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15-11-2019

Multiple event times in the presence of informative censoring: modeling and analysis by copulas

Authors: Dongdong Li, X. Joan Hu, Mary L. McBride, John J. Spinelli

Published in: Lifetime Data Analysis | Issue 3/2020

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Abstract

Motivated by a breast cancer research program, this paper is concerned with the joint survivor function of multiple event times when their observations are subject to informative censoring caused by a terminating event. We formulate the correlation of the multiple event times together with the time to the terminating event by an Archimedean copula to account for the informative censoring. Adapting the widely used two-stage procedure under a copula model, we propose an easy-to-implement pseudo-likelihood based procedure for estimating the model parameters. The approach yields a new estimator for the marginal distribution of a single event time with semicompeting-risks data. We conduct both asymptotics and simulation studies to examine the proposed approach in consistency, efficiency, and robustness. Data from the breast cancer program are employed to illustrate this research.

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Title: Multiple event times in the presence of informative censoring: modeling and analysis by copulas
Authors: Dongdong Li
X. Joan Hu
Mary L. McBride
John J. Spinelli
Publication date: 15-11-2019
Publisher: Springer US
Published in: Lifetime Data Analysis / Issue 3/2020
Print ISSN: 1380-7870
Electronic ISSN: 1572-9249
DOI: https://doi.org/10.1007/s10985-019-09490-0

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