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Short course : Yi Li (University of Michigan)
By EDT stat Actu

The EDT StatActu, together with KULeuven and the ARC “Imperfect data: From Mathematical Foundations to Applications in Life Sciences” is organising the following event:

Short course on

"Modeling Survival Outcomes with High Dimensional Predictors: Methods and Applications"


Yi Li,
University of Michigan


The Short Course will be held online

DATE :  
Monday July 5th, 2021
From 2:00 pm to 6:00 pm


Registration is free. To register, please send an email to
The link to the online presentation, together with its slides, will be circulated to registered participants the day before the short course.

In the era of precision medicine, survival outcome data with high-throughput predictors are routinely 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 intent 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:

05 July
14:00 - 18:00