Threshold Regression with Censored Covariates

• Activity Number: 604
• Type: Contributed
• Date/Time: Thursday, August 7, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Jing Qian*+ and Folefac Atem and Rebecca Betensky
• Companies: University of Massachusetts and Harvard and Harvard
• Keywords: Dementia ; Linear regression ; Kaplan-Meier estimator ; Power ; Type I error

Abstract

We develop new threshold regression approaches for linear regression models with covariate subject to random censoring. Our method is motivated by a recent study of Alzheimer's disease risk factors, in which family history of dementia is a major risk factor of interest but subject to random censoring. Compared with existing methods, the proposed methods are simple but effective as they avoid complicated modeling in dealing with censored covariate values. In addition to estimating the regression coefficient of the censored covariate, the threshold regression methods can also be used to test whether the effect of the censored covariate is significant. We discuss the choice of optimal threshold which yields the most powerful test. The finite sample performance of the proposed methods are assessed through simulation studies. We also apply the method to the motivation example.