|Title||Failure time regression with continuous informative auxiliary covariates.|
|Publication Type||Journal Article|
|Year of Publication||2015|
|Authors||Ghosh, Lipika, Jiancheng Jiang, Yanqing Sun, and Haibo Zhou|
|Journal||J Stat Distrib Appl|
|Date Published||2015 Feb|
In this paper we use Cox's regression model to fit failure time data with continuous informative auxiliary variables in the presence of a validation subsample. We first estimate the induced relative risk function by kernel smoothing based on the validation subsample, and then improve the estimation by utilizing the information on the incomplete observations from non-validation subsample and the auxiliary observations from the primary sample. Asymptotic normality of the proposed estimator is derived. The proposed method allows one to robustly model the failure time data with an informative multivariate auxiliary covariate. Comparison of the proposed approach with several existing methods is made via simulations. Two real datasets are analyzed to illustrate the proposed method.
|Alternate Journal||J Stat Distrib Appl|
|Original Publication||Failure time regression with continuous informative auxiliary covariates.|
|PubMed Central ID||PMC4651204|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 ES021900 / ES / NIEHS NIH HHS / United States
R37 AI054165 / AI / NIAID NIH HHS / United States
Failure time regression with continuous informative auxiliary covariates.