Information and asymptotic efficiency of the case-cohort sampling design in Cox’s regression model

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Haimeng Zhang, Associate Professor (Creator)
The University of North Carolina at Greensboro (UNCG )
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Abstract: Efficiencies of the maximum pseudolikelihood estimator and a number of related estimators for the case-cohort sampling design in the proportional hazards regression model are studied. The asymptotic information and lower bound for estimating the parametric regression parameter are calculated based on the effective score, which is obtained by determining the component of the parametric score orthogonal to the space generated by the infinite-dimensional nuisance parameter. The asymptotic distributions of the maximum pseudolikelihood and related estimators in an i.i.d. setting show that these estimators do not achieve the computed asymptotic lower bound. Simple guidelines are provided to determine in which instances such estimators are close enough to efficient for practical purposes.

Additional Information

Journal of Multivariate Analysis, 85(2), 292-317
Language: English
Date: 2003
Efficiency, Information bound, Semiparametric model, Epidemiology, Case-cohort design, Counting process, Cox regression model

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