Zero-inflated negative binomial mixed regression modeling of over-dispersed count data with extra zeros
Zero-inflated negative binomial mixed regression modeling of over-dispersed count data with extra zeros
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DOI：
10.1002/bimj.200390024
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发表时间：
2003-01-01
影响因子：
1.7
通讯作者：
Lee, AH
中科院分区：
文献类型：
--
作者：
Yau, KKW;Wang, K;Lee, AH
在许多生物统计应用中，遇到的计数数据往往包含额外的零相对于泊松分布。零膨胀泊松回归模型对于分析此类数据是有用的，但是如果非零观测值由于抽样设计或数据收集程序而过度分散并且同时相关，则参数估计可能会严重偏倚。本文采用零膨胀负二项混合回归模型分析了一组胰腺疾病住院时间（LOS）数据，主要包括当天分离。随机效应被引入到考虑医院间的变化和集群LOS观测的依赖性。通过使用EM算法最大化适当的对数似然函数来实现参数估计。替代建模策略，即泊松分布和非参数最大似然法的有限混合，也被认为是。相关协变量的确定将有助于医院管理者和临床医生有效地管理LOS和支出。
In many biometrical applications, the count data encountered often contain extra zeros relative to the Poisson distribution. Zero-inflated Poisson regression models are useful for analyzing such data, but parameter estimates may be seriously biased if the nonzero observations are over-dispersed and simultaneously correlated due to the sampling design or the data collection procedure. In this paper, a zero-inflated negative binomial mixed regression model is presented to analyze a set of pancreas disorder length of stay (LOS) data that comprised mainly same-day separations. Random effects are introduced to account for inter-hospital variations and the dependency of clustered LOS observations. Parameter estimation is achieved by maximizing an appropriate log-likelihood function using an EM algorithm. Alternative modeling strategies, namely the finite mixture of Poisson distributions and the non-parametric maximum likelihood approach, are also considered. The detemidnation of pertinent covariates would assist hospital administrators and clinicians to manage LOS and expenditures efficiently.