Department of
presents
Dr. Lu Tian
Northwestern
University
Regularized Estimation for the
Accelerated Failure Time Model
Abstract
It is challenging to develop a stable regression model for predicting failure
time outcomes when the dimension of the covariates is big relative to the
sample size. Further complication arises due to the fact that failure time
responses are often not completely observed because of right censoring. In this
paper, we propose to couple the LASSO type regularization methods with the Gehan's rank based estimator in the setting of accelerated
failure time model to construct a stable and parsimonious prediction model.
Unlike the inverse probability weighting approach, the proposed estimators are
valid under the general noninformative censoring
assumption. We also propose an efficient numerical algorithm for obtaining the
entire regularization path to facilitate the adaptive selection of the tuning
parameter. We illustrate the proposed methods with an application to predict
the survival time of breast cancer patients based on a set of clinical
prognostic factors and collected gene signatures and evaluate their finite
sample performance through a simulation study.
Friday, October 5, 2007
3:35 - 4:35 pm
301 Riddick Hall
Refreshments will be served in the common area of 301 Riddick at 3:00 pm.