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Nonparametric inference on bivariate survival data with interval sampling: association estimation and testing.

Hong Zhu1, Mei-Cheng Wang2

  • 1Division of Biostatistics, Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, Texas 75390, U.S.A.

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Summary
This summary is machine-generated.

This study introduces new methods for analyzing bivariate survival data, crucial for understanding disease onset and survival. The research provides tools to estimate associations and test for independence in interval-sampled data.

Keywords:
Bivariate survival dataDependenceInterval samplingKendall’s tauU-statistic

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Bivariate survival data with interval sampling are common in disease registries.
  • Understanding the relationship between consecutive failure events (e.g., disease onset, death) is vital.
  • Existing methods may not fully account for biases introduced by interval sampling.

Purpose of the Study:

  • To propose nonparametric methods for estimating the association between bivariate failure times using interval-sampled data.
  • To develop a nonparametric test for the quasi-independence assumption in bivariate survival analysis with interval sampling.
  • To provide practical illustrations using real-world datasets.

Main Methods:

  • Nonparametric estimation of association using Kendall's tau.
  • Weighting pairs by the inverse of their selection probability.
  • Developing a bivariate conditional Kendall's tau test for quasi-independence.

Main Results:

  • A novel nonparametric estimator for association between bivariate failure times was developed.
  • A new nonparametric test for quasi-independence was established.
  • Simulation studies confirmed the performance of the methods with moderate sample sizes.

Conclusions:

  • The proposed methods offer effective tools for analyzing bivariate survival data collected via interval sampling.
  • The study addresses key statistical challenges in survival analysis for biomedical applications.
  • The findings are applicable to life history studies and disease surveillance data.