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Biometrika 2006 93(1):207-214; doi:10.1093/biomet/93.1.207
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© 2006 Biometrika Trust

Miscellanea

Semiparametric transformation models for the case-cohort study

Wenbin Lu and Anastasios A. Tsiatis

Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695, U.S.A. lu{at}stat.ncsu.edu, tsiatis{at}stat.ncsu.edu

A general class of semiparametric transformation models is studied for analysing survival data from the case-cohort design, which was introduced by Prentice (1986). Weighted estimating equations are proposed for simultaneous estimation of the regression parameters and the transformation function. It is shown that the resulting regression estimators are asymptotically normal, with variance-covariance matrix that has a closed form and can be consistently estimated by the usual plug-in method. Simulation studies show that the proposed approach is appropriate for practical use. An application to a case-cohort dataset from the Atherosclerosis Risk in Communities study is also given to illustrate the methodology.

Key Words: Case-cohort design; Martingale; Transformation model; Weighted estimating equation

Received July 2004. Revised August 2005.


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