Hofstetter Hedwig, Dusseldorp Elise, Zeileis Achim, Schuller Annemarie A
TNO (Netherlands Organization for Applied Scientific Research), Expertise Group Life Style, Leiden, The Netherlands.
Caries Res. 2016;50(6):517-526. doi: 10.1159/000448197. Epub 2016 Sep 17.
In dental epidemiology, the decayed (D), missing (M), and filled (F) teeth or surfaces index (DFM index) is a frequently used measure. The DMF index is characterized by a strongly positive skewed distribution with a large stack of zero counts for those individuals without caries experience. Therefore, standard generalized linear models often lead to a poor fit. The hurdle regression model is a highly suitable class to model a DMF index, but its use is subordinated. We aim to overcome the gap between the suitability of the hurdle model to fit DMF indices and the frequency of its use in caries research. A theoretical introduction to the hurdle model is provided, and an extensive comparison with the zero-inflated model is given. Using an illustrative data example, both types of models are compared, with a special focus on interpretation of their parameters. Accompanying R code and example data are provided as online supplementary material.
在口腔流行病学中，龋失补（DMF）牙或牙面指数是一种常用的测量方法。DMF指数的特点是呈强正偏态分布，对于没有龋齿经历的个体，有大量的零计数堆积。因此，标准广义线性模型往往拟合效果不佳。 hurdle回归模型是一种非常适合对DMF指数进行建模的模型类型，但其应用并不广泛。我们旨在弥合hurdle模型对DMF指数拟合的适用性与其在龋齿研究中的使用频率之间的差距。本文提供了hurdle模型的理论介绍，并与零膨胀模型进行了广泛比较。通过一个说明性数据示例，对这两种模型进行了比较，特别关注它们参数的解释。随附的R代码和示例数据作为在线补充材料提供。
