Model of hidden heterogeneity in longitudinal data.

Pubmed ID: 17977568

Pubmed Central ID: PMC2268646

Journal: Theoretical population biology

Publication Date: Feb. 1, 2008

MeSH Terms: Humans, United States, Aging, Longitudinal Studies, Data Interpretation, Statistical, Models, Statistical

Grants: R01 AG028259, R01 AG027019, P01 AG008761, R01 AG028259-03, 1R01 AG028259-01, 1R01-AG-027019-01, 1R01AG030612-01, 5P01-AG-008761-16, P01 AG008761-180005, R01 AG027019-02

Authors: Yashin AI, Arbeev KG, Kulminski A, Ukraintseva SV, Akushevich I, Akushevich L

Cite As: Yashin AI, Arbeev KG, Akushevich I, Kulminski A, Akushevich L, Ukraintseva SV. Model of hidden heterogeneity in longitudinal data. Theor Popul Biol 2008 Feb;73(1):1-10. Epub 2007 Sep 18.

Studies:

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.