Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11408
Title: Hazard function estimation with cause-of-death data missing at random
Authors: Wang, Q
Dinse, GE
Liu, C 
Keywords: Imputation estimator
Inverse probability weighted estimator
Kernel estimator
Regression surrogate estimator
Issue Date: 2012
Publisher: Springer
Source: Annals of the Institute of Statistical Mathematics, 2012, v. 64, no. 2, p. 415-438 How to cite?
Journal: Annals of the Institute of Statistical Mathematics 
Abstract: Hazard function estimation is an important part of survival analysis. Interest often centers on estimating the hazard function associated with a particular cause of death. We propose three nonparametric kernel estimators for the hazard function, all of which are appropriate when death times are subject to random censorship and censoring indicators can be missing at random. Specifically, we present a regression surrogate estimator, an imputation estimator, and an inverse probability weighted estimator. All three estimators are uniformly strongly consistent and asymptotically normal. We derive asymptotic representations of the mean squared error and the mean integrated squared error for these estimators and we discuss a data-driven bandwidth selection method. A simulation study, conducted to assess finite sample behavior, demonstrates that the proposed hazard estimators perform relatively well. We illustrate our methods with an analysis of some vascular disease data.
URI: http://hdl.handle.net/10397/11408
ISSN: 0020-3157
EISSN: 1572-9052
DOI: 10.1007/s10463-010-0317-2
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