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

Dongdong Li, X. Joan Hu (), Mary L. McBride and John J. Spinelli
Additional contact information
Dongdong Li: Harvard Medical School
X. Joan Hu: Simon Fraser University
Mary L. McBride: BC Cancer Agency
John J. Spinelli: BC Cancer Agency

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2020, vol. 26, issue 3, No 7, 573-602

Abstract: 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.

Keywords: Efficiency and robustness; Marginal distribution; Pseudo-likelihood estimation; Variable correlation; Variance estimation (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10985-019-09490-0

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