Hazard function estimation with cause-of-death data missing at random

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 subj...

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Bibliographic Details
Published in:Annals of the Institute of Statistical Mathematics 2012-04, Vol.64 (2), p.415-438
Main Authors: Wang, Qihua, Dinse, Gregg E., Liu, Chunling
Format: Article
Language:English
Subjects:
Applied mathematics
Asymptotic properties
Death
Economics
Errors
Estimating
Estimating techniques
Estimators
Finance
Hazards
Hypothesis testing
Insurance
Leukemia
Management
Mathematical analysis
Mathematics
Mathematics and Statistics
Missing data
Probability
Random variables
Regression analysis
Statistical methods
Statistics
Statistics for Business
Studies
Survival analysis
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Summary: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.
ISSN:0020-3157
1572-9052
DOI:10.1007/s10463-010-0317-2