Modeling Frailty as a Function of Observed Covariates
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 Classification62K15
KeywordsFrailty covariates MCMC accelerated failure time hazard
Unable to display preview. Download preview PDF.
- D‘Agostino, R.B., Nam, B-H., 2004. Evaluation of the performance of survival analysis models: Discrimination and calibration measures. In Balakrishnan, N. and Rao, C.R.(eds.), Handbook of Statistics 23: Advances in survival analysis, Elsevier, North Holland, 1–25.Google Scholar
- Jones, G., 2004. Markov Chain Monte Carlo methods for inference in frailty models with doubly-censored data. Journal of data science 2, 33–47.Google Scholar
- Lancaster, T., 1979. Econometric methods for the duration of unemployment. Econometrica 47(4), 939–956.Google Scholar
- SAS, Version 8.2, Copyright © 1999 by SAS Institute Inc., Cary, NC, USA.Google Scholar
- Spiegelhalter, D.J., Thomas, A., Best, N.G., 2003. WinBugs Version 1.4 User Manual, MRC Biostatistics Unit., Cambridge, UK.Google Scholar
- Wolf, P.A., Abbott, R.D., Kannel, W.B., 1987. Atrial fibrillation: a major contributor to stroke in the elderly. The Framingham Study. Archives of Internal Medicine 147(9), 1561–4.Google Scholar