Web-Based Lectures




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.

Registration is closed.

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.



Title: Statistical Issues in Agent-Based Models for Risk Assessment
Presenter: David Banks, PhD, Department of Statistical Science, Duke University
Date and Time: Thursday, June 17, 2:00 p.m. – 4:00 p.m. Eastern Time
Sponsor: Section on Risk Analysis

Registration Deadline: Wednesday, June 16, at 12:00 p.m. Eastern time

Description:
Agent-based models (ABMs) are computational models used to simulate the actions and interactions of agents within a system. Usually, each agent has a relatively simple set of rules for how it responds to its environment and to other agents. These models are used to gain insight into the emergent behavior of complex systems with many agents, in which the emergent behavior depends upon the micro-level behavior of the individuals. ABMs are widely used in many fields, and this talk emphasizes the challenges that arise in the context various risk analyses (e.g., epidemics, invasive species, insurance). Relatively little work has been done on statistical theory for such models, this talk also points out some of those gaps and recent strategies to address them.

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, June 16, with the information to join the webinar and, if possible, a link to download and print a copy of the presentation slides.



Title: Survival Outcome Data with High Dimensional Predictors: Methods and Applications
Presenter: Dr. Yi Li, Professor of Biostatistics, University of Michigan
Date and Time: Thursday, June 24, 2021, 1:00 p.m. – 5:00 p.m. Eastern Time
Sponsor: Lifetime Data Science Section

Registration Deadline: Wednesday, June 23, at 12:00 p.m. Eastern time

Description:
In the era of precision medicine, survival outcome data with high-throughput covariates and predictors are often collected. These high dimensional data defy classical survival regression models, which are either infeasible to fit or likely to incur low predictability because of overfitting. This short course will introduce various cutting-edge methods that handle survival outcome data with high dimensional predictors. I will cover statistical principles and concepts behind the methods, and will also discuss their applications to the real medical examples.

Time permitting, I intend to cover the following topics.

  1. Survival analysis overview: basic concepts and models, e.g. Cox, Accelerated Failure Time (AFT), and Censored Quantile Regression (CQR) Models;
  2. Survival models with high dimensional predictors (p>n): Regularized methods and Dantzig selector;
  3. Survival analysis with ultra-high dimensional predictors (p>>n): Screening Methods, e.g, Principled sure independent screening (PSIS), Conditional screening, IPOD, Forward selection, etc;
  4. Inference for survival models with high dimensional predictors (p>n).
Audience only needs to have some basic knowledge of regression analysis and survival analysis. The relevant papers and software for this short course can be found in: http://www-personal.umich.edu/~yili/resindex.html.

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

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, June 23, with the information to join the webinar and, if possible, a link to download and print a copy of the presentation slides.