Frailty Models

  • Terry M. Therneau
  • Patricia M. Grambsch
Part of the Statistics for Biology and Health book series (SBH)


In the last several years there has been significant and active research concerning the addition of random effects to survival models. In this setting, a random effect is a continuous variable that describes excess risk or frailty for distinct categories, such as individuals or families. The idea is that individuals have different frailties, and that those who are most frail will die earlier than the others. Aalen [1] provides theoretical and practical motivation for frailty models by discussing the impact of heterogeneity on analyses, and by illustrating how random effects can deal with it. He states

It is a basic observation of medical statistics that individuals are dissimilar. . .. Still, there is a tendency to regard this variation as a nuisance, and not as something to be considered seriously in its own right. Statisticians are often accused of being more interested in averages, and there is some truth to this


Penalty Function Frailty Model Profile Likelihood Shared Frailty Gamma Frailty 
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 2000

Authors and Affiliations

  • Terry M. Therneau
    • 1
  • Patricia M. Grambsch
    • 2
  1. 1.Department of Health Sciences ResearchMayo ClinicRochesterUSA
  2. 2.Division of Biostatistics, School of Public HealthUniversity of MinnesotaMinneapolisUSA

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