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Estimating a Survival Distribution with Current Status Data and High-dimensional Covariates

Author

Listed:
  • van der Vaart Aad

    (Vrije Universiteit Amsterdam)

  • van der Laan Mark J.

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

Abstract

We consider the inverse problem of estimating a survival distribution when the survival times are only observed to be in one of the intervals of a random bisection of the time axis. We are particularly interested in the case that high-dimensional and/or time-dependent covariates are available, and/or the survival events and censoring times are only conditionally independent given the covariate process. The method of estimation consists of regularizing the survival distribution by taking the primitive function or smoothing, estimating the regularized parameter by using estimating equations, and finally recovering an estimator for the parameter of interest.

Suggested Citation

  • van der Vaart Aad & van der Laan Mark J., 2006. "Estimating a Survival Distribution with Current Status Data and High-dimensional Covariates," The International Journal of Biostatistics, De Gruyter, vol. 2(1), pages 1-42, October.
    • Handle: RePEc:bpj:ijbist:v:2:y:2006:i:1:n:9
    DOI: 10.2202/1557-4679.1014
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    Cited by:

    1. Matias D. Cattaneo & Michael Jansson & Kenichi Nagasawa, 2023. "Bootstrap-Assisted Inference for Generalized Grenander-type Estimators," Papers 2303.13598, arXiv.org, revised Jul 2024.

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