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April 2005 Likelihood approach for marginal proportional hazards regression in the presence of dependent censoring
Donglin Zeng
Ann. Statist. 33(2): 501-521 (April 2005). DOI: 10.1214/009053604000001291

Abstract

In many public health problems, an important goal is to identify the effect of some treatment/intervention on the risk of failure for the whole population. A marginal proportional hazards regression model is often used to analyze such an effect. When dependent censoring is explained by many auxiliary covariates, we utilize two working models to condense high-dimensional covariates to achieve dimension reduction. Then the estimator of the treatment effect is obtained by maximizing a pseudo-likelihood function over a sieve space. Such an estimator is shown to be consistent and asymptotically normal when either of the two working models is correct; additionally, when both working models are correct, its asymptotic variance is the same as the semiparametric efficiency bound.

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Donglin Zeng. "Likelihood approach for marginal proportional hazards regression in the presence of dependent censoring." Ann. Statist. 33 (2) 501 - 521, April 2005. https://doi.org/10.1214/009053604000001291

Information

Published: April 2005
First available in Project Euclid: 26 May 2005

zbMATH: 1068.62100
MathSciNet: MR2163149
Digital Object Identifier: 10.1214/009053604000001291

Subjects:
Primary: 62G07
Secondary: 62F12

Rights: Copyright © 2005 Institute of Mathematical Statistics

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Vol.33 • No. 2 • April 2005
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