Skip to main content
Journal cover image

Stochastic model for analysis of longitudinal data on aging and mortality.

Publication ,  Journal Article
Yashin, AI; Arbeev, KG; Akushevich, I; Kulminski, A; Akushevich, L; Ukraintseva, SV
Published in: Mathematical biosciences
August 2007

Aging-related changes in a human organism follow dynamic regularities, which contribute to the observed age patterns of incidence and mortality curves. An organism's 'optimal' (normal) physiological state changes with age, affecting the values of risks of disease and death. The resistance to stresses, as well as adaptive capacity, declines with age. An exposure to improper environment results in persisting deviation of individuals' physiological (and biological) indices from their normal state (due to allostatic adaptation), which, in turn, increases chances of disease and death. Despite numerous studies investigating these effects, there is no conceptual framework, which would allow for putting all these findings together, and analyze longitudinal data taking all these dynamic connections into account. In this paper we suggest such a framework, using a new version of stochastic process model of aging and mortality. Using this model, we elaborated a statistical method for analyses of longitudinal data on aging, health and longevity and tested it using different simulated data sets. The results show that the model may characterize complicated interplay among different components of aging-related changes in humans and that the model parameters are identifiable from the data.

Duke Scholars

Published In

Mathematical biosciences

DOI

EISSN

1879-3134

ISSN

0025-5564

Publication Date

August 2007

Volume

208

Issue

2

Start / End Page

538 / 551

Related Subject Headings

  • Stochastic Processes
  • Mortality
  • Models, Statistical
  • Mathematics
  • Humans
  • Data Interpretation, Statistical
  • Bioinformatics
  • Aging
  • 49 Mathematical sciences
  • 31 Biological sciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Yashin, A. I., Arbeev, K. G., Akushevich, I., Kulminski, A., Akushevich, L., & Ukraintseva, S. V. (2007). Stochastic model for analysis of longitudinal data on aging and mortality. Mathematical Biosciences, 208(2), 538–551. https://doi.org/10.1016/j.mbs.2006.11.006
Yashin, Anatoli I., Konstantin G. Arbeev, Igor Akushevich, Aliaksandr Kulminski, Lucy Akushevich, and Svetlana V. Ukraintseva. “Stochastic model for analysis of longitudinal data on aging and mortality.Mathematical Biosciences 208, no. 2 (August 2007): 538–51. https://doi.org/10.1016/j.mbs.2006.11.006.
Yashin AI, Arbeev KG, Akushevich I, Kulminski A, Akushevich L, Ukraintseva SV. Stochastic model for analysis of longitudinal data on aging and mortality. Mathematical biosciences. 2007 Aug;208(2):538–51.
Yashin, Anatoli I., et al. “Stochastic model for analysis of longitudinal data on aging and mortality.Mathematical Biosciences, vol. 208, no. 2, Aug. 2007, pp. 538–51. Epmc, doi:10.1016/j.mbs.2006.11.006.
Yashin AI, Arbeev KG, Akushevich I, Kulminski A, Akushevich L, Ukraintseva SV. Stochastic model for analysis of longitudinal data on aging and mortality. Mathematical biosciences. 2007 Aug;208(2):538–551.
Journal cover image

Published In

Mathematical biosciences

DOI

EISSN

1879-3134

ISSN

0025-5564

Publication Date

August 2007

Volume

208

Issue

2

Start / End Page

538 / 551

Related Subject Headings

  • Stochastic Processes
  • Mortality
  • Models, Statistical
  • Mathematics
  • Humans
  • Data Interpretation, Statistical
  • Bioinformatics
  • Aging
  • 49 Mathematical sciences
  • 31 Biological sciences