Work

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

Year: 2019

Type: article

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 facilitates an estimator of the conditional concordance measure, namely, a conditional index, where the unconditional index is commonly used to assess the predictive capacity for survival outcomes. The proposed method is flexible and robust and can be easily implemented using standard software. The method performed satisfactorily in extensive simulation studies with and without censoring. Application of our methods to two real‐life data examples demonstrates their desirable practical utility.

Source: Statistics in Medicine

DOI: 10.1002/sim.8202

Authors: Xiangyu Liu, Jing Ning, Yu Cheng, Xuelin Huang, Ruosha Li

Institutions: The University of Texas Health Science Center at Houston, The University of Texas MD Anderson Cancer Center, University of Pittsburgh

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green