Manton K G
Center for Demographic Studies, Duke University, Durham, North Carolina 27708-0408, USA.
J Gerontol A Biol Sci Med Sci. 1999 Jun;54(6):B247-54. doi: 10.1093/gerona/54.6.b247.
Hazard models are often applied to mortality data of humans and other species so that the parameter estimates made for those models can be used to make inferences about the biology, and comparative biology, of aging processes. Enough longitudinal data on physiological and functional changes in humans now exist to know that the age trajectory of the physiological state of individuals is multidimensional, stochastic, and plastic. Thus, to fully assess the biological significance of existing longitudinal data on human aging and mortality processes, multivariate stochastic process models must be developed that are biologically detailed and valid. This requires assessing genetic mechanisms controlling human longevity and rates of aging, developing models of how those traits may have evolved, and developing statistical methods for identifying gene environment interactions. This article examines the theoretical basis for such models and the biological rationale of their parametric structure. Several examples are given.
风险模型经常应用于人类和其他物种的死亡率数据，以便为这些模型所做的参数估计能够用于推断衰老过程的生物学以及比较生物学。现在已有足够的关于人类生理和功能变化的纵向数据，从而了解到个体生理状态的年龄轨迹是多维的、随机的和可塑的。因此，为了全面评估现有的关于人类衰老和死亡过程的纵向数据的生物学意义，必须开发出在生物学上详细且有效的多变量随机过程模型。这需要评估控制人类寿命和衰老速率的遗传机制，建立这些性状可能如何进化的模型，并开发用于识别基因-环境相互作用的统计方法。本文探讨了此类模型的理论基础及其参数结构的生物学原理。文中给出了几个例子。
