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Biometrika Advance Access originally published online on February 6, 2008
Biometrika 2008 95(1):233-240; doi:10.1093/biomet/asm089
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© 2008 Biometrika Trust

Miscellanea

Nonparametric estimation of cause-specific cross hazard ratio with bivariate competing risks data

Yu Cheng

Department of Statistics, University of Pittsburgh, 2717 Cathedral of Learning, Pittsburgh, Pennsylvania 15260, U.S.A yucheng{at}pitt.edu

Jason P. Fine

Department of Statistics and Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, 1300 University Avenue, Medical School Center, Madison, Wisconsin 53706, U.S.A. fine{at}biostat.wisc.edu

Received for publication 1 August 2006. Revision received 1 July 2007.
  Abstract

We propose an alternative representation of the cause-specific cross hazard ratio for bivariate competing risks data. The representation leads to a simple plug-in estimator, unlike an existing ad hoc procedure. The large sample properties of the resulting inferences are established. Simulations and a real data example demonstrate that the proposed methodology may substantially reduce the computational burden of the existing procedure, while maintaining similar efficiency properties.

Key Words: Bivariate hazard function • Cross ratio • Dependent censoring • Empirical processes theory • Rank correlation


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Y. Cheng, J. P. Fine, and K. Bandeen-Roche
Association analyses of clustered competing risks data via cross hazard ratio
Biostat., January 1, 2010; 11(1): 82 - 92.
[Abstract] [Full Text] [PDF]


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