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Censored Linear Regression for Case-Cohort Studies

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Author Info
Bin Nan (University of Michigan)
Menggang Yu (University of Indiana)
Jack Kalbfleisch (University of Michigan)
Abstract

Right censored data from a classical case-cohort design and a stratified case-cohort design are considered. In the classical case-cohort design, the subcohort is obtained as a simple random sample of the entire cohort, whereas in the stratified design, the subcohort is selected by independent Bernoulli sampling with arbitrary selection probabilities. For each design and under a linear regression model, methods for estimating the regression parameters are proposed and analyzed. These methods are derived by modifying the linear ranks tests and estimating equations that arise from full-cohort data using methods that are similar to the "pseudo-likelihood" estimating equation that has been used in relative risk regression for these models. The estimates so obtained are shown to be consistent and asymptotically normal. Variance estimation and numerical illustrations are also provided.

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Publisher Info
Paper provided by Berkeley Electronic Press in its series The University of Michigan Department of Biostatistics Working Paper Series with number 1044.

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Date of creation: 20 Oct 2004
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Handle: RePEc:bep:mchbio:1044

Note: oai:bepress.com:umichbiostat-1044
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Related research
Keywords: Case-cohort design; Censored linear regression; Counting processes; Martin-gales; Rank statistic ;

References listed on IDEAS
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  1. Huang Y., 2002. "Calibration Regression of Censored Lifetime Medical Cost," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 318-327, March. [Downloadable!] (restricted)
  2. Zhezhen Jin, 2003. "Rank-based inference for the accelerated failure time model," Biometrika, Oxford University Press for Biometrika Trust, vol. 90(2), pages 341-353, June.
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This page was last updated on 2009-12-15.

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