Nonparametric estimation for length-biased and right-censored data

60Citations
Citations of this article
30Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

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. © 2011 Biometrika Trust.

Cite

CITATION STYLE

APA

Huang, C. Y., & Qin, J. (2011). Nonparametric estimation for length-biased and right-censored data. Biometrika, 98(1), 177–186. https://doi.org/10.1093/biomet/asq069

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free