Thursday morning – 8:30 am -12:30 pm
1. Advanced Topics in R By Arthur Li
After programming in R in several years, you might wonder how to consolidate/shorten your lengthy R code, which was probably coded by repetitive copying and pasting, into a more elegant, compact, and easy-to-understand style. In this seminar, we will go over the selective advanced topics that will make your code-writing more efficient. These topics include efficiently processing text files, using regular expression to manipulate characters, object-oriented programming, and functional programming. The methods from this seminar will broaden your thinking of programming in R. Hopefully, grasping these tools will make you become an efficient programmer.
2. CDISC ADaM – Implementation by Example By Richann Watson
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.
Thursday afternoon – 01:30 - 05:30 pm
3. Python Programming for Data Science and Machine Learning By Haiping Yu
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 and has experience of other programming languages like SAS or C#. 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 JupyterLab and Jupyter Notebook
• Python programming foundations, and comparison with other languages
• Data Manipulation 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
4. 2-for1 advanced SQL & Macro techniques By Charu Shankar
PROC SQL & the Macro language are some of the most frequently used techniques by advanced SAS users. Learn to use advanced techniques in this unique 2-for-1 seminar where you will learn both languages. This practical session is culled from a SAS instructor’s years of experience in working with SAS helping SAS customers resolve their efficiency issues. The audience will be guided on benchmarking statistics for picking an efficient technique.
PROC SQL is the language of databases. It is a powerful query language that can sort, summarize, subset, join and print results all in one step. Users who are continuously improving their analytical processing will benefit from this Seminar.
Participants will learn the following elements to master PROC SQL:
1. The SQL syntax order & the logical query order in which PROC SQL processes queries
2. Manage metadata using dictionary tables
3. Join tables using join conditions like inner join and reflexive join
4. Summarize data using boolean operations
The SAS macro language is a very versatile and useful tool. It is often used to reduce the amount of regular SAS code and it facilitates passing information from one procedure to another procedure. Furthermore, we can use it to write SAS programs that are “dynamic” and flexible. In this seminar we will demonstrate how to create macro variables and how to write macro programs.
1. Use macro variables for text substitution
2. Use macro functions to alter text
3. Use macro programs to generate code
4. Learn 3 techniques to store a query in a Macro to reuse macro values over and over again
Arthur Li 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.
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.
Haiping Yu is the Director, Techinical 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. He joined dMed in 2017, he and his team developed Clinical Metadata Repository (MDR) to enable end-to-end automation of clinical data workflows, and many other programs to fuel innovation at every level of clinical development. 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. Prior to that, he was Research assistant at Chinese National Center of Drug Screening, Chinese Academy of Sciences. During this time, he started to learn programming with PHP and MySQL, and developed web applications for management of high-throughput compound screening and lab materials.
Charu Shankar is a SAS Senior Technical Trainer. She teaches by engaging with logic, visuals and analogies to spark critical thinking. She interviews users to recommend the right SAS training. SAS blogger, yoga teacher & chef, Charu also helps support users looking to land work using SAS through Linkedin. Charu has presented at over 100 SAS international user group conferences on SAS programming, SAS Viya, SAS Enterprise Guide, PROC SQL, Hadoop, Python, DS2 programming, tips and tricks, efficiencies.