Journal of Statistical Theory and Practice

, Volume 1, Issue 1, pp 117–135 | Cite as

Modeling Frailty as a Function of Observed Covariates

  • Usha S. GovindarajuluEmail author
  • Mark E. Glickman
  • Ralph B. D’AgostinoSr.


In survival analysis, frailty models are potential choices for modeling unexplained heterogeneity in a population, which exists due to missing covariate information or to differential survival patterns among members of a population. Typically, in these models, the frailty term, which is a random effect, is unconditional on the observed covariates. In our model, we allow the frailty effect to be modulated by the observed covariates. In this way, the frailty effect is no longer rendered separate from the covariates, allowing the model to capture the frailty effect as function of unobserved as well as observed information. We demonstrate this model on a set of subjects in the Framingham Heart Study who had atrial fibrillation events and who were followed forward in time for the development of stroke. As assessed via performance measures, our model performs better on this data than the other models considered. It also captures unique hazard configurations not produced by the other models.

AMS Subject Classification



Frailty covariates MCMC accelerated failure time hazard 


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Copyright information

© Grace Scientific Publishing 2007

Authors and Affiliations

  • Usha S. Govindarajulu
    • 1
    Email author
  • Mark E. Glickman
    • 2
  • Ralph B. D’AgostinoSr.
    • 3
  1. 1.School of MedicineYale UniversityNew HavenUSA
  2. 2.Dept. of Health Policy and ManagementBoston University School of Public HealthBostonUSA
  3. 3.Dept. of Math & StatisticsBoston UniversityBostonUSA

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