Education > Continuing Education

Web-Based Lectures

Current Webinar Offerings:


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 20, 2016 Global Sensitivity Analysis of Randomized Trials with Informative Drop-out
September 27, 2016 Parallel and Cluster Computing with R
October 6, 2016 Introduction to Stan - From Logistic Regression to PK/PD ODE Models
October 11, 2016 Basket Design of Phase III Confirmatory Trials
December 7, 2016 Evidence Generation Roadmap: The Role of Real World Data in the Drug Development Process




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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: Global Sensitivity Analysis of Randomized Trials with Informative Drop-out
Presenter: Daniel Scharfstein, John Hopkins University
Date and Time: Tuesday, September 20, 2016, 12:00 p.m. – 2:00 p.m. Eastern time
Sponsor: Biopharmaceutical Section

Registration Deadline: Friday, September 16, at 12:00 p.m. Eastern time

Description:
We present a formal methodology for conducting sensitivity analysis of randomized trials in which outcomes are scheduled to be measured at fixed points in time after randomization and some subjects prematurely withdraw from study participation. We motivate our methods by a placebo-controlled randomized trial designed to evaluate a treatment for bipolar disorder. We present a comprehensive data analysis and a simulation study to evaluate the performance of our methods. A software package entitled SAMON (R and SAS versions) that implements our methods is available at www.missingdatamatters.org.

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 Friday, September 16, 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.




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Title: Basket Design of Phase III Confirmatory Trials
Presenter: Cong Chen, Merck
Date and Time: Tuesday, October 11, 2016, 12:00 p.m. – 2:00 p.m. Eastern time
Sponsor: Biopharmaceutical Section

Registration Deadline: Friday, October 7, at 12:00 p.m. Eastern time

Description:
The discovery of numerous molecular subtypes of common cancers leads to the investigation of biomarkers potentially predictive of treatment effect of an experimental treatment in multiple histologies. However, the prevalence of a putative predictive biomarker within a histology is often low, which makes it challenging to enroll adequate number of patients in a conventional histology-based confirmatory trial. An alternative approach is to study patients with a common biomarker signature in a “basket” trial across multiple histologies. This study design has previously been used to explore experimental therapies with potentially transformative effects. We present a general design concept of a Phase 3 basket trial broadly applicable to any effective therapy. The trial is designed with scientific and statistical rigor to enable the approval of an experimental treatment in multiple tumor indications based on the outcome from a single study. Given the difficulty in indication selection, the basic idea is to prune the inactive indications at an interim analysis and pool the active indications in the final analysis. A critical statistical issue of the basket design is Type I error control for the pooled analysis after pruning. While pruning may be seen as cherry-picking which tends to inflate the Type I error, it also shares similarity with a binding futility analysis which tends to deflate the Type I error if all indications are pruned. The net impact of pruning is complicated. The use of different endpoints for pruning and pooling further complicates the issue. This webinar will provide statistical details on Type I error control for the general basket design concept under three sample size adjustment strategies after pruning. Power and sample size calculations are also provided. Comparisons are made to a straightforward design without pruning.

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, June 14, 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: Evidence Generation Roadmap: The Role of Real World Data in the Drug Development Process
Presenter: Alex Exuzides, ICON Commercialisation & Outcomes
Date and Time: Wednesday, December 7, 2016, 12:00 p.m. – 2:00 p.m. Eastern time
Sponsor: Biopharmaceutical Section

Registration Deadline: Monday, December 5, at 12:00 p.m. Eastern time

Description:
Despite the increasing demand for Real World Evidence (RWE) generation across a product’s lifecycle, there are challenges that need to be carefully addressed. These include the choice of the appropriate data sources and methodological approaches to structure and analyze such data to produce viable action plans. Learn how to effectively address these challenges. Using actual case studies attendees will understand the development of an effective strategy that incorporates epidemiology, health economics and outcome research, and uses already available Real World data. When such data are not available, learn how to develop the data to support an effective evidence generation plan.

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 Monday, December 5, with the access information to join the webinar and the link to download and print a copy of the presentation slides.