Conclusions Regarding Statistical Modeling of Aging, Health, and Longevity

Abstract

The analyses conducted in Part I did not exhaust all factors affecting age patterns of age-related changes in health and mortality. They actually provided a strong rationale for conducting more detailed analyses which require advanced methods of mathematical and statistical modeling. Development and implementation of such state-of-the-art methods is driven by two major factors. The first reflects systemic effects of various behavioral, physiological, and environmental processes on human aging and the related phenotypes. The second is that not all such processes can be readily measured and quantified in studies of human health, aging, and lifespan. In this regard, longitudinal data play a pivotal role in discovering different aspects of knowledge related to aging, health, and lifespan. A variety of statistical methods can be used to analyze longitudinal data.