Stochastic model for analysis of longitudinal data on aging and mortality
Open accessAbstract
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.
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Cut-points can be difficult to apply across a sample that covers many ages, as the effects of continuous traits can be age-specific.
“cut-points can be difficult to apply across a sample that covers many ages, as the effects of continuous traits can be age-specific.[28]”
From A standard procedure for creating a frailty index (2008) · cites this paper as [28]
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Arbeev, K. G., Land, K. C., & Yashin, A. I. (2016) · The Springer series on demographic methods and population analysis · doi:10.1007/978-94-017-7587-8_13 · Cited by 1 · see citing works
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Self-citationIntegrative Mortality Models with Parameters That Have Biological Interpretations
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Circulatory Diseases and Aging
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Multi-state Continuous-Time Markov Chain Modeling for Chronic Kidney Disease ProgressionPreprint
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- Authors
- Yashin, A. I., Arbeev, K. G., Akushevich, I., Kulminski, A., Akushevich, L., & Ukraintseva, S. V.
- Affiliations
- Duke University (US)
- Corresponding author
- Yashin, Anatoli I.
- Venue
- Mathematical Biosciences
- Publisher
- Elsevier BV
- Published
- 2007-08
- Volume
- 208
- Issue
- 2
- Pages
- 538-551
- ISSN
- 0025-5564 (ISSN-L)
- PMC
- PMC2084381
- OpenAlex
- W1990667054
- Cited by
- 82
- References
- 44
- Citations by year
- 2026: 4 · 2025: 1 · 2024: 2 · 2023: 3 · 2022: 2 · 2021: 1 · 2020: 2 · 2019: 5 · 2018: 2 · 2017: 3 · 2016: 15 · 2015: 3 · 2014: 8 · 2013: 4 · 2012: 7
- FWCI
- 7.56
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Mathematical Biosciences (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute on Aging (P01AG008761, R01AG027019, R01AG028259)
- Topics
- Social Sciences › Demography › Insurance, Mortality, Demography, Risk Management
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Keywords
- stochastic modeling, aging, mortality, longitudinal analysis, physiological state, disease risk, longevity, parameter identifiability, stress tolerance
- MeSH terms
- Aging, Data Interpretation, Statistical, Humans, Mathematics, Mortality, Stochastic Processes, Models, Statistical
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