Web-Based Lectures


April 8, 2021
April 15, 2021
April 22, 2021
April 29, 2021



Title: Causal Inference for Multiple Time-point (Longitudinal) Exposures
Presenter: Laura Balzer and Lina Montoya
Date and Time: Wednesday, April 7, 1:00 p.m. – 5:00 p.m. Eastern time

Registration Deadline: Tuesday, April 6, at 12:00 p.m. Eastern time

Description:
This workshop applies the Causal Roadmap to estimate the causal effects with multiple intervention variables, such as the cumulative effect of an exposure over time, controlled direct effects, and effects on survival-type outcomes with right-censoring. We will cover longitudinal causal models, identification in the presence of time-dependent confounding; and estimation of joint treatment effects using G-computation, inverse probability weighting (IPW), and targeted maximum likelihood estimation (TMLE) with Super Learner. During the workshop session, participants will work through the Roadmap using an applied example and implement these estimators with the ltmle R package. Prior training in causal inference in a single time-point setting is strongly recommended, but not required.

Registration:
ASA Members: $40
Student ASA Member: $25
Nonmembers: $65

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: Time Series: A First Course with Bootstrap Sampler
Presenter: Tucker McElroy, U.S. Census Bureau
Dates and Times: This will be a four-part webinar presentation from 10:00 a.m. – 12:00 p.m. Eastern time on these Thursdays in April: the 8th, 15th, 22nd, and 29th. Register just once to receive the access information prior to each presentation date. Each session will be recorded and the links will be sent to all registered attendees after each session in case one has a conflict with any of the presentation times.
Sponsor: Business and Economic Statistics Section

Registration Deadline: Wednesday, April 7, at 12:00 p.m. Eastern time

Description:
This course will be a reprise of the main topics of the book by the same name, by Tucker McElroy and Dimitris Politis. The intended audience includes statisticians with little or no knowledge of time series, but a general knowledge of statistics. Prerequisites include a course on linear models, a course on mathematical statistics (such concepts as bias, variance, and the Gaussian distribution), and a familiarity with linear algebra (the transpose, inverse, and eigen-values of a matrix). The aim is to cover basic concepts of time series analysis at a level suitable for those with a bachelor's or master's degree in statistics, while including a few non-standard concepts such as volatility filtering and time series bootstraps. A second aim is to incorporate coding in R of all concepts, methods, and examples.

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).

Registration is closed.

Access Information
Registered persons will be sent an email the afternoon of the day prior to each live presentation with the information to join the webinar.



Title: Randomization Tests for Weak Null Hypotheses
Presenter: Dr. Peng Ding, University of California, Berkeley
Date and Time: Wednesday, April 21, 12:00 p.m. – 2:00 p.m. Eastern time
Sponsor: Mental Health Statistics Section

Registration Deadline: Tuesday, April 20, at 12:00 p.m. Eastern time

Description:
The Fisher randomization test (FRT) is appropriate for any test statistic, under a sharp null hypothesis that can recover all missing potential outcomes. However, it is often sought after to test a weak null hypothesis that the treatment does not affect the units on average. To use the FRT for a weak null hypothesis, we must address two issues. First, we need to impute the missing potential outcomes although the weak null hypothesis cannot determine all of them. Second, we need to choose a proper test statistic. For a general weak null hypothesis, we propose an approach to imputing missing potential outcomes under a compatible sharp null hypothesis. Building on this imputation scheme, we advocate a studentized statistic. The resulting FRT has multiple desirable features. First, it is model-free. Second, it is finite-sample exact under the sharp null hypothesis that we use to impute the potential outcomes. Third, it conservatively controls large-sample type I error under the weak null hypothesis of interest. Therefore, our FRT is agnostic to the treatment effect heterogeneity. We establish a unified theory for general factorial experiments and extend it to stratified and clustered experiments. We also propose a general strategy for covariate-adjusted FRTs.

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.

Register

Access Information
Registered persons will be sent an email the afternoon of Tuesday, April 20, with the information to join the webinar and, if possible, a link to download and print a copy of the presentation slides.



Title: Polygenic Risk Prediction and Equitable Disease Prevention
Presenter: Nilanjan Chatterjee, PhD, Bloomberg School of Public Health, Johns Hopkins University
Date and Time: Thursday, April 22, 2:00 p.m. – 4:00 p.m. Eastern Time
Sponsor: Section on Risk Analysis

Registration Deadline: Wednesday, April 21, at 12:00 p.m. Eastern time

Description:
Recent discoveries from large scale genome-wide association studies (GWAS) have raised the prospect of using polygenic risk scores in routine health care setting for the prediction of future incidence of large variety of complex diseases. However, as GWAS studies to date have been heavily biased towards European origin populations, current polygenic risk scores often underperform in non-European populations and thus use of them can further exacerbate healthcare inequality. In this talk, I will review simple and advanced statistical methods for generating polygenic risk score using high-dimensional SNP data and describe theoretical characterizations of their expected performance, both in the population that underlies original studies and in a different population that is expected to have different distribution of allele frequencies and linkage disequilibrium (SNP-correlation). I will further describe novel Bayesian and machine learning based methods for building polygenic risk scores that can borrow information across GWAS studies of different ethnic groups, and thus makes best use of available data to generate more powerful polygenic risk scores across different ethnic groups. I will demonstrate potential utility for PRS in precision medicine using our recent studies on breast cancer.

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.

Register

Access Information
Registered persons will be sent an email the afternoon of Wednesday, April 21, with the information to join the webinar and, if possible, a link to download and print a copy of the presentation slides.



Title: Competing Frameworks and Methods for Competing Risks Data
Presenter: Douglas Schaubel, University of Pennsylvania Perelman School of Medicine
Date and Time: Friday, April 30, 12:00 p.m. – 2:00 p.m. Eastern Time
Sponsor: Lifetime Data Science Section

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

Description:
Competing risks data arise frequently in clinical and epidemiologic studies. Such data are characterized by a survival time that terminates due to one of several mutually exclusive causes. This webinar will cover the following: the two most commonly adopted frameworks for competing risks data; relevant estimands and estimators within each framework; the role of censoring as a competing risk; available modeling strategies; and causal inference in the competing risks setting. The main ideas will be illustrated through several real-data examples.

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.

Register

Access Information
Registered persons will be sent an email the afternoon of Thursday, April 29, with the information to join the webinar and, if possible, a link to download and print a copy of the presentation slides.