Regression Estimation Using Multivariate Failure Time Data and a Common Baseline Hazard Function Model |
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Authors: | Cai Jianwen Prentice Ross L |
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Institution: | (1) The University of North Carolina at, Chapel Hill;(2) Fred Hutchinson Cancer Research Center, USA |
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Abstract: | Recent ‘marginal’ methods for the regression analysis of multivariate failure time data have mostly assumed Cox (1972)model
hazard functions in which the members of the cluster have distinct baseline hazard functions. In some important applications,
including sibling family studies in genetic epidemiology and group randomized intervention trials, a common baseline hazard
assumption is more natural. Here we consider a weighted partial likelihood score equation for the estimation of regression
parameters under a common baseline hazard model, and provide corresponding asymptotic distribution theory. An extensive series
of simulation studies is used to examine the adequacy of the asymptotic distributional approximations, and especially the
efficiency gain due to weighting, as a function of strength of dependency within cluster, and cluster size.
This revised version was published online in July 2006 with corrections to the Cover Date. |
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Keywords: | Correlated failure times counting process estimating equations marginal hazard rates nonparametric estimation |
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