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Basic Principles of Survival Analysis

  • Dirk F. Moore
Chapter
Part of the Use R! book series (USE R)

Abstract

Survival analysis methods depend on the survival distribution, and two key ways of specifying it are the survival function and the hazard function . The survival function defines the probability of surviving up to a point t. Formally,
$$\displaystyle{S(t) = pr(T > t),\begin{array}{cc} &0 < t < \infty \end{array} }$$
This function takes the value 1 at time 0, decreases (or remains constant) over time, and of course never drops below 0. As defined here it is right continuous.

Keywords

Exponential Distribution Hazard Function Survival Function Weibull Distribution Survival Distribution 
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.

References

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    Kalbfleisch, J.D., Prentice, R.L.: The Statistical Analysis of Failure Time Data, 2nd edn. Wiley, Hoboken (2002)Google Scholar
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    Klein, J.P., Moeschberger, M.L.: Survival Analysis: Techniques for Censored and Truncated Data, 2nd edn. Springer, New York (2005)Google Scholar
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    Preston, S.H., Heuveline, P., Guillot, M.: Demography: Measuring and Modeling Population Processes. Blackwell Malden, MA (2000)Google Scholar
  5. 70.
    Therneau, T.M., Offord, J.: Expected survival based on hazard rates (update). Technical Report 63, Mayo Clinic Department of Health Science Research (1999)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Dirk F. Moore
    • 1
  1. 1.Department of BiostatisticsRutgers School of Public HealthPiscatawayUSA

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