Education > Continuing Education

Web-Based Lectures

Current Webinar Offerings:


July 7, 2016 Pushing the Frontier of TFL Automation and Dynamic Visualization with R/Shiny
July 14, 2016 Enabling Reproducibility in Statistical Analyses Using R Markdown
September 14, 2016 Best Practices in Data Analysis
September 15, 2016 An Overview of Statistical Considerations in Clinical Validation of Companion Diagnostic Devices of Precision Medicine
September 27, 2016 Parallel and Cluster Computing with R
October 6, 2016 Introduction to Stan - From Logistic Regression to PK/PD ODE Models




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Title: Pushing the Frontier of TFL Automation and Dynamic Visualization with R/Shiny
Presenters: Danni Yu, Eli Lilly and Company and Tuan Nguyen Sr., Eli Lilly and Company
Date and Time: Thursday, July 7, 2016, 11:00 a.m. - 12:30 p.m. Eastern time
Sponsor: Section for Statistical Programmers and Analysts

Registration Deadline: Tuesday, July 5, at 12:00 p.m. Eastern time

Description:
Producing TFLs can be a tedious, time-consuming, expensive and painful process. It has been challenging to automate until the arrival of new technologies. Shiny is a R tool for building web-based GUI for statistical analyses and is well-suited for automation. In addition, Shiny is built for dynamic visualization/analyses; this key feature allows us to interact with data dynamically, thus enabling proactive engagement with physicians/scientists. We will give some examples on how R/Shiny is used to lead innovation in drug development.

About the Presenters:
Danni Yu (Research Scientist, Oncology Biomarker Statistics, Eli Lilly and Company) and Tuan Nguyen (Sr. Research Scientist, Oncology Biomarker Statistics, Eli Lilly and Company). Dr. Yu and Dr. Nguyen and colleagues developed a comprehensive automation platform based on the R and Shiny tools that allows for fast, dynamic, scalable, inexpensive and reproducible visualization/analyses and generation of TFLs in drug development.

Registration Fees:
Member of the Section for Statistical Programmers and Analysts: $40
ASA Member: $65
Nonmember: $85

Each registration is allowed one web connection and one audio connection. Multiple persons are encouraged to view each registered connection (for example, by projecting the webinar in a conference room).

Register



Access Information
Registered persons will be sent an email the afternoon of Tuesday, July 5, with the access information to join the webinar and the link to download and print a copy of the presentation slides.




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Title: Enabling Reproducibility in Statistical Analyses Using R Markdown
Presenter: Eric Nantz, Eli Lilly and Company
Date and Time: Thursday, July 14, 2016, 11:00 a.m. - 12:30 p.m. Eastern time
Sponsor: Section for Statistical Programmers and Analysts

Registration Deadline: Tuesday, July 12, at 12:00 p.m. Eastern time

Description:
Reproducibility in statistical analyses has always been an important topic in many fields of statistics, but has gained even more attention in the last few years. In the past, the software tools enabling reproducibility in statistical programming required a large investment in time and effort. However, a new ecosystem around reproducibility has emerged within the R statistical language. In this talk, I will demonstrate specific examples using R in combination with rmarkdown and additional packages to make analysis reproducibility easy to set up and maintain throughout the life cycle of a project.

About the Presenter:
Eric Nantz is a senior research scientist supporting advanced analytics within the immunology unit at Eli Lilly and Company. Eric has utilized R in a wide variety of analyses involving clinical and novel biomarker data sets. Eric is also the creator and host of the R-Podcast, an audio podcast that provides valuable information for both new and experienced R users to accomplish data analyses.

Registration Fees:
Member of the Section for Statistical Programmers and Analysts: $40
ASA Member: $65
Nonmember: $85

Each registration is allowed one web connection and one audio connection. Multiple persons are encouraged to view each registered connection (for example, by projecting the webinar in a conference room).

Register



Access Information
Registered persons will be sent an email the afternoon of Tuesday, July 12, with the access information to join the webinar and the link to download and print a copy of the presentation slides.




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Title: Best Practices in Data Analysis
Presenter: Abhijit Dasgupta
Date and Time: Wednesday, September 14, 2016, 12:00 p.m. – 1:30 p.m. Eastern time
Sponsor: Section for Statistical Programmers and Analysts

Registration Deadline: Monday, September 12, at 12:00 p.m. Eastern time

Description:
The rise of data science has made acquiring, storing, manipulating, analyzing and reporting data much more commonplace. The data science pipeline has several points where errors can hide. Moreover, often the data analyst must come back to an analysis after a period of time and has to spend time to re-learn what he had earlier done. Several best practices have become established to catch and minimize errors, improve reproducibility, and allow the pipeline to run smoothly. I will address several best practices in data science using packages and code from the R ecosystem, including data storage, data classes and types, coding practices, error checking, modular programming, and reproducible reporting. At least a familiarity with R code will be expected.

About the Presenter
Abhijit Dasgupta is a data scientist and statistician with over 15 years’ experience in statistical modeling, machine learning methods and data visualization. He has a PhD in Biostatistics from the University of Washington and postdoctoral training at the National Cancer Institute, where he worked on genetic epidemiology, computational biology and analyses of microarrays and other genomic platforms for cancer research. He also helped organize the data community in DC, serving as an organizer of the Statistical Programming DC meetup and a board member of Data Community DC.

Registration Fees:
Member of the Section for Statistical Programmers and Analysts: $40
ASA Member: $65
Nonmember: $85

Each registration is allowed one web connection and one audio connection. Multiple persons are encouraged to view each registered connection (for example, by projecting the webinar in a conference room).

Register



Access Information
Registered persons will be sent an email the afternoon of Monday, September 12, with the access information to join the webinar and the link to download and print a copy of the presentation slides.




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Title: An Overview of Statistical Considerations in Clinical Validation of Companion Diagnostic Devices of Precision Medicine
Presenter: Meijuan Li, FDA
Date and Time: Thursday, September 15, 2016, 12:00 p.m. - 2:00 p.m. Eastern time
Sponsor: Biopharmaceutical Section

Registration Deadline: Tuesday, September 13, at 12:00 p.m. Eastern time

Description:
A key component of precision medicine is companion diagnostics that measure biomarkers e.g. protein expression, gene amplification or specific mutations. For example, most of the recent attention concerning molecular cancer diagnostics has been focused on the biomarkers of response to therapy, such as KRAS mutations in metastatic colorectal cancer, EGFR mutations in advanced Non-small cell lung cancer, and BRAF mutations in metastatic malignant melanoma. The presence or absence of these markers is directly linked to the response rates of particular targeted therapies with small-molecule kinase inhibitors or antibodies. Therefore, testing for these markers has become a critical step in the target therapy of the above-mentioned tumors. A companion diagnostic device is essential for the safe and effective use of a corresponding therapeutic product. The validation of a new companion diagnostic device includes both measurement and clinical validation as it is intended to be used. In this webinar we aim to cover the following important aspects of companion diagnostic device clinical validation (1) different indications for use and study designs (2) companion diagnostic device clinical performance measures (3) companion diagnostic device bridging study (4) follow-on companion diagnostic device (5) the impact of companion diagnostic device measurement performance on clinical validation of precision medicine including samples size calculation.

Registration Fees:
Biopharmaceutical Section Members: $0
ASA Members: $59
Nonmembers: $74

Each registration is allowed one web connection and one audio connection. Multiple persons are encouraged to view each registered connection (for example, by projecting the webinar in a conference room).

Register



Access Information
Registered persons will be sent an email the afternoon of Tuesday, September 13, with the access information to join the webinar and the link to download and print a copy of the presentation slides.




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Title: Parallel and Cluster Computing with R
Presenter: Elizabeth Byerly
Date and Time: Tuesday, September 27, 2016, 12:00 p.m. – 1:30 p.m. Eastern time
Sponsor: Section for Statistical Programmers and Analysts

Registration Deadline: Friday, September 23, at 12:00 p.m. Eastern time

Description:
Parallel programming is an essential tool for computationally intensive statistical methods. This webinar focuses on the basics of parallel programming in R and includes first principles of cluster computing. We will define parallelizable problems with examples, introduce base R functions for parallel programming, and walk through configuring multiple systems to communicate and share parallel operations (cluster computing). Attendees should expect to leave knowing how to adapt functions to run in parallel, including how to distribute data and retrieve outputs, and the requirements to build a cluster computing system. The webinar's content will require no advance preparation and is appropriate for analysts who are intermediate R programmers.

About the Presenter Elizabeth Byerly is a Data Systems Architect at Summit Consulting, developing and supporting the firm's parallel computing and cloud-based analytical platforms. She is a Linux Foundation Certified System Administrator and has been working for two years with distributed systems for statistical analysis.

Registration Fees:
Member of the Section for Statistical Programmers and Analysts: $40
ASA Member: $65
Nonmember: $85

Each registration is allowed one web connection and one audio connection. Multiple persons are encouraged to view each registered connection (for example, by projecting the webinar in a conference room).

Register



Access Information
Registered persons will be sent an email the afternoon of Friday, September 23, with the access information to join the webinar and the link to download and print a copy of the presentation slides.




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Title: Introduction to Stan - From Logistic Regression to PK/PD ODE Models
Presenter: Sebastian Weber, Novartis Pharma AG
Date and Time: Thursday, October 6, 2016, 10:00 a.m. - 12:00 p.m. Eastern time
Sponsor: Biopharmaceutical Section

Registration Deadline: Tuesday, October 4, at 12:00 p.m. Eastern time

Description:
The Stan project is in development since 2011 and aims to enable efficient Bayesian inference. This tutorial will focus on the foundations of Stan, introduce the Stan modeling language, explain how to do Bayesian inference with Stan and finally address best practices. These will be introduced using examples of increasing complexity ranging from logistic regression to non-linear population pharmacokinetic/pharmacodynamic ODE models which will demonstrate the scalability and flexibility of Stan. Stan's key feature is the Hamiltonian MCMC sampler which is different than the various established flavors of Bayesian inference Using Gibbs Sampling (BUGS), such as WinBUGS, OpenBUGS, and JAGS. To fully exploit the advantages of Hamiltonian MCMC, participants will be briefly introduced to the foundations of Hamiltonian MCMC. After these more theoretical aspects, the Stan modeling language will be introduced. The Stan modeling language is inspired by the BUGS family such that BUGS users can quickly adopt Stan. Most importantly, participants will be taught best practices to write efficient Stan models. This will include how to debug Stan models easily and what to consider in order to expedite Stan models. These best practices will be presented using examples of increasing complexity. The examples presented will be run using the R package rstan.

Registration Fees:
Biopharmaceutical Section Members: $0
ASA Members: $59
Nonmembers: $74

Each registration is allowed one web connection and one audio connection. Multiple persons are encouraged to view each registered connection (for example, by projecting the webinar in a conference room).

Register



Access Information
Registered persons will be sent an email the afternoon of Tuesday, October 4, with the access information to join the webinar and the link to download and print a copy of the presentation slides.