Analysis of zero inflated dichotomous variables from a Bayesian perspective: application to occupational health.
Analysis of zero inflated dichotomous variables from a Bayesian perspective: application to occupational health.
复制标题
DOI：
10.1186/s12874-021-01427-2
复制
发表时间：
2021-12-12
影响因子：
4
通讯作者：
Navarro A
中科院分区：
文献类型：
--
作者：
Moriña D;Puig P;Navarro A
零膨胀模型通常旨在解决由两个不同的源产生在分布中观察到的零值而引起的问题。在实践中，这是因为所研究的总体实际上由两个子总体组成：一个是缺省值为零(结构零)，另一个是间接的(样本零)。这项工作提出了一种新的方法来拟合零膨胀的伯努利数据从贝叶斯方法，能够区分两个潜在的零源(结构性和非结构性)。建议的方法的性能已经通过全面的模拟研究进行了评估，并已被编译为社区免费提供的R包。它的用法是通过职业健康领域的一个真实例子来说明的，这是一种疾病出勤现象，在这种现象中，有理由认为一些人永远不会有患病的风险，因为他们在研究期间没有生病(结构零)。如果不区分结构性和非结构性零点，人们就会联合研究一般健康状况和出勤主义本身，从而获得潜在的有偏见的估计，因为通过将其淡化为一般健康状况，这一现象被隐含地低估了。所提出的方法能够从贝叶斯框架中有或没有协变量的二分数据中区分两个不同的零源(结构性和非结构性)，并且已经以bayesZIB R Package(https://cran.r-project.org/package=bayesZIB).的形式提供给任何感兴趣的研究人员网上版载有补充材料，可在10.1186/s12874-021-01427-2查阅。
Zero-inflated models are generally aimed to addressing the problem that arises from having two different sources that generate the zero values observed in a distribution. In practice, this is due to the fact that the population studied actually consists of two subpopulations: one in which the value zero is by default (structural zero) and the other is circumstantial (sample zero). This work proposes a new methodology to fit zero inflated Bernoulli data from a Bayesian approach, able to distinguish between two potential sources of zeros (structural and non-structural). The proposed methodology performance has been evaluated through a comprehensive simulation study, and it has been compiled as an R package freely available to the community. Its usage is illustrated by means of a real example from the field of occupational health as the phenomenon of sickness presenteeism, in which it is reasonable to think that some individuals will never be at risk of suffering it because they have not been sick in the period of study (structural zeros). Without separating structural and non-structural zeros one would be studying jointly the general health status and the presenteeism itself, and therefore obtaining potentially biased estimates as the phenomenon is being implicitly underestimated by diluting it into the general health status. The proposed methodology is able to distinguish two different sources of zeros (structural and non-structural) from dichotomous data with or without covariates in a Bayesian framework, and has been made available to any interested researcher in the form of the bayesZIB R package (https://cran.r-project.org/package=bayesZIB). The online version contains supplementary material available at 10.1186/s12874-021-01427-2.
登录
查看更多内容
影响因子：
6.1
作者：
VUONG, QH
通讯作者：
VUONG, QH
影响因子：
--
作者：
Vives A;Amable M;Ferrer M;Moncada S;Llorens C;Muntaner C;Benavides FG;Benach J
通讯作者：
Benach J
DOI：
10.1080/00949655.2012.668550
发表时间：
2013-09-01
影响因子：
1.2
作者：
Perumean-Chaney, Suzanne E.;Morgan, Charity;Aban, Inmaculada
通讯作者：
Aban, Inmaculada
DOI：
10.1080/03610918.2014.950743
发表时间：
2016-01-01
影响因子：
0.9
作者：
Diop, Aba;Diop, Aliou;Dupuy, Jean-Francois
通讯作者：
Dupuy, Jean-Francois
影响因子：
3.9
作者：
Zhao, Hantong;Pan, Yingan;Li, Bo
通讯作者：
Li, Bo