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模拟无残疾人预期寿命与持续时间依赖:关于马尔科夫假设偏差的研究笔记

Tianyu Shen¹, James O'Donnell¹

¹School of Demography, Research School of Social Sciences, College of Arts and Social Sciences, Australian National University, Acton, Australian Capital Territory, Australia.

|December 5, 2024

概括

本研究引入了一种方法,用调查数据来估计健康预期寿命 (HLE),发现尽管持续时间依赖,但共同的马尔科夫假设提供了合理的估计. 该方法解决了分析复杂健康转型的现有模型的局限性.

科学领域:

人口统计学人口统计学
流行病学流行病学
生物统计学生物统计学

背景情况:

关于健康预期寿命 (HLE) 的传统人口学研究经常使用马尔科夫假设,它忽略了暴露时间对健康转变的影响.
时间依赖多态生命表 (DDMSLT) 模型考虑了暴露时间,但由于未知的初始状态持续时间,很难将其应用于左边审查的调查数据.

研究的目的:

提出一种灵活的方法,将DDMSLT应用于对左审查的调查数据,以估计多个状态的预期寿命.
使用这种新的方法计算美国老年人的无残疾/健康预期寿命 (HLE),并将其与传统的基于马尔科夫模型进行比较.

主要方法:

开发了一种方法,通过部分排除观测和截断持续时间来适应DDMSLT用于左翼审查的调查数据.
应用了美国健康与退休研究 (HRS) 的方法来估计HLE.
从使用马尔科夫假设的标准多态模型与持续时间依赖模型的结果进行比较.

主要成果:

过渡概率表现出持续时间的依赖.
尽管依赖于持续时间,但其对健康预期寿命 (HLE) 的整体影响是最小的,在整个人口中平均出.
马尔科夫假设在这种情况下产生了对HLE的可信和节的估计.

结论:

拟议的方法允许在DDMSLT模型中使用左边审查的调查数据.
对于美国老年人的HLE估计,马尔科夫假设带来的偏差是最小的.
马尔科夫假设提供了一种实用且足够准确的方法,用于在类似的人口统计研究中估计HLE.

关键词:

持续时间依赖持续时间依赖健康的预期寿命健康的预期寿命纵向调查数据纵向调查数据马尔科夫假设的假设多州模式的模型.

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