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Nonparametric estimation of the conditional mean residual life function with censored data

Lifetime Data Analysis volume 17, pages 514–532 (2011)Cite this article

Abstract

The conditional mean residual life (MRL) function is the expected remaining lifetime of a system given survival past a particular time point and the values of a set of predictor variables. This function is a valuable tool in reliability and actuarial studies when the right tail of the distribution is of interest, and can be more informative than the survivor function. In this paper, we identify theoretical limitations of some semi-parametric conditional MRL models, and propose two nonparametric methods of estimating the conditional MRL function. Asymptotic properties such as consistency and normality of our proposed estimators are established. We investigate via simulation study the empirical properties of the proposed estimators, including bootstrap pointwise confidence intervals. Using Monte Carlo simulations we compare the proposed nonparametric estimators to two popular semi-parametric methods of analysis, for varying types of data. The proposed estimators are demonstrated on the Veteran’s Administration lung cancer trial.

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Authors and Affiliations

  1. Division of Epidemiology, Statistics and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD, 20892, USA

    Alexander C. McLain

  2. Department of Statistics, North Carolina State University, SAS Hall, Raleigh, NC, 27695, USA

    Sujit K. Ghosh

Authors
  1. Alexander C. McLain
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  2. Sujit K. Ghosh
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Correspondence to Alexander C. McLain.

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McLain, A.C., Ghosh, S.K. Nonparametric estimation of the conditional mean residual life function with censored data. Lifetime Data Anal 17, 514–532 (2011). https://doi.org/10.1007/s10985-011-9197-x

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  • DOI: https://doi.org/10.1007/s10985-011-9197-x

Keywords

  • Covariates
  • Local averaging
  • Mean residual life
  • Right censoring
  • Smoothing