Nonparametric estimation for length-biased and right-censored data
This paper considers survival data arising from length-biased sampling, where the survival times are left truncated by uniformly distributed random truncation times. We propose a nonparametric estimator that incorporates the information about the length-biased sampling scheme. The new estimator retains the simplicity of the truncation product-limit estimator with a closed-form expression, and has a small efficiency loss compared with the nonparametric maximum likelihood estimator, which requires an iterative algorithm. Moreover, the asymptotic variance of the proposed estimator has a closed form, and a variance estimator is easily obtained by plug-in methods. Numerical simulation studies with practical sample sizes are conducted to compare the performance of the proposed method with its competitors. A data analysis of the Canadian Study of Health and Aging is conducted to illustrate the methods and theory. Copyright 2011, Oxford University Press.
Year of publication: |
2011
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Authors: | Huang, Chiung-Yu ; Qin, Jing |
Published in: |
Biometrika. - Biometrika Trust, ISSN 0006-3444. - Vol. 98.2011, 1, p. 177-186
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Publisher: |
Biometrika Trust |
Saved in:
Online Resource
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