Evaluating Association Between Two Event Times with Observations Subject to Informative Censoring

• Published in: Journal of the American Statistical Association Vol. 118; no. 542; pp. 1282 - 1294
• Main Authors: Li, Dongdong, Hu, X. Joan, Wang, Rui
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis 03.04.2023; Taylor & Francis Ltd
• Subjects: Association parameter; Asymptotic methods; Asymptotic properties; Bivariate analysis; Breast cancer; breast neoplasms; byproducts; Copula model; data analysis; death; Marginal distribution; Pseudolikelihood estimation; Regression analysis; Semiparametric modeling; Simulation; Statistical methods; Statistics; Survival; survival rate; Association parameter; Pseudolikelihood estimation; Semiparametric modeling; Copula model; Marginal distribution
• ISSN: 0162-1459; 1537-274X; 1537-274X
• DOI: 10.1080/01621459.2021.1990766

This article is concerned with evaluating the association between two event times without specifying the joint distribution parametrically. This is particularly challenging when the observations on the event times are subject to informative censoring due to a terminating event such as death. There are few methods suitable for assessing covariate effects on association in this context. We link the joint distribution of the two event times and the informative censoring time using a nested copula function. We use flexible functional forms to specify the covariate effects on both the marginal and joint distributions. In a semiparametric model for the bivariate event time, we estimate simultaneously the association parameters, the marginal survival functions, and the covariate effects. A byproduct of the approach is a consistent estimator for the induced marginal survival function of each event time conditional on the covariates. We develop an easy-to-implement pseudolikelihood-based inference procedure, derive the asymptotic properties of the estimators, and conduct simulation studies to examine the finite-sample performance of the proposed approach. For illustration, we apply our method to analyze data from the breast cancer survivorship study that motivated this research. Supplementary materials for this article are available online.