The zero-inflated negative binomial regression model with correction for misclassification: an example in caries research
The zero-inflated negative binomial regression model with correction for misclassification: an example in caries research
复制标题
DOI：
10.1177/0962280206071840
复制
发表时间：
2008-04-01
影响因子：
2.3
通讯作者：
Declerck, Dominique
中科院分区：
文献类型：
--
作者：
Mwalili, Samuel M.;Lesaffre, Emmanuel;Declerck, Dominique
计数数据的零膨胀模型在当今越来越流行，在医学、经济学、生物学、社会学等许多应用领域都有应用。然而，在实践中，这些计数往往容易产生测量误差，在这种情况下归结为错误分类。最近已经提出了处理计数错误分类的方法，但仅限于二项模型和泊松模型。在这里，我们采用了一个更复杂的模型，即零膨胀负二项，并说明如何实现对错误分类的纠正。我们的方法是用dmft指数来说明的，dmft指数是龋齿研究中常用的龋齿测量方法。一个额外的问题是，有几个牙科检查人员参与了对蛀牙经验的评分。使用我们的例子，我们说明了每个审查员的非差异错误分类过程如何导致总体上的差异错误分类。
Zero-inflated models for count data are becoming quite popular nowadays and are found in many application areas, such as medicine, economics, biology, sociology and so on. However, in practice these counts are often prone to measurement error which in this case boils down to misclassification. Methods to deal with misclassification of counts have been suggested recently, but only for the binomial model and the Poisson model. Here we took at a more complex model, that is, the zero-inflated negative binomial, and illustrate how correction for misclassification can be achieved. Our approach is illustrated on the dmft-index which is a popular measure for caries experience in caries research. An extra problem was the fact that several dental examiners were involved in scoring caries experience. Using our example, we illustrate how a non-differential misclassification process for each examiner can lead to differential misclassification overall.