A flexible and robust method for assessing conditional association and conditional concordance.

ABSTRACT: When analyzing bivariate outcome data, it is often of scientific interest to measure and estimate the association between the bivariate outcomes. In the presence of influential covariates for one or both of the outcomes, conditional association measures can quantify the strength of association without the disturbance of the marginal covariate effects, to provide cleaner and less-confounded insights into the bivariate association. In this work, we propose estimation and inferential procedures for assessing the conditional Kendall's tau coefficient given the covariates, by adopting the quantile regression and quantile copula framework to handle marginal covariate effects. The proposed method can flexibly accommodate right censoring and be readily applied to bivariate survival data. It also f

SUBMITTER: Liu X 

PROVIDER: S-EPMC7045600 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature