Literature DB >> 17977568

Model of hidden heterogeneity in longitudinal data.

Anatoli I Yashin1, Konstantin G Arbeev, Igor Akushevich, Alexander Kulminski, Lucy Akushevich, Svetlana V Ukraintseva.   

Abstract

Variables measured in longitudinal studies of aging and longevity do not exhaust the list of all factors affecting health and mortality transitions. Unobserved factors generate hidden variability in susceptibility to diseases and death in populations and in age trajectories of longitudinally measured indices. Effects of such heterogeneity can be manifested not only in observed hazard rates but also in average trajectories of measured indices. Although effects of hidden heterogeneity on observed mortality rates are widely discussed, their role in forming age patterns of other aging-related characteristics (average trajectories of physiological state, stress resistance, etc.) is less clear. We propose a model of hidden heterogeneity to analyze its effects in longitudinal data. The approach takes the presence of hidden heterogeneity into account and incorporates several major concepts currently developing in aging research (allostatic load, aging-associated decline in adaptive capacity and stress-resistance, age-dependent physiological norms). Simulation experiments confirm identifiability of model's parameters.

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Year:  2007        PMID: 17977568      PMCID: PMC2268646          DOI: 10.1016/j.tpb.2007.09.001

Source DB:  PubMed          Journal:  Theor Popul Biol        ISSN: 0040-5809            Impact factor:   1.570


  15 in total

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

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  13 in total

1.  A genetic stochastic process model for genome-wide joint analysis of biomarker dynamics and disease susceptibility with longitudinal data.

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2.  New stochastic carcinogenesis model with covariates: an approach involving intracellular barrier mechanisms.

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Review 3.  Dynamic heterogeneity and life histories.

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4.  How Genes Modulate Patterns of Aging-Related Changes on the Way to 100: Biodemographic Models and Methods in Genetic Analyses of Longitudinal Data.

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Journal:  N Am Actuar J       Date:  2016-06-22

5.  Modeling hematopoietic system response caused by chronic exposure to ionizing radiation.

Authors:  Igor V Akushevich; Galina A Veremeyeva; Georgy P Dimov; Svetlana V Ukraintseva; Konstantin G Arbeev; Alexander V Akleyev; Anatoly I Yashin
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6.  The quadratic hazard model for analyzing longitudinal data on aging, health, and the life span.

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7.  Dynamic determinants of longevity and exceptional health.

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8.  Genetic model for longitudinal studies of aging, health, and longevity and its potential application to incomplete data.

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9.  Cumulative deficits and physiological indices as predictors of mortality and long life.

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Review 10.  Joint Analyses of Longitudinal and Time-to-Event Data in Research on Aging: Implications for Predicting Health and Survival.

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Journal:  Front Public Health       Date:  2014-11-06
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