Approaches to Statistical Analysis of Longitudinal Data on Aging, Health, and Longevity: Biodemographic Perspectives

Konstantin G. Arbeev (), Igor Akushevich (), Alexander M. Kulminski (), Kenneth C. Land () and Anatoliy I. Yashin ()
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Konstantin G. Arbeev: Duke Population Research Institute & Social Science Research Institute at Duke University, Biodemography of Aging Research Unit, Center for Population Health and Aging
Igor Akushevich: Duke Population Research Institute & Social Science Research Institute at Duke University, Biodemography of Aging Research Unit, Center for Population Health and Aging
Alexander M. Kulminski: Duke Population Research Institute & Social Science Research Institute at Duke University, Biodemography of Aging Research Unit, Center for Population Health and Aging
Kenneth C. Land: Duke Population Research Institute & Social Science Research Institute at Duke University, Biodemography of Aging Research Unit, Center for Population Health and Aging
Anatoliy I. Yashin: Duke Population Research Institute & Social Science Research Institute at Duke University, Biodemography of Aging Research Unit, Center for Population Health and Aging

Chapter Chapter 11 in Biodemography of Aging, 2016, pp 241-261 from Springer

Abstract: Abstract Longitudinal data play a pivotal role in discovering different aspects of knowledge related to aging, health, and longevity. There are many statistical methods for the analysis of longitudinal data, which is one of the most prolific areas of statistical science. The goal of this chapter is to provide a brief discussion of approaches to statistical analysis of longitudinal data on aging relevant to the major topic of this book, Biodemography of Aging, and relate this discussion to the subsequent chapters in Part II of the monograph. We focus on statistical methods aimed at analyses of time-to-event data jointly with longitudinal measurements that have become known in the mainstream biostatistical literature as “joint models for longitudinal and time-to-event data” or simply “joint models.” We briefly present the basics of joint models and their various extensions suggested in the recent biostatistical literature and discuss them in the context of biodemographic applications. We mention stochastic process models in the context of biodemographic analyses of mechanisms and regularities of aging in relation to mortality and morbidity risks. These models will be discussed in detail in Chap. 12 of this monograph.

Keywords: Longitudinal Data; Hazard Rate; Joint Model; Baseline Hazard; Longitudinal Outcome (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/978-94-017-7587-8_11

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