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

dc.contributor.author Akushevich, Igor
dc.contributor.author Arbeev, Konstantin
dc.contributor.author Kulminski, Alexander
dc.contributor.author Ukraintseva, Svetlana
dc.contributor.author Yashin, Anatoli I
dc.coverage.spatial Switzerland
dc.date.accessioned 2017-06-02T18:05:55Z
dc.date.available 2017-06-02T18:05:55Z
dc.date.issued 2014
dc.identifier https://www.ncbi.nlm.nih.gov/pubmed/25414844
dc.identifier.uri https://hdl.handle.net/10161/14758
dc.description.abstract Longitudinal data on aging, health, and longevity provide a wealth of information to investigate different aspects of the processes of aging and development of diseases leading to death. Statistical methods aimed at analyses of time-to-event data jointly with longitudinal measurements became known as the "joint models" (JM). An important point to consider in analyses of such data in the context of studies on aging, health, and longevity is how to incorporate knowledge and theories about mechanisms and regularities of aging-related changes that accumulate in the research field into respective analytic approaches. In the absence of specific observations of longitudinal dynamics of relevant biomarkers manifesting such mechanisms and regularities, traditional approaches have a rather limited utility to estimate respective parameters that can be meaningfully interpreted from the biological point of view. A conceptual analytic framework for these purposes, the stochastic process model of aging (SPM), has been recently developed in the biodemographic literature. It incorporates available knowledge about mechanisms of aging-related changes, which may be hidden in the individual longitudinal trajectories of physiological variables and this allows for analyzing their indirect impact on risks of diseases and death. Despite, essentially, serving similar purposes, JM and SPM developed in parallel in different disciplines with very limited cross-referencing. Although there were several publications separately reviewing these two approaches, there were no publications presenting both these approaches in some detail. Here, we overview both approaches jointly and provide some new modifications of SPM. We discuss the use of stochastic processes to capture biological variation and heterogeneity in longitudinal patterns and important and promising (but still largely underused) applications of JM and SPM to predictions of individual and population mortality and health-related outcomes.
dc.language eng
dc.relation.ispartof Front Public Health
dc.relation.isversionof 10.3389/fpubh.2014.00228
dc.subject aging
dc.subject forecasting
dc.subject health
dc.subject joint model
dc.subject mortality
dc.subject stochastic process model
dc.subject trajectory
dc.title Joint Analyses of Longitudinal and Time-to-Event Data in Research on Aging: Implications for Predicting Health and Survival.
dc.type Journal article
pubs.author-url https://www.ncbi.nlm.nih.gov/pubmed/25414844
pubs.begin-page 228
pubs.organisational-group Center for Population Health & Aging
pubs.organisational-group Duke
pubs.organisational-group Duke Population Research Institute
pubs.organisational-group Sanford School of Public Policy
pubs.organisational-group Staff
pubs.publication-status Published online
pubs.volume 2


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