Biometrika Advance Access originally published online on February 6, 2008
Biometrika 2008 95(1):233-240; doi:10.1093/biomet/asm089
Miscellanea |
Nonparametric estimation of cause-specific cross hazard ratio with bivariate competing risks data
Department of Statistics, University of Pittsburgh, 2717 Cathedral of Learning, Pittsburgh, Pennsylvania 15260, U.S.A yucheng{at}pitt.edu
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.
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., October 13, 2009; (2009) kxp039v1. [Abstract] [Full Text] [PDF]
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