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, the following topics will be covered:
- Survival analysis overview: basic concepts and models, e.g. Cox, Accelerated Failure Time (AFT), and Censored Quantile Regression (CQR) Models;
- Survival models with high dimensional predictors (p>n): Regularized methods and Dantzig selector;
- Survival analysis with ultra-high dimensional predictors (p>>n): Screening Methods, e.g, Principled sure independent screening (PSIS), Conditional screening, IPOD, Forward selection, etc;
- 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:
https://www.amstat.org/ASA/Education/Web-Based-Lectures.aspx
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.
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.
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Mimi Kim
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