Robust Rank Estimation of Accelerated Failure Time Model with Truncated Data
Date Issued
2007
Date
2007
Author(s)
Lee, Shih-Kuang
DOI
zh-TW
Abstract
In observational studies, truncated survival data are often collected according to a certain sampling criterions in. For example, in left-truncated data, event time is observed only if it is larger than truncation time. In a semiparametrically accelerated failure time model, rank-based methods developed for estimating regression parameters for truncated data require the assumption of quasi-independence that the event time and truncation time are independent under the observable region. Quasi-independence assumption may fail to hold in many situations. Therefore, we develop a robust U-statistic-based estimating equation to estimate the regression parameters without relying on the quasi-independence assumption. In our proposed method, comparable pairs for uncensored cases are established and artificial truncation as well as inverse-censoring-probability weighted technique are used to modify truncation and censoring effects. Our simulation shows that our proposed estimators are consistent when event time and truncation time are dependent. However, the naive estimator from the rank-based estimating equation requiring the quasi-independence assumption is biased when event time and truncation time are strongly correlated. We apply our proposed method to the channing house data and transfusion-related AIDS data. Since our proposed estimating equation is a nondifferentiable function with respect to regression parameters, we also compare two root-finding algorithms for nondifferentiable function, bisection and Nelder-Mead methods, in this thesis.
Subjects
加速事件時間模型
人工截切
相依截切
U統計量
Accelerated failure time model
Artificial truncation
Dependent truncation
U-statistic
SDGs
Type
thesis
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