Web-Based Lectures


Three-part webinar:
October 15, 2020,
October 22, 2020,
October 29, 2020

Three-part webinar:
October 26, 2020,
October 27, 2020,
October 28, 2020

Three-part webinar:
November 2, 2020,
November 9, 2020,
November 16, 2020

Three-part webinar:
November 3, 2020,
November 10, 2020,
November 17, 2020

Four-part webinar:
November 4, 2020, 
November 11, 2020,
November 18, 2020,
November 25, 2020



Title: Practical Considerations for Bayesian and Frequentist Adaptive Clinical Trials
Presenters: Peter Müller, Byron Jones, and Frank Bretz
Dates and Times: This will be a three-part webinar presentation from 12:00 p.m. – 2:00 p.m. Eastern time on these Thursdays in October: the 15th, 22nd, and 29th. Register just once to receive the access information prior to each presentation date.
Sponsor: Section on Bayesian Statistical Science

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

Description:
Clinical trials play a critical role in pharmaceutical drug development. New trial designs often depend on historical data, which, however, may not be accurate for the current study due to changes in study populations, patient heterogeneity, or different medical facilities. As a result, the original study design may need to be adjusted or even altered to accommodate new findings and unexpected interim results. Through carefully thought-out and planned adaptations, the right dose can be identified faster, patients can be treated more effectively, and treatment effects evaluated more efficiently. Reflecting the increasing importance and use of adaptive clinical trials, the International Council for Harmonisation (ICH) has recently tasked a working group to develop harmonized regulatory guidance for these studies in global drug development programs.

This three-part webinar presentation introduces various adaptive methods for Phase I to Phase III clinical trials using both, frequentist and Bayesian methods. Accordingly, we introduce different types of adaptive designs and illustrate practical considerations with case studies. Types of adaptive designs covered in this course include dose escalation/de-escalation and dose insertion designs, adaptive dose finding studies, trials with blinded and unblinded sample size re-estimation as well as adaptive designs for confirmatory trials with treatment or population selection at interim.

Registration:
ASA Members: $75
Student ASA Member: $50
Nonmembers: $125

This presentation will use the Zoom webinar platform. Each registration is allowed one web connection. Sound is received via audio streaming from your computer’s speakers.

Registration is closed.

Access Information
Registered persons will be sent an email the morning of each live presentation with the information to join the webinar and, if possible, a link to download and print a copy of the presentation slides.



Title: Biomarker Analysis in Clinical Trials Using R
Presenter: Nusrat Rabbee, Eisai, Inc.
Date and Time: Wednesday, Oct 21, 2020, 12:00 p.m. – 1:30 p.m. Eastern time
Sponsor: Biopharmaceutical Section

Registration Deadline: Monday, October 19, at 12:00 p.m. Eastern time

Description:
This course is based on “Biomarker Analysis in Drug Clinical Trials with R” - a book, which offers practical guidance to statisticians in the pharmaceutical industry on how to incorporate biomarker data analysis in clinical trial studies. We will discuss the appropriate statistical methods for evaluating biomarkers in different stages of clinical development: as pharmacodynamic, predictive, and surrogate biomarkers for delivering increased value in the drug development process. The topic of combining multiple biomarkers to predict drug response using machine learning is covered. We will discuss uses and abuses of biomarkers in drug development.

We will cover the models which will help students, researchers, and biostatisticians get started in tackling the hard problems of designing and analyzing clinical trials with biomarkers.

The course will highlight:

  • Analysis of pharmacodynamic biomarkers for lending evidence target modulation.
  • Design and analysis of trials with a predictive biomarker.
  • Framework for analyzing surrogate biomarkers.
  • Methods for combining multiple biomarkers to predict treatment response.
  • Writing Biomarker statistical analysis plan
  • Uses and abuses of biomakers
Presenter:
Nusrat Rabbee is a biostatistician and data scientist - who spent over 17 years in the pharmaceutical and diagnostics industry focusing on biomarker development. Currently she is the Head of Statistical Methodology and Data Science in Eisai, Inc. in Neurology. In this role she and her team is developing predictive models for disease progression risk and diagnosis - as well identifying key endpoints and biomarkers from clinical trial and real world data.

Nusrat creates innovative solutions to help companies accelerate drug and diagnostic development for patients. Her research interest lies in the intersection of statistics and computer science, as applied to clinical trials. She has extensive experience in clinical statistics, biomarker statistics and high-dimensional data science. She has co-discovered the RLMM algorithm for genotyping Affymetrix SNP chips and co-invented a high-dimensional molecular signature for cancer. She has taught statistics at UC Berkeley for 4 years. Nusrat has authored the book "Biomarker Analysis in Clinical Trials with R" with Taylor and Francis on April 2020.

Nusrat has a BA in Computer Science from Wellesley College; an MA in Statistics from UC Berkeley; and a PhD in Biostatistics from Harvard University. She completed the NSF VIGRE postdoctoral fellowship at UC Berkeley in Statistics.

Registration:
ASA Members: $20
Student ASA Member: $15
Nonmembers: $35

Each registration is allowed one web connection. Sound is received via audio streaming from your computer’s speakers. 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, October 19, with the information to join the webinar and, if possible, a link to download and print a copy of the presentation slides.




Title: If Your Data is Bad, Your AI initiatives are Doomed!
Presenter: Thomas C. Redman, “the Data Doc”
Date and Time: Tuesday, October 20, 1:00pm – 2:30 p.m. Eastern time
Sponsor: Section on Statistical Learning and Data Science

Registration Deadline: Monday, October 19, at 12:00 p.m. Eastern time

Description:
“Poor quality data is enemy number one to the profitable, widespread use of machine learning and artificial intelligence.” A scary claim! In this webinar, we will summarize why--after all, garbage-in, garbage-out has plagued analytics, data science, decision-making, and statistics for generations. Then we’ll explore what to do about it. As we’ll see, the quality demands of machine learning and artificial intelligence are incredibly steep, demanding a comprehensive and well-executed data quality program. We’ll leave plenty of time to discuss the implications for statisticians/data scientists.

Presenter:
Dr. Thomas C. Redman, “the Data Doc,” President of Data Quality Solutions, helps companies and people chart their courses to data-driven futures, with special emphasis on quality and data science. He has published dozens of articles—the most important article is “Data’s Credibility Problem” (Harvard Business Review, December 2013). Tom has a Ph.D. in Statistics and two patents. Prior to forming DQS, he led the Data Quality Lab at Bell Labs.

Registration:
ASA Members: $20
Student ASA Member: $15
Nonmembers: $35

Each registration is allowed one web connection. Sound is received via audio streaming from your computer’s speakers. 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, October 19, with the information to join the webinar and, if possible, a link to download and print a copy of the presentation slides.



Title: An Introduction to R for Non-Programmers
Presenter: William Lamberti
Dates and Times: This will be a three-part webinar presentation from 11:00 a.m. – 1:00 p.m. Eastern time on October: the 26th, 27th, and 28th. Register just once to receive the access information prior to each presentation date.

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

Description:
In this one day course, participants will be introduced to the basics of R. Basic data manipulation, cleaning, and data visualization will be discussed. Learning through examples will be greatly emphasized. This course is designed for individuals who have little to no experience with object oriented programming. Familiarity with programming in tools such as SAS will be helpful, but is not required. It is assumed that the baseline familiarity with data analysis tools have been primarily through a graphical user interface such as Excel.

Session 1
45 Minutes: R is a Big Fancy Calculator - Topics include vectors, matrices, and math operations.
5 Minute break
60 Minutes: Computing Things Quickly - Topics include functions and computing linear regression.
10 Minutes for Questions

Session 2
60 Minutes: Dealing with Data - Topics include objects, object types, and data frames.
5 Minute Break
60 Minutes: Plotting Data Part 1 - Topics include scatterplots, color, and titles.
10 Minutes for Questions

Session 3
60 Minutes: Plotting Data Part 2 - Topics include histograms and combining plots.
60 Minutes: R Packages for Visualization, Discussion, and Questions - Topics include R packages and other resources available. This time will also be allotted for specific questions from the participants on any of the content covered from the webinar. If there is extra time, additional topics such as ggplot2 may be introduced.

Registration:
ASA Members: $75
Student ASA Member: $50
Nonmembers: $125

This presentation will use the Zoom webinar platform. Each registration is allowed one web connection. Sound is received via audio streaming from your computer’s speakers.

Register

Access Information
Registered persons will be sent an email the morning of each live presentation with the information to join the webinar and, if possible, a link to download and print a copy of the presentation slides.



Title: Gaussian Process Modeling, Design and Optimization
Presenter: Robert Gramacy
Dates and Times: This will be a three-part webinar presentation from 11:00 a.m. – 1:00 p.m. Eastern time on these Mondays in November: the 2nd, 9th, and 16th. Register just once to receive the access information prior to each presentation date.

Registration Deadline: Thursday, October 29, at 12:00 p.m. Eastern time

Description:
This course details statistical techniques at the interface between geostatistics, machine learning, mathematical modeling via computer simulation, calibration of computer models to data from field experiments, and model-based sequential design and optimization under uncertainty (a.k.a. Bayesian Optimization). The treatment will include some of the historical methodology in the literature, and canonical examples, but will primarily concentrate on modern statistical methods, computation and implementation, as well as modern application/data type and size. The course will return at several junctures to real-word experiments coming from the physical, biological and engineering sciences, such as studying the aeronautical dynamics of a rocket booster re-entering the atmosphere; modeling the drag on satellites in orbit; designing a hydrological remediation scheme for water sources threatened by underground contaminants; studying the formation of supernova via radiative shock hydrodynamics; modeling the evolution a spreading epidemic. The course material will emphasize deriving and implementing methods over proving theoretical properties.

Registration:
ASA Members: $75
Student ASA Member: $50
Nonmembers: $125

This presentation will use the Zoom webinar platform. Each registration is allowed one web connection. Sound is received via audio streaming from your computer’s speakers.

Register

Access Information
Registered persons will be sent an email the morning of each live presentation with the information to join the webinar and, if possible, a link to download and print a copy of the presentation slides.



Title: Bayesian Thinking: Fundamentals, Regression, and Multilevel Modeling
Presenters: Jim Albert, Bowling Green State University and Jingchen (Monika) Hu, Vassar College
Dates and Times: This will be a three-part webinar presentation from 11:00 a.m. – 1:00 p.m. Eastern time on these Tuesdays in November: the 3rd, 10th, and 17th. Register just once to receive the access information prior to each presentation date.

Registration Deadline: Friday October 30, at 12:00 p.m. Eastern time

Description:
This series of webinar provides a general introduction to Bayesian modeling with a particular focus on regression and multilevel models. The use of the system R in Bayesian computation is described, including the programming of the Bayesian model and the use of different R tools to summarize the posterior. Special focus will be on the application of Markov chain Monte Carlo (MCMC) algorithms and diagnostic methods to assess convergence of the algorithms. The LearnBayes and rethinking R packages are used to illustrate MCMC fitting by the use of Gibbs sampling and Metropolis algorithms. Larger Bayesian models will be fit using JAGS and Stan and the accompanying runjags and rstan packages.

Part 1: November 3, 1-3 pm. Introduction to Bayesian Inference. Basic tenets of Bayesian thinking including construction of priors, summarization of the posterior to perform inferences, and the use of prediction distributions for prediction and model checking.

Part 2: November 10, 1-3 pm. Bayesian Regression. Implementation of Bayesian thinking for regression models for continuous and categorical response data.

Part 3: November 17, 1-3 pm. Bayesian Multilevel Modeling. Introduction to multilevel models as a flexible way of modeling regressions over groups.

Instructors: Jim Albert is Emeritus Professor at Bowling Green State University and Jingchen (Monika) Hu is Assistant Professor at Vassar College. They are coauthors of the text Probability and Bayesian Modeling published by Chapman and Hall in 2019.

Registration:
ASA Members: $75
Student ASA Member: $50
Nonmembers: $125

This presentation will use the Zoom webinar platform. Each registration is allowed one web connection. Sound is received via audio streaming from your computer’s speakers.

Register

Access Information
Registered persons will be sent an email the morning of each live presentation with the information to join the webinar and, if possible, a link to download and print a copy of the presentation slides.



Title: Weighting Methods in Surveys
Presenter: David Haziza, Department of Mathematics and Statistics, University of Ottawa
Dates and Times: This will be a four-part webinar presentation from 1:00 – 3:00 p.m. Eastern time on these Wednesdays in November: the 4th, 11th, 18th, and 25th. Register just once to receive the access information prior to each presentation date.
Sponsor: Survey Research Methods Section

Registration Deadline: Monday, November 2, at 12:00 p.m. Eastern time

Description:
Data collected by surveys are typically stored in a rectangular data file, each row corresponding to a sample unit (e.g., a business, a household, an individual, etc.) and each column corresponding to a survey variable (age, gender, income, etc.). Made available on the data file is a column of final weights. This set of weights constitutes a weighting system. The idea is to construct a unique weighting system that may be applied to all the survey variables. The typical process leading to the final weights involves three major stages. At the first stage, each unit is assigned a base weight, which is generally defined as the inverse of its inclusion probability. The base weights are then modified to account for unit nonresponse. At this stage, survey statisticians aim at reducing the nonresponse bias. Finally, the weights adjusted for nonresponse are further modified to ensure consistency between survey estimates and known population totals, a process often referred to as calibration. In some cases, the weights undergo a last modification trough weight trimming or weight smoothing procedures in order to improve the efficiency of survey estimates. This webinar series will provide the participants with an overview of the various stages.

Registration:
ASA Members: $75
Student ASA Member: $50
Nonmembers: $125

Each registration is allowed one web connection. Sound is received via audio streaming from your computer’s speakers. 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 morning of each live presentation with the information to join the webinar and, if possible, a link to download and print a copy of the presentation slides.



Title: ASA BIOP JSM Contributed Paper Award Webinar Series
Date and Time: Tuesday, November 24, 2020, 12:00 p.m. – 2:00 p.m. Eastern time
Sponsor: Biopharmaceutical Section

Registration Deadline: Monday, November 23, at 12:00 p.m. Eastern time

Presentation 1:
Title: Analysis of Multiple Outcome Measures with Applications to Disability Improvement in Multiple Sclerosis
Speaker: Wenting Cheng; Biogen

Abstract: In clinical trials, measurements or endpoints in various domains are assessed and usually combined to evaluate the totality of the treatment effect of a specific treatment. This strategy is common in neurological disease area where many patient performance assessments have been developed. The most common and simplest way to combine multiple measures is through constructing a composite endpoint when all outcome measures are binary or can be dichotomized, and an event occurrence is defined as if any of the component outcome measures achieves an event. Alternatively, the overall evaluation can be achieved analytically by analyzing each outcome measure separately, e.g., through multivariate regression. In literature, there is no systematic evaluation of these various approaches and performance comparisons. In this project, we first propose two general frameworks to combine multiple measures, a composite endpoint approach and a model-based approach. Statistical properties of the approaches are then evaluated using disability improvement in multiple sclerosis as an example. We finally illustrate our methodology through simulations and an application to a motivating clinical trial data.

Bio: Wenting Cheng is a Principal Biostatistician at Biogen, where she works as a clinical trial statistician of a Phase 2 trial of Opicinumab for treating multiple sclerosis. Wenting graduated with a PhD in Biostatistics from the University of Michigan, Ann Arbor.

Presentation 2:
Title: Variance estimation for the Kappa statistic in the presence of clustered data and heterogeneous observations
Speaker: Mary Ryan; University of California, Irvine

Abstract: We present methodology motivated by a controlled trial designed to validate SPOT GRADE, a novel surgical bleeding severity scale (Spotnitz et al., 2018). Briefly, the study was designed to quantify inter- and intra-surgeon agreement for characterizing the severity of surgical bleeds via a Kappa statistic. Multiple surgeons were presented with a randomized sequence of controlled bleeding videos and asked to apply the rating system to characterize each wound. Each video was shown multiple times to quantify intra-surgeon reliability, creating clustered data. In addition, videos within the same category may have had different classification probabilities due to changes in blood flow rates and wound sizes. In this work, we propose a new variance estimator for the Kappa statistic, for use in clustered data as well as heterogeneity among items within the same classification category. We then apply this methodology to data from the SPOT GRADE trial.

Bio: Mary Ryan is a fifth-year Statistics Ph.D. student at the University of California, Irvine, working under Dr. Daniel Gillen. Her current research interests are in group sequential testing, with applications in Alzheimer's Disease biomarker discovery. She earned her BA in Statistics from the University of Missouri in 2016, and her MS in Statistics from UCI in 2019.Association.

Registration:
ASA Members: $20
Student ASA Member: $15
Nonmembers: $35

Each registration is allowed one web connection. Sound is received via audio streaming from your computer’s speakers. 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, November 23, with the information to join the webinar and, if possible, a link to download and print a copy of the presentation slides.