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生存和认知功能变化的双变量联合模型。

Bivariate joint models for survival and change of cognitive function.

机构信息

Department of Statistical Science, University College, London, UK.

出版信息

Stat Methods Med Res. 2023 Mar;32(3):474-492. doi: 10.1177/09622802221146307. Epub 2022 Dec 26.

DOI:10.1177/09622802221146307
PMID:36573012
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9983056/
Abstract

Changes in cognitive function over time are of interest in ageing research. A joint model is constructed to investigate. Generally, cognitive function is measured through more than one test, and the test scores are integers. The aim is to investigate two test scores and use an extension of a bivariate binomial distribution to define a new joint model. This bivariate distribution model the correlation between the two test scores. To deal with attrition due to death, the Weibull hazard model and the Gompertz hazard model are used. A shared random-effects model is constructed, and the random effects are assumed to follow a bivariate normal distribution. It is shown how to incorporate random effects that link the bivariate longitudinal model and the survival model. The joint model is applied to the English Longitudinal Study of Ageing data.

摘要

随着时间的推移,认知功能的变化是衰老研究的一个关注点。本文构建了一个联合模型来进行研究。一般来说,认知功能是通过多项测试来衡量的,并且测试分数是整数。本研究旨在调查两个测试分数,并使用二元二项分布的扩展来定义一个新的联合模型。这个二元分布模型可以描述两个测试分数之间的相关性。为了处理由于死亡而导致的缺失数据,本文使用了威布尔风险模型和戈珀兹风险模型。构建了一个共享随机效应模型,假设随机效应服从二元正态分布。本文还展示了如何将二元纵向模型和生存模型的随机效应联系起来。该联合模型应用于英国老龄化纵向研究数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0b1/9983056/ac582f22ec80/10.1177_09622802221146307-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0b1/9983056/091bb6ddd85e/10.1177_09622802221146307-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0b1/9983056/4073ddb8bbd4/10.1177_09622802221146307-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0b1/9983056/d71be5d17850/10.1177_09622802221146307-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0b1/9983056/2154f9eff012/10.1177_09622802221146307-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0b1/9983056/ac582f22ec80/10.1177_09622802221146307-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0b1/9983056/091bb6ddd85e/10.1177_09622802221146307-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0b1/9983056/4073ddb8bbd4/10.1177_09622802221146307-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0b1/9983056/d71be5d17850/10.1177_09622802221146307-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0b1/9983056/2154f9eff012/10.1177_09622802221146307-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0b1/9983056/ac582f22ec80/10.1177_09622802221146307-fig5.jpg

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Hidden three-state survival model for bivariate longitudinal count data.双变量纵向计数数据的隐藏三状态生存模型。
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