Work

A general semiparametric hazards regression model: efficient estimation and structure selection

Year: 2013

Type: article

Abstract: We consider a general semiparametric hazards regression model that encompasses the Cox proportional hazards model and the accelerated failure time model for survival analysis. To overcome the nonexistence of the maximum likelihood, we derive a kernel-smoothed profile likelihood function and prove that the resulting estimates of the regression parameters are consistent and achieve semiparametric efficiency. In addition, we develop penalized structure selection techniques to determine which covariates constitute the accelerated failure time model and which covariates constitute the proportional hazards model. The proposed method is able to estimate the model structure consistently and model parameters efficiently. Furthermore, variance estimation is straightforward. The proposed estimation performs well in simulation studies and is applied to the analysis of a real data set.

Source: Statistics in Medicine

DOI: 10.1002/sim.5885

Authors: Xingwei Tong, Liang Zhu, Chenlei Leng, Wendy Leisenring, Leslie L. Robison

Institution: National University of Singapore

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funder: National Cancer Institute

Award: Childhood Cancer Survivorship Study