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Biometrika 2001 88(2):581-586; doi:10.1093/biomet/88.2.581
© 2001 by Biometrika Trust
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Miscellaneous

Marginal proportional hazards models for multiple event-time data

Yaning Yang1 and Zhiliang Ying2

1 Laboratory of Statistical Genetics, Rockefeller University, Box 192, 1230 York Avenue, New York, New York 10021, U.S.Ayyang{at}linkage.rockefeller.edu 2 Department of Statistics, Columbia University, 618 Mathematics Building, New York, New York 10027, U.S.A.zying{at}stat.columbia.edu

The Wei et al.(1989) semiparametric approach to the analysis of multiple event-time data assumes that each event time is related to covariates through a proportional hazards model with a completely unspecified baseline hazard function, but does not impose any constraint on the joint distribution of different event times. As a result of the order restriction, it is not clear whether or not event times can simultaneously satisfy their respective marginal proportional hazards assumption, while having continuous joint distribution. This leads to inability to conduct simulation studies. We resolve this issue by constructing parametric models for multiple event times with proper joint density functions and marginal proportional hazards. Simulation studies are reported that compare efficiencies of the method of Wei et al. (1989) and of the maximum likelihood approach.

Key Words: Efficiency; Hazard function; Marginal method; Maximum likelihood estimator; Multiple event times; Multivariate distribution; Proportional hazards model

Received January 1999. Revised December 2000


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