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Linear Life Expectancy Regression with Censored Data

Author

Listed:
  • Ying Chen

    (Division of Biostatistics, School of Public Health, University of California, Berkeley)

  • Su-Chun Cheng

    (Department of Epidemiology and Biostatistics, University of California, San Francisco)

Abstract

Life expectancy, i.e., mean residual life function, has been of important practical and scientific interests to characterise the distribution of residual life. Regression models are often needed to model the association between life expectancy and its covariates. In this article, we consider a linear mean residual life model and further developed some inference procedures in presence of censoring. The new model and proposed inference procedure will be demonstrated by numerical examples and application to the well-known Stanford heart transplant data. Additional semiparametric efficiency calculation and information bound are also considered.

Suggested Citation

  • Ying Chen & Su-Chun Cheng, 2004. "Linear Life Expectancy Regression with Censored Data," U.C. Berkeley Division of Biostatistics Working Paper Series 1154, Berkeley Electronic Press.
    • Handle: RePEc:bep:ucbbio:1154
    Note: oai:bepress.com:ucbbiostat-1154
    as

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