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用于残疾调查数据的纵向混合成员轨迹模型

Longitudinal Mixed Membership Trajectory Models for Disability Survey Data.

作者信息

Manrique-Vallier Daniel

机构信息

Department of Statistics, Indiana University.

出版信息

Ann Appl Stat. 2014 Dec;8(4):2268-2291. doi: 10.1214/14-AOAS769.

Abstract

We develop new methods for analyzing discrete multivariate longitudinal data and apply them to functional disability data on U.S. elderly population from the National Long Term Care Survey (NLTCS), 1982-2004. Our models build on a mixed membership framework, in which individuals are allowed multiple membership on a set of extreme profiles characterized by time-dependent trajectories of progression into disability. We also develop an extension that allows us to incorporate birth-cohort effects, in order to assess inter-generational changes. Applying these methods we find that most individuals follow trajectories that imply a late onset of disability, and that younger cohorts tend to develop disabilities at a later stage in life compared to their elders.

摘要

我们开发了用于分析离散多变量纵向数据的新方法,并将其应用于1982 - 2004年美国国家长期护理调查(NLTCS)中关于老年人口的功能残疾数据。我们的模型建立在混合成员框架之上,在该框架中,个体被允许在一组以进入残疾的时间依赖轨迹为特征的极端概况上具有多重成员身份。我们还开发了一个扩展,使我们能够纳入出生队列效应,以便评估代际变化。应用这些方法,我们发现大多数个体遵循的轨迹意味着残疾发病较晚,并且与年长者相比,年轻队列在生命后期更容易出现残疾。

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