GEE type inference for clustered zero-inflated negative binomial regression with application to dental caries.
GEE type inference for clustered zero-inflated negative binomial regression with application to dental caries.
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DOI：
10.1016/j.csda.2014.11.014
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发表时间：
2015-05-01
影响因子：
1.8
通讯作者：
Datta, Somnath
中科院分区：
文献类型：
--
作者：
Kong, Maiying;Xu, Sheng;Levy, Steven M.;Datta, Somnath
关键词：
在零计数的数量超过传统计数数据模型（如泊松或负二项分布）预测的数量的应用中，零膨胀计数数据模型的使用很常见。当显示膨胀的零计数的计数数据在受试者之间相关时，自然的方法将是在广义估计方程（GEE）的帮助下拟合边缘模型，广义估计方程（GEE）可以包含受试者与受试者的相关性。提出了一种基于GEE的零膨胀负二项分布（ZINB）模型来拟合具有过多零点的聚集计数。然而，相应的三明治方差估计似乎低估了真实的方差。解释了其失败的理论原因，并提供了额外的建模假设下的校正。此外，提出了一个集群rescue（自助）程序估计方差，它表明，自助程序捕获正确的方差下没有额外的模型假设。使用一个彻底的模拟研究，这边际GEE为基础的ZINB模型超过其他两个竞争车型的效用进行了评估。由此产生的推理程序应用于研究龋齿和氟化物暴露之间的关联使用从爱荷华州氟化物研究中提取的数据集。许多具有临床意义的风险因素可靠地确定使用所提出的模型。
Use of zero-inflated count data models is common in applications where the number of zero counts exceeds that predicted from a traditional count data model such as Poisson or negative binomial. When count data exhibiting inflated zero counts are correlated among subjects, a natural approach will be to fit a marginal model with the help of generalized estimating equations (GEE) that can incorporate subject-to-subject correlations. A GEE based zero-inflated negative binomial (ZINB) model is proposed to fit clustered counts with excessive zeros. However, the corresponding sandwich variance estimator appears to underestimate the true variance. The theoretical reasons for its failure are explained and a correction under additional modeling assumptions is offered. In addition, a clustered resampling (bootstrap) procedure is proposed to estimate the variance and it is shown that the bootstrap procedure captures the correct variance under no additional model assumptions. Utility of this marginal GEE based ZINB model over two other competing models has been assessed using a thorough simulation study. The resulting inference procedure is applied to study the association between the dental caries and fluoride exposures using a dataset extracted from the Iowa Fluoride Study. A number of risk factors of clinical significance are reliably identified using the proposed model.
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