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- W2626264695 abstract "As the majority of service recipients of the U.S. long-term care system, the elderly with disabilities experience heterogeneous functional limitation and performance degradation. Successful modeling of heterogeneous degradation becomes of great importance for long-term care decision making. Existing heterogeneity modeling approaches are mainly restricted to single-level modeling of functional performance degradation for older adults. This article proposes a novel bi-level heterogeneity modeling and quantification framework to systematically investigate heterogeneous performance degradation at both sub-population and individual levels. Specifically, a Bayesian non-parametric modeling and inference framework is first proposed to characterize sub-population-level degradation heterogeneity. It relaxes the conventional assumption of pre-specifying a fixed number of sub-populations and allows joint model estimation and selection. Functional data analysis techniques are further considered to quantify individual heterogeneity nested within sub-populations. Based on the extracted temporal variation signatures of degradation profiles, post-analysis is performed to identify relevant risk/protective factors in explaining individual heterogeneity. To illustrate the proposed work and demonstrate its validity, the heterogeneous cognitive degradation quantification of a sample of community-dwelling older adults from the Health and Retirement Study is provided." @default.
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- W2626264695 date "2017-07-03" @default.
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- W2626264695 title "Bi-level heterogeneity modeling of functional performance degradation for the aging population" @default.
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- W2626264695 doi "https://doi.org/10.1080/24725579.2017.1339147" @default.
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