Title: Statistical Learning with Time-to-Event Outcome
Presenter: Dr. Noah Simon, University of Washington
Date and Time: Tuesday, Jan. 26th, 2021, 12:00 p.m. – 2:00 p.m. Eastern Time
Sponsor: Section on Lifetime Data Science
Registration Deadline: Monday, January 25, at 12:00 p.m. Eastern time
We will discuss contemporary methodologies for statistical learning with time-to-event outcome. We will discuss techniques for engaging with high dimensional data, as well as methods appropriate for data of more modest dimension with larger numbers of observations. We will look at modern software packages for statistical learning in these contexts, as well as validation of these predictive models. Time permitting, we will also touch on deep learning with time-to-event data.
During this session we aim to engage with loss-based estimation for time-to-event outcome; penalized regression; tree-based-survival models; as well as uses of cross-validation with kernel-weighted Kaplan-meier estimation to evaluate/calibrate a statistical-learning-based model. We will engage with these methodologies using a number of practical examples.
ASA Members: $20
Student ASA Member: $15
Each registration is allowed one web connection. Sound is received via audio streaming from your computer’s speakers.
Registration is closed.
Registered persons will be sent an email the afternoon of Wednesday, January 20 with the information to join the webinar and, if possible, a link to download and print a copy of the presentation slides.