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

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          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.

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          Most cited references108

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          The concept of allostasis in biology and biomedicine.

          Living organisms have regular patterns and routines that involve obtaining food and carrying out life history stages such as breeding, migrating, molting, and hibernating. The acquisition, utilization, and storage of energy reserves (and other resources) are critical to lifetime reproductive success. There are also responses to predictable changes, e.g., seasonal, and unpredictable challenges, i.e., storms and natural disasters. Social organization in many populations provides advantages through cooperation in providing basic necessities and beneficial social support. But there are disadvantages owing to conflict in social hierarchies and competition for resources. Here we discuss the concept of allostasis, maintaining stability through change, as a fundamental process through which organisms actively adjust to both predictable and unpredictable events. Allostatic load refers to the cumulative cost to the body of allostasis, with allostatic overload being a state in which serious pathophysiology can occur. Using the balance between energy input and expenditure as the basis for applying the concept of allostasis, we propose two types of allostatic overload. Type 1 allostatic overload occurs when energy demand exceeds supply, resulting in activation of the emergency life history stage. This serves to direct the animal away from normal life history stages into a survival mode that decreases allostatic load and regains positive energy balance. The normal life cycle can be resumed when the perturbation passes. Type 2 allostatic overload begins when there is sufficient or even excess energy consumption accompanied by social conflict and other types of social dysfunction. The latter is the case in human society and certain situations affecting animals in captivity. In all cases, secretion of glucocorticosteroids and activity of other mediators of allostasis such as the autonomic nervous system, CNS neurotransmitters, and inflammatory cytokines wax and wane with allostatic load. If allostatic load is chronically high, then pathologies develop. Type 2 allostatic overload does not trigger an escape response, and can only be counteracted through learning and changes in the social structure.
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            PyMC: Bayesian Stochastic Modelling in Python.

            This user guide describes a Python package, PyMC, that allows users to efficiently code a probabilistic model and draw samples from its posterior distribution using Markov chain Monte Carlo techniques.
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              Joint latent class models for longitudinal and time-to-event data: a review.

              Most statistical developments in the joint modelling area have focused on the shared random-effect models that include characteristics of the longitudinal marker as predictors in the model for the time-to-event. A less well-known approach is the joint latent class model which consists in assuming that a latent class structure entirely captures the correlation between the longitudinal marker trajectory and the risk of the event. Owing to its flexibility in modelling the dependency between the longitudinal marker and the event time, as well as its ability to include covariates, the joint latent class model may be particularly suited for prediction problems. This article aims at giving an overview of joint latent class modelling, especially in the prediction context. The authors introduce the model, discuss estimation and goodness-of-fit, and compare it with the shared random-effect model. Then, dynamic predictive tools derived from joint latent class models, as well as measures to evaluate their dynamic predictive accuracy, are presented. A detailed illustration of the methods is given in the context of the prediction of prostate cancer recurrence after radiation therapy based on repeated measures of Prostate Specific Antigen.
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                Author and article information

                Contributors
                URI : http://frontiersin.org/people/u/74060
                URI : http://frontiersin.org/people/u/77198
                URI : http://frontiersin.org/people/u/77194
                URI : http://frontiersin.org/people/u/65884
                URI : http://frontiersin.org/people/u/37393
                Journal
                Front Public Health
                Front Public Health
                Front. Public Health
                Frontiers in Public Health
                Frontiers Media S.A.
                2296-2565
                25 September 2014
                06 November 2014
                2014
                : 2
                : 228
                Affiliations
                [1] 1Center for Population Health and Aging, Duke University , Durham, NC, USA
                Author notes

                Edited by: Jimmy Thomas Efird, Brody School of Medicine, USA

                Reviewed by: Charles E. White, Charles E. White’s Biostatistical Consulting LLC, USA; Xiaohui Xu, University of Florida, USA

                *Correspondence: Konstantin G. Arbeev, Center for Population Health and Aging, Duke University, 2024 West Main Street, Room A102F, Box 90420, Durham, NC 27705, USA e-mail: ka29@ 123456duke.edu

                This article was submitted to Epidemiology, a section of the journal Frontiers in Public Health.

                Article
                10.3389/fpubh.2014.00228
                4222133
                58c66dbe-2c05-4114-b3a4-89cfee049f71
                Copyright © 2014 Arbeev, Akushevich, Kulminski, Ukraintseva and Yashin.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 29 August 2014
                : 24 October 2014
                Page count
                Figures: 0, Tables: 0, Equations: 10, References: 117, Pages: 12, Words: 12319
                Categories
                Public Health
                Review Article

                forecasting,mortality,health,joint model,stochastic process model,aging,trajectory

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