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Activity Number: 87 - Survival and Longitudinal/Clustered Data Analysis
Type: Contributed
Date/Time: Monday, August 9, 2021 : 10:00 AM to 11:50 AM
Sponsor: Biometrics Section
Abstract #319099
Title: Partly Interval-Censored Rank Regressions
Author(s): Sangbum Choi and Taehwa Choi* and Zhezhen Jin
Companies: Department of Statistics, Korea University and Department of Statistics, Korea University and Columbia University
Keywords: Accelerated lifetime; Empirical processes; Gehan statistics; Interval censoring; Survival analysis; Variance estimation

Interval censoring frequently occurs in many biomedical or health studies, and an interval containing exact failure time is observed from sequential monitoring. When exact failure time is also partly available, it is often referred as double and partly interval censoring, and it has been studied by several authors while they focused on transformation models. In this paper, we suggest the semiparametric accelerated failure time model under double and partly interval censoring. Gehan-type weighted estimating function is constructed by investigating comparable rank cases for each censoring indicator, and extension to the general class of estimating function can be simply derived. Furthermore, an efficient variance estimation procedure is considered to reduce computation time compared to existing resampling methods. Asymptotic behaviors of the method are established under mild conditions by using standard empirical processes. Simulation studies demonstrate the method is more efficient than existing Buckley-James methods in dominant cases. Two data examples are given to illustrate the practical usefulness of the method.

Authors who are presenting talks have a * after their name.

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