Parametric Survival Models

  • Frank E. HarrellJr.
Part of the Springer Series in Statistics book series (SSS)

Abstract

The nonparametric estimator of S(t) is a very good descriptive statistic for displaying survival data. For many purposes, however, one may want to make more assumptions to allow the data to be modeled in more detail. By specifying a functional form for S(t) and estimating any unknown parameters in this function, one can

  1. 1.

    easily compute selected quantiles of the survival distribution;

     
  2. 2.

    estimate (usually by extrapolation) the expected failure time;

     
  3. 3.

    derive a concise equation and smooth function for estimating S(t), Λ(t), and λ(t); and

     
  4. 4.

    estimate S(t) more precisely than S KM(t) or S Λ(t) if the parametric form is correctly specified.

     

Keywords

Hazard Function Failure Time Weibull Model Survival Distribution Cumulative Hazard 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Frank E. HarrellJr.
    • 1
  1. 1.Department of BiostatisticsVanderbilt University School of MedicineNashvilleUSA

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