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Frailty models based on Lévy processes

Published online by Cambridge University Press:  22 February 2016

Håkon K. Gjessing*
Affiliation:
University of Oslo and Norwegian Institute of Public Health
Odd O. Aalen*
Affiliation:
University of Oslo
Nils Lid Hjort*
Affiliation:
University of Oslo
*
Postal address: Norwegian Institute of Public Health, PO Box 4404, Nydalen, N-0403 Oslo, Norway. Email address: hakon.gjessing@fhi.no
∗∗ Postal address: Section of Medical Statistics, University of Oslo, PO Box 1122, Blindern, N-0317 Oslo, Norway.
∗∗∗ Postal address: Department of Mathematics, University of Oslo, PO Box 1053, Blindern, N-0316 Oslo, Norway.

Abstract

Generalizing the standard frailty models of survival analysis, we propose to model frailty as a weighted Lévy process. Hence, the frailty of an individual is not a fixed quantity, but develops over time. Formulae for the population hazard and survival functions are derived. The power variance function Lévy process is a prominent example. In many cases, notably for compound Poisson processes, quasi-stationary distributions of survivors may arise. Quasi-stationarity implies limiting population hazard rates that are constant, in spite of the continual increase of the individual hazards. A brief discussion is given of the biological relevance of this finding.

Type
General Applied Probability
Copyright
Copyright © Applied Probability Trust 2003 

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References

[1] Aalen, O. O. (1992). Modelling heterogeneity in survival analysis by the compound Poisson distribution. Ann. Appl. Prob. 2, 951972.Google Scholar
[2] Aalen, O. O. (1994). Effects of frailty in survival analysis. Statist. Meth. Med. Res. 3, 227243.Google Scholar
[3] Aalen, O. O. and Gjessing, H. K. (2001). Understanding the shape of the hazard rate: a process point of view. Statist. Sci. 16, 122.CrossRefGoogle Scholar
[4] Aalen, O. O. and Hjort, N. L. (2002). Frailty models that yield proportional hazards. Statist. Prob. Lett. 58, 335342.Google Scholar
[5] Aalen, O. O. and Tretli, S. (1999). Analyzing incidence of testis cancer by means of a frailty model. Cancer Causes Control 10, 285292.Google Scholar
[6] Andersen, P. K., Borgan, Ø., Gill, R. and Keiding, N. (1994). Statistical Models Based on Counting Processes. Springer, New York.Google Scholar
[7] Barndorff-Nielsen, O. E. and Shephard, N. (2001). Non-Gaussian Ornstein–Uhlenbeck-based models and some of their uses in financial economics (with discussion). J. R. Statist. Soc. B 63, 167241.Google Scholar
[8] Bertoin, J. (1996). Lévy Processes. Cambridge University Press.Google Scholar
[9] Clarke, C., Lumsden, C. J. and McInnes, R. R. (2001). Inherited neurodegenerative diseases: the one-hit model of neurodegeneration. Human Molecular Genetics 10, 22692275.CrossRefGoogle ScholarPubMed
[10] Clarke, C. et al., (2000). A one-hit model of cell death in inherited neuronal degenerations. Nature 406, 195199.Google Scholar
[11] Hjort, N. L. (2003). Topics in non-parametric Bayesian statistics (with discussion). In Highly Structured Stochastic Systems, eds Green, P. J., Hjort, N. L. and Richardson, S., Oxford University Press, pp. 455478.Google Scholar
[12] Hougaard, P. (1986). Survival models for heterogeneous populations derived from stable distributions. Biometrika 73, 387396. (Correction: 75 (1998), 395.).Google Scholar
[13] Hougaard, P. (2000). Analysis of Multivariate Survival Data. Springer, New York.Google Scholar
[14] Kebir, Y. (1991). On hazard rate processes. Naval Res. Logistics 38, 865876.Google Scholar
[15] Lebedev, N. N. (1972). Special Functions and Their Applications. Dover Publications, New York.Google Scholar
[16] Lo, C.-C. et al., (2002). Dynamics of sleep–wake transitions during sleep. Europhys. Lett. 57, 625631.Google Scholar
[17] Singpurwalla, N. D. (1995). Survival in dynamic environments. Statist. Sci. 10, 86103.Google Scholar
[18] Slate, E. H. and Turnbull, B. W. (2000). Statistical models for longitudinal biomarkers of disease onset. Statist. Med. 19, 617637.Google Scholar
[19] Triarhou, L. C. (1998). Rate of neuronal fallout in a transsynaptic cerebellar model. Brain Res. Bull. 47, 219222.Google Scholar
[20] Yashin, A. I. and Manton, K. G. (1997). Effects of unobserved and partially observed covariate processes on system failure: a review of models and estimation strategies. Statist. Sci. 12, 2034.Google Scholar