Statistical inference for the accelerated failure time model under two-stage generalized case-cohort design

• Published in: Communications in statistics. Theory and methods Vol. 48; no. 24; pp. 6063 - 6079
• Main Authors: Cao, Yongxiu, Shi, Yueyong, Yu, Jichang
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 17.12.2019; Taylor & Francis Ltd
• Subjects: Accelerated failure time model; Computer simulation; Estimated augmented estimating equation; Estimators; Failure; Failure times; Generalized case-cohort; Kernel functions; Kernel method; Markov analysis; Parameter estimation; Random sampling; Statistical analysis; Statistical inference; Two-stage sampling
• ISSN: 0361-0926; 1532-415X
• DOI: 10.1080/03610926.2018.1528363

In this article, we propose a two-stage generalized case-cohort design and develop an efficient inference procedure for the data collected with this design. In the first-stage, we observe the failure time, censoring indicator and covariates which are easy or cheap to measure, and in the second-stage, select a subcohort by simple random sampling and a subset of failures in remaining subjects from the first-stage subjects to observe their exposures which are different or expensive to measure. We derive estimators for regression parameters in the accelerated failure time model under the two-stage generalized case-cohort design through the estimated augmented estimating equation and the kernel function method. The resulting estimators are shown to be consistent and asymptotically normal. The finite sample performance of the proposed method is evaluated through the simulation studies. The proposed method is applied to a real data set from the National Wilm's Tumor Study Group.