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

Xiangyu Liu1, Jing Ning2, Yu Cheng3

  • 1Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, Texas.

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|May 11, 2019
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Summary
This summary is machine-generated.

This study introduces a new method to measure the association between two outcomes, even when other factors (covariates) influence them. The approach provides clearer insights into conditional Kendall's tau and is useful for survival data analysis.

Keywords:
Kendall's tauassociation measurebivariate outcomespredictive capacityquantile regression

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Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • Measuring bivariate outcome association is crucial in scientific research.
  • Covariates can confound the assessment of bivariate associations.
  • Existing methods may not adequately address covariate effects in association measures.

Purpose of the Study:

  • To propose novel estimation and inferential procedures for conditional Kendall's tau coefficient.
  • To develop a method that accounts for influential covariates in bivariate outcome analysis.
  • To provide a flexible framework for analyzing bivariate survival data with censoring.

Main Methods:

  • Utilizing quantile regression and quantile copula frameworks.
  • Adopting methods to handle marginal covariate effects.
  • Incorporating procedures for right-censored bivariate survival data.

Main Results:

  • Developed an estimator for the conditional Kendall's tau coefficient.
  • Introduced an estimator for the conditional concordance measure (conditional C-index).
  • Demonstrated satisfactory performance in simulations and real-data applications.

Conclusions:

  • The proposed method offers a robust and flexible approach to assessing conditional associations.
  • The method effectively handles covariate effects and censoring in bivariate data.
  • The approach provides practical utility for analyzing complex scientific data, particularly in survival analysis.