Variable selection for partially linear proportional hazards model with covariate measurement error

On corrected score approach for proportional hazards model with covariate measurement error.

In the presence of covariate measurement error with the proportional hazards model, several functional modeling methods have been proposed. These include the conditional score estimator (Tsiatis and Davidian, 2001, Biometrika 88, 447-458), the parametric correction estimator (Nakamura, 1992, Biometrics 48, 829-838), and the nonparametric correction estimator (Huang and Wang, 2000, Journal of th...

Variable Selection for Cox's Proportional Hazards Model and Frailty Model

Augustin, Schwarz: Cox’s Proportional Hazards Model under Covariate Measurement Error - A Review and Comparison of Methods

This contribution studies the Cox model under covariate measurement error Methods proposed in the literature to adjust for measurement error are reviewed The basic structural and functional approaches are discussed in some detail important modi cations and further develop ments are brie y sketched Then the basic methods are compared in a simulation study

Bayesian variable selection for proportional hazards models

The authors consider the problem of Bayesian variable selection for proportional hazards regression models with right censored data. They propose a semi-parametric approach in which a nonparametric prior is specified for the baseline hazard rate and a fully parametric prior is specified for the regression coefficients. For the baseline hazard, they use a discrete gamma process prior, and for th...

High-dimensional variable selection for Cox’s proportional hazards model

Variable selection in high dimensional space has challenged many contemporary statistical problems from many frontiers of scientific disciplines. Recent technological advances have made it possible to collect a huge amount of covariate information such as microarray, proteomic and SNP data via bioimaging technology while observing survival information on patients in clinical studies. Thus, the ...