Quantile association for bivariate survival data

Ruosha Li, Yu Cheng, Qingxia Chen, Jason Fine

Research output: Contribution to journalArticle

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Abstract

Bivariate survival data arise frequently in familial association studies of chronic disease onset, as well as in clinical trials and observational studies with multiple time to event endpoints. The association between two event times is often scientifically important. In this article, we examine the association via a novel quantile association measure, which describes the dynamic association as a function of the quantile levels. The quantile association measure is free of marginal distributions, allowing direct evaluation of the underlying association pattern at different locations of the event times. We propose a nonparametric estimator for quantile association, as well as a semiparametric estimator that is superior in smoothness and efficiency. The proposed methods possess desirable asymptotic properties including uniform consistency and root-n convergence. They demonstrate satisfactory numerical performances under a range of dependence structures. An application of our methods suggests interesting association patterns between time to myocardial infarction and time to stroke in an atherosclerosis study.

LanguageEnglish (US)
Pages506-516
Number of pages11
JournalBiometrics
Volume73
Issue number2
DOIs
StatePublished - Jun 1 2017

Fingerprint

Survival Data
myocardial infarction
observational studies
Quantile
atherosclerosis
endpoints
chronic diseases
stroke
application methods
clinical trials
Association Measure
methodology
Uniform Consistency
Atherosclerosis
Observational Studies
Chronic Disease
Observational Study
Myocardial Infarction
Nonparametric Estimator
Dependence Structure

Keywords

  • Association
  • Bivariate survival data
  • Copula
  • Odds ratio
  • Quantiles

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

Cite this

Li, R., Cheng, Y., Chen, Q., & Fine, J. (2017). Quantile association for bivariate survival data. Biometrics, 73(2), 506-516. DOI: 10.1111/biom.12584

Quantile association for bivariate survival data. / Li, Ruosha; Cheng, Yu; Chen, Qingxia; Fine, Jason.

In: Biometrics, Vol. 73, No. 2, 01.06.2017, p. 506-516.

Research output: Contribution to journalArticle

Li, R, Cheng, Y, Chen, Q & Fine, J 2017, 'Quantile association for bivariate survival data' Biometrics, vol. 73, no. 2, pp. 506-516. DOI: 10.1111/biom.12584
Li R, Cheng Y, Chen Q, Fine J. Quantile association for bivariate survival data. Biometrics. 2017 Jun 1;73(2):506-516. Available from, DOI: 10.1111/biom.12584
Li, Ruosha ; Cheng, Yu ; Chen, Qingxia ; Fine, Jason. / Quantile association for bivariate survival data. In: Biometrics. 2017 ; Vol. 73, No. 2. pp. 506-516
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