Web-Based Lectures


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



Title: Learning from COVID-19 Data on Transmission, Health Outcomes, Interventions and Vaccination
Presenter: Xihong Lin, Department of Biostatistics and Department of Statistics Harvard University
Date and Time: Tuesday, March 23, 3:00 p.m. – 5:00 p.m. Eastern Time
Sponsor: Section on Statistical Consulting

Registration Deadline: Monday, March 22, at 12:00 p.m. Eastern time

Description:
COVID-19 is an emerging respiratory infectious disease that has become a pandemic. In this talk, I will first provide a historical overview of the epidemic in Wuhan. I will then provide the analysis results of 32,000 lab-confirmed COVID-19 cases in Wuhan to estimate the transmission rates using Poisson Partial Differential Equation based transmission dynamic models. This model is also used to evaluate the effects of different public health interventions on controlling the COVID-19 outbreak, such as social distancing, isolation and quarantine. I will present the results on the epidemiological characteristics of the cases. The results show that multi-faceted intervention measures successfully controlled the outbreak in Wuhan. I will next present transmission regression models for estimating transmission rates in USA and other countries, as well as factors including intervention effects using social distancing, test-trace-isolate strategies that affect transmission rates. I will discuss estimation of the proportion of undetected cases, including asymptomatic, pre-symptomatic cases and mildly symptomatic cases, the chances of resurgence in different scenarios, prevalence, and the factors that affect transmissions. I will also present the US county-level analysis to study the demographic, social-economic, and comorbidity factors that are associated with COVID-19 case and death rates. I will also present the analysis results of >500,000 participants of the HowWeFeel project on health outcomes and behaviors in US, and discuss the factors associated with infection, behavior, and vaccine hesitancy. I will provide several takeaways and discuss priorities.

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 Monday, March 22, with the information to join the webinar and, if possible, a link to download and print a copy of the presentation slides.



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.

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: 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).

Register

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: 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.