Assessing individual and population variability in degenerative joint disease prevalence using generalized linear mixed models.

Assessing individual and population variability in degenerative joint disease prevalence using generalized linear mixed models.
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使用广义线性混合模型评估退行性关节疾病患病率的个体和群体变异性。

DOI:
10.1002/ajpa.24195
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发表时间:
2021
影响因子:
2.8
通讯作者:
Alonso-Llamazares C
Alonso-Llamazares C
中科院分区:
地球科学2区
文献类型:
--
作者:
Alonso-Llamazares C

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ObjectivesIn本文中,我们介绍了使用广义线性混合模型(GLMM)作为一个更好的替代传统的统计方法,研究相关因素的流行退行性关节疾病(DJD)在bioarchaeological contextuals.Materials和MethodsDJD患病率进行了评估的apapricular关节和脊柱的西班牙人口从15日至18日世纪。数据进行了分析,使用列联表,逻辑回归模型,和逻辑GLMM.ResultsIn一般情况下,从GLSTIM的结果发现协议在其他方法。然而,通过能够在个体骨骼而不是聚合关节或肢体的水平上分析数据,GLPNN能够揭示在其他框架中不明显的关联。讨论目前在统计分析软件中广泛使用,GLPNN可以适应广泛的数据分布,考虑层次相关性,回归估计DJD患病率在个人和骨骼位置是无偏的协变量的影响。这为生物考古数据集的分析提供了明显的优势,可以导致更强大和可比较的跨人群分析。
ObjectivesIn this paper, we introduce the use of generalized linear mixed models (GLMM) as a better alternative to traditional statistical methods for studying factors associated to the prevalence of degenerative joint disease (DJD) in bioarchaeological contexts.Materials and MethodsDJD prevalence was assessed for the appendicular joints and the spine of a Spanish population dated from the 15th to the 18th century. Data were analyzed using contingency tables, logistic regression models, and logistic GLMM.ResultsIn general, results from GLMMs find agreement in other methods. However, by being able to analyze the data at the level of individual bones instead of aggregated joints or limbs, GLMMs are capable of revealing associations that are not evident in other frameworks.DiscussionCurrently widely available in statistical analysis software, GLMMs can accommodate a wide array of data distributions, account for hierarchical correlations, and return estimates of DJD prevalence within individuals and skeletal locations that are unbiased by the effect of covariates. This gives clear advantages for the analysis of bioarchaeological datasets which can lead to more robust and comparable analyses across populations.
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