alexa Accelerated Failure Time Models with Auxiliary Covariates
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
Open Access

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Research Article

Accelerated Failure Time Models with Auxiliary Covariates

Kevin Granville and Zhaozhi Fan*

Department of Mathematics and Statistics, Memorial University, St. John’s, A1C 5S7, Newfoundland, Canada

*Corresponding Author:
Zhaozhi Fan
Department of Mathematics and Statistics
Memorial University
St. John’s, A1C 5S7
Newfoundland, Canada
Tel: 1-709-864-8076
Fax: 1-709-864-3010
E-mail: [email protected]

Received date: August 17, 2012; Accepted date: September 20, 2012; Published date: September 25, 2012

Citation:Granville K, Fan Z (2012) Accelerated Failure Time Models with Auxiliary Covariates. J Biom Biostat 3:152. doi:10.4172/2155-6180.1000152

Copyright: © 2012 Granville K, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

 

Abstract

In this paper we study semi-parametric inference procedure for accelerated failure time models with auxiliary information about a main exposure variable. We use a kernel smoothing method to introduce the auxiliary covariate to the likelihood function. The regression parameters are then estimated through maximization of the estimated likelihood function. A consistent estimator of the variance of the estimator of the regression coefficients is proposed. Simulation studies show that the efficiency gain is remarkable when compared to just using the validation sample. The method is applied to the PBC data from the Mayo Clinic trial in primary biliary cirrhosis as an illustration.

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