using Shiny. CDISC formatted datasets examples will be provided for each topic. Attendees need to get their hands dirty in class and instructors will be around for assistance. If you are passionate to explore different methods to achieve your daily goals, this course will surely have benefits and a lot of fun.
The whole course is separated into two independent parts and attendees can join one of them or join both their need.
Part 2, ggplot2 Advanced and Shiny Advanced
- ggplot2: quick go through of syntax
- Common plots in clinical trials and practices
- Get started with Shiny, Interactive visualization
- Shiny apps examples in clinical trials and practices
|13:30 pm – 15:00 pm||ggplot2 Advanced||Emily Cheng||1.5hr|
|15:00 pm – 15:30 pm||Tea Break|
|15:30 pm – 17:30 pm||Shiny Advanced||Jerry Wang||2hr|
|17:30 pm – 17:35 pm||Closing||Jerry Wang|
4. Python Programming for Reporting, Data Visualization and Machine Learning By Haiping Yu & Chengjun Lv
Python is a versatile open-source programming language which has become very popular nowadays in web development, data science and artificial intelligence, because of its active community, fueling of both academia and industry, and its unique way of simplicity and efficiency.
This seminar is intended for beginners and intermediate in Python programming. In the seminar, we will learn the Python programming features, practice tutorials in a web-based interactive development environment. After completing the seminar, we will understand “The Zen of Python”, know richness of Python packages and possibilities of Python application, and start to think about how to use Python to empower clinical research and development.
Topics for this seminar:
- Get started with the Python programming environment, the JupyterLab and Jupyter Notebook
- Python programming foundations, and comparison with other languages
- Data wrangling and analysis using Pandas, and comparison with SAS
- Data plotting and choices of Python visualization libraries
- Introduction of machine learning and example of application to real world problem
Ten years of work experience in the field of regulatory operations. Currently he is working for Johnson & Johnson as the Head of Regulatory Operations in China and North Asia, and worked for Bayer and Novartis in registration and operation related businesses prior to join J&J. Previously, he represented the company in eCTD testing organized by CDE and completed the testing work.
Worked for Novartis China over 20 years in Public Affairs, Business Development and Regulatory Affairs. Her current role is Head of Regulatory Operations and Compliance of Novartis GDD China RA. She started the role as RA Operations Group Manager in 2009 and set up the Ops & Compliance group in Novartis RA. Her team has been working with global RA Ops team in preparing China eCTD and mirrored global submission management process on NDA/CTA filing in China since 2013.
Director, Statistical Analysis, at dMed Biopharmaceutical Co., Ltd. He has 14+ years extensive statistical analysis experience in all phases of clinical trials and is familiar with different kinds of programming languages and system tools in clinical research. Before he joined dMed, he was the 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.
Statistical Programming Analyst at Janssen China R&D with 3 years programming experience in pharmaceutical industry supporting Oncology studies. Her expertise in R is data visualization, focusing on ggplot2. As R trainer, she delivered R base trainings to APAC programming colleagues. She also organized cross-sector R sharing seminar and facilitated workshops to build a dynamic R learning environment for R beginners. She enjoys exploring elegant solutions to create graphs, the process that taking flat data and bringing it to life.
Statistical Programming Lead, at Janssen China R & D. He has 9+ years of experiences in drug development industry. He joined Janssen in 2017 and accumulated extensive statistical programming and analysis experiences spanning phase I to phase III trials, including eSub, data pooling, FDA/EMA/and other healthy authority ad-hoc requests and publication support requests. Aside from projects, Jerry is pioneer in departmental R initiatives, his key accomplishments include Shiny APP development and several pilot projects with TLFs in R static and interactive mode. Jerry is passionate in coding with various languages, particularly R package and Shiny app development are his expertise. He finds the automation using R in our daily work is not only a fancy idea and R really lights up this solution for efficiency and cross-function connections, which make this R journey very promising and evolutionary in future of industry. Prior to Janssen, he worked at Pfizer R&D as Technical Supervisor, Clinical Programming.
Director, Technical Operation Lead of Clinical Data Management, at dMed Biopharmaceutical Co., Ltd. He has 15+ years of experiences in new drug R&D industry, he is enthusiastic about software development and data standards implementation in this area. He joined dMed in 2017, he and his team developed Clinical Metadata Repository (MDR) and many other programs to enable end-to-end automation of clinical data workflows. 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 working in data standards and data visualization team.
Manager, Clinical Data Management, at dMed Biopharmaceutical Co., Ltd. He has 10+ years of experiences in clinical data management, focuses on the application of clinical research-related software systems, as well as the development of automated tools, he is familiar with both SAS and Python, he has also involved in central monitoring software setups in a couple of years. He joined dMed in 2018, prior to that, he worked at Pfizer R&D China Center.