Joint analysis of health histories, physiological state, and survival

Mathematical Population Studies

Volumn 18, Issue 4, 2011, Pages 207-233

  Yashin, Anatoly I ^a   Akushevich, Igor ^a   Arbeev, Konstantin G ^a   Kulminski, Alexander ^a   Ukraintseva, Svetlana ^a  

^a DUKE UNIVERSITY   (United States)

Author keywords

Aging related changes; Longitudinal data; Mortality modelling; Observational plans

Indexed keywords

AGE STRUCTURE; HEALTH STATUS; MORTALITY; OBSERVATIONAL METHOD; PHYSIOLOGY; SURVIVAL;

EID: 84855585904     PISSN: 08898480     EISSN: 1547724X     Source Type: Journal    
DOI: 10.1080/08898480.2011.614486     Document Type: Article

References (16)

1. Akushevich, I., Kulminski, A., and Manton, K. (2005). Life tables with covariates: Dynamic model for nonlinear analysis of longitudinal data. Mathematical Population Studies, 12: 51-80.
2. Arbeev, K.G., Akushevich, I., Kulminski, A., et al. (2009). Genetic model for longitudinal studies of aging, health, and longevity and its potential application to incomplete data. Journal of Theoretical Biology, 258: 103-111.
3. Bagdonavicius, V. and Nikulin, M. (2009). Statistical models to analyze failure, wear, fatigue, and degradation data with explanatory variables. Communications in Statistics-Theory and Methods, 38(16-17): 3031-3047.
4. Dawber, T.R. (1980). The Framingham Study: The Epidemiology of Atherosclerotic Disease. Cambridge, MA: Harvard University Press.
5. Sacher, G. and Trucco, E. (1962). The stochastic theory of mortality. Annals of the New York Academy of Sciences, 96: 985-1007.
6. Strehler, B.L. and Mildvan, A.S. (1960). General theory of mortality and aging. Science, 132(3418): 14-21.
7. Woodbury, M.A. and Manton, K.G. (1977). A random walk model of human mortality and aging. Theoretical Population Biology, 11: 37-48.
8. Yashin, A.I. (1980). Conditional Gaussian estimation of dynamic systems under jumping observations. Automation and Remote Control, 5: 123-129.
9. Yashin, A.I. (1985). Dynamics in survival analysis: Conditional Gaussian property versus Cameron-Martin formula. In N.V. Krylov, R.Sh. Liptser, and A.A. Novikov (Eds.), Statistics and Control of Stochastic Processes. New York, NY: Springer, pp. 446-485.
10. 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. Statistical Science, 12: 20-34.
11. Yashin, A.I., Arbeev, K.G., Akushevich, I., et al. (2007). Stochastic model for analysis of longitudinal data on aging and mortality. Mathematical Biosciences, 208: 538-551
12. Yashin, A.I., Arbeev, K.G., Akushevich, I., et al. (2008). Model of hidden heterogeneity in longitudinal data. Theoretical Population Biology, 73: 1-10.
13. Yashin, A.I., Manton, K.G., Woodbury, M.A., and Stallard, E. (1995). The effects of health histories on stochastic process models of aging and mortality. Journal of Mathematical Biology, 34: 1-16.
14. Yashin, A.I. and Iachine, I.A. (1995). Survival of related individuals: An extension of some fundamental results of heterogeneity analysis. Mathematical Population Studies, 5(4): 321-339.
15. Yashin, A.I., Iachine, I.A., Andreev, K.F., and Larsen, U. (1998). Multistate models of postpartum infecundity, fecundability and sterility by age and parity: Methodological issues. Mathematical Population Studies, 7(1): 51-78.
16. Yashin, A.I., Iachine, I.A., and Begun, A.S. (2000). Mortality modeling: A review. Mathematical Population Studies, 8(4): 305-332.