Literature DB >> 30076511

Parametric estimation of association in bivariate failure-time data subject to competing risks: sensitivity to underlying assumptions.

Jeongyong Kim1, Karen Bandeen-Roche2.   

Abstract

There has arisen a considerable body of research addressing the estimation of association between paired failure times in the presence of competing risks. In a 2002 paper, Bandeen-Roche and Liang proposed the conditional cause-specific hazard ratio (CCSHR) as a measure of this association and a parametric method by which to estimate it. The method features an interpretable decomposition of the CCSHR into factors describing the association between a pair's times to first failure among multiple failure causes and the association in pair members' propensities to fail due to a common cause. There were indications of sensitivity to model assumptions, however, in the 2002 work. Here we report a detailed study of the method's sensitivity to its parametric assumptions. We conclude that the method's performance is most sensitive to mis-specification of temporality in the association between pair members' first-failure times and of correlation between propensity to fail early or late and the propensity to fail of a specific cause. Implications for methods development are highlighted.

Entities:  

Keywords:  Conditional cause-specific hazard ratio; Conditional hazard ratio; Dependence; Frailty; Multivariate; Survival

Mesh:

Year:  2018        PMID: 30076511      PMCID: PMC6360142          DOI: 10.1007/s10985-018-9438-3

Source DB:  PubMed          Journal:  Lifetime Data Anal        ISSN: 1380-7870            Impact factor:   1.588


  10 in total

1.  Non-parametric estimation of bivariate failure time associations in the presence of a competing risk.

Authors:  Karen Bandeen-Roche; Jing Ning
Journal:  Biometrika       Date:  2008-03-01       Impact factor: 2.445

2.  A diagnostic for association in bivariate survival models.

Authors:  Min-Chi Chen; Karen Bandeen-Roche
Journal:  Lifetime Data Anal       Date:  2005-06       Impact factor: 1.588

3.  Nonparametric association analysis of exchangeable clustered competing risks data.

Authors:  Yu Cheng; Jason P Fine; Michael R Kosorok
Journal:  Biometrics       Date:  2008-05-11       Impact factor: 2.571

4.  Association analyses of clustered competing risks data via cross hazard ratio.

Authors:  Yu Cheng; Jason P Fine; Karen Bandeen-Roche
Journal:  Biostatistics       Date:  2009-10-13       Impact factor: 5.899

5.  Self-Consistent Nonparametric Maximum Likelihood Estimator of the Bivariate Survivor Function.

Authors:  R L Prentice
Journal:  Biometrika       Date:  2014-09       Impact factor: 2.445

6.  A semiparametric random effects model for multivariate competing risks data.

Authors:  Thomas H Scheike; Yanqing Sun; Mei-Jie Zhang; Tina Kold Jensen
Journal:  Biometrika       Date:  2010-03       Impact factor: 2.445

7.  The impact of heterogeneity in individual frailty on the dynamics of mortality.

Authors:  J W Vaupel; K G Manton; E Stallard
Journal:  Demography       Date:  1979-08

8.  Inferences on the association parameter in copula models for bivariate survival data.

Authors:  J H Shih; T A Louis
Journal:  Biometrics       Date:  1995-12       Impact factor: 2.571

9.  Frailty-based competing risks model for multivariate survival data.

Authors:  Malka Gorfine; Li Hsu
Journal:  Biometrics       Date:  2010-08-05       Impact factor: 2.571

10.  Semicompeting risks in aging research: methods, issues and needs.

Authors:  Ravi Varadhan; Qian-Li Xue; Karen Bandeen-Roche
Journal:  Lifetime Data Anal       Date:  2014-04-12       Impact factor: 1.588

  10 in total

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