Longitudinal models of growth and survival applied to the early detection of Alzheimer's disease
Longitudinal models of growth and survival applied to the early detection of Alzheimer's disease
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DOI：
10.1177/0891988705281879
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发表时间：
2005-12-01
影响因子：
2.6
通讯作者：
Fratiglioni, L
中科院分区：
文献类型：
--
作者：
McArdle, JJ;Small, BJ;Fratiglioni, L
本文探讨了在阿尔茨海默病（AD）早期预测中使用纵向数据的新统计方法。具体来说，作者研究了一些新技术，这些技术允许基于持续时间（生存）和数量变化（生长曲线）对纵向成分进行联合或“共享”估计。这些新的共享生长-生存参数模型可用于表征预测AD发病的功能下降。作者将这些模型应用于瑞典斯德哥尔摩的Kungsholmen项目的数据，这是一项关于老龄化的纵向研究。他们研究了阿尔茨海默病发病的基于年龄的生存-脆弱模型，随着年龄增长认知变化的潜在生长-下降曲线模型，以及生存和早期认知衰退的共同关系的3种替代形式的模型。这种方法的准确性和可靠性被认为是为了更好地理解这些数据中AD的发展过程，包括潜在地消除由于受试者选择而产生的偏差。
This article explores new statistical methodologies for using longitudinal data in the early prediction of Alzheimer's disease (AD). Specifically, the authors examine some new techniques that allow the joint or "shared" estimation of longitudinal components based on both duration (survival) and quantitative changes (growth curves). These new shared growth-survival parameter models may be used to characterize the declining functions that anticipate the onset of AD. The authors apply these models to data from the Kungsholmen Project, a longitudinal study of aging in Stockholm, Sweden. They examine age-based survival-frailty models for the onset of AD, latent growth-decline curve models for changes in cognition over age, and 3 alternative forms of models for the shared relationships of survival and early cognitive decline. The accuracy and reliability of this approach is considered for a better understanding of the developmental course of AD in these data, including the potential removal of biases due to subject selection.