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

ABSTRACT: 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 byprod

SUBMITTER: Li D 

PROVIDER: S-EPMC10259842 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature