Hazard function estimation with cause-of-death data missing at random |
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Authors: | Qihua Wang Gregg E Dinse Chunling Liu |
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Institution: | (1) Departamento de Matem?ticas, Facultad de Inform?tica, Universidade da Coru?a, 15071 A Coru?a, Spain;(2) Departamento de Matem?ticas, Escuela Universitaria Polit?cnica, Universidade da Coru?a, Campus de Serantes, 15405, Ferrol, A Coru?a, Spain; |
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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. |
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