An efficient Gehan-type estimation for the accelerated failure time model with clustered and censored data

Journal article

Fu, Liya, Yang, Zhuoran, Zhou, Yan and Wang, You-Gan. (2021). An efficient Gehan-type estimation for the accelerated failure time model with clustered and censored data. Lifetime Data Analysis. 27(4), pp. 679-709. https://doi.org/10.1007/s10985-021-09526-4
AuthorsFu, Liya, Yang, Zhuoran, Zhou, Yan and Wang, You-Gan
Abstract

In medical studies, the collected covariates contain underlying outliers. For clustered/longitudinal data with censored observations, the traditional Gehan-type estimator is robust to outliers in response but sensitive to outliers in the covariate domain, and it also ignores the within-cluster correlations. To take account of within-cluster correlations, varying cluster sizes, and outliers in covariates, we propose weighted Gehan-type estimating functions for parameter estimation in the accelerated failure time model for clustered data. We provide the asymptotic properties of the resulting estimators and carry out simulation studies to evaluate the performance of the proposed method under a variety of realistic settings. The simulation results demonstrate that the proposed method is robust to the outliers existing in the covariate domain and lead to much more efficient estimators when a strong within-cluster correlation exists. Finally, the proposed method is applied to two medical datasets and more reliable and convincing results are hence obtained.

Keywordscensored data; induced smoothing; robust
Year2021
JournalLifetime Data Analysis
Journal citation27 (4), pp. 679-709
PublisherSpringer
ISSN1380-7870
Digital Object Identifier (DOI)https://doi.org/10.1007/s10985-021-09526-4
Scopus EID2-s2.0-85109276771
Page range679-709
FunderNational Natural Science Foundation of China (NSFC)
Natural Science Foundation of Guangdong Province of China
Project of Educational Commission of Guangdong Province of China
Australian Research Council (ARC)
Publisher's version
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All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Online02 Jul 2021
Publication process dates
Accepted05 Jun 2021
Deposited12 Dec 2022
ARC Funded ResearchThis output has been funded, wholly or partially, under the Australian Research Council Act 2001
Grant ID11871390
12071305
11871411
2020B1515310008
2019KZDZX1007
DP160104292
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