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Censored Linear Regression for Case-Cohort Studies

Author

Listed:
  • 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.

Suggested Citation

  • Bin Nan & Menggang Yu & Jack Kalbfleisch, 2004. "Censored Linear Regression for Case-Cohort Studies," The University of Michigan Department of Biostatistics Working Paper Series 1044, Berkeley Electronic Press.
    • Handle: RePEc:bep:mchbio:1044
    Note: oai:bepress.com:umichbiostat-1044
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    References listed on IDEAS

    as
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    2. Zhezhen Jin, 2003. "Rank-based inference for the accelerated failure time model," Biometrika, Biometrika Trust, vol. 90(2), pages 341-353, June.
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