Loading...
An efficient Gehan-type estimation for the accelerated failure time model with clustered and censored data
Fu, Liya Yang, Zhuoran Zhou, Yan Wang, You-Gan
Fu, Liya
Yang, Zhuoran
Zhou, Yan
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.
Keywords
censored data, induced smoothing, robust
Date
2021
Type
Journal article
Journal
Lifetime Data Analysis
Book
Volume
27
Issue
4
Page Range
679-709
Article Number
ACU Department
Institute for Learning Sciences and Teacher Education (ILSTE)
Faculty of Education and Arts
Faculty of Education and Arts
Relation URI
Source URL
Event URL
Open Access Status
License
All rights reserved
File Access
Controlled