Spatiotemporal hurdle models for zero-inflated count data: Exploring trends in emergency department visits
Spatiotemporal hurdle models for zero-inflated count data: Exploring trends in emergency department visits
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DOI：
10.1177/0962280214527079
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发表时间：
2016-12-01
影响因子：
2.3
通讯作者：
Hastings, Nicole S.
中科院分区：
文献类型：
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作者：
Neelon, Brian;Chang, Howard H.;Hastings, Nicole S.
在一项探索急诊科使用时空趋势的研究的推动下，我们开发了一类由两部分组成的障碍模型，用于分析零膨胀面积计数数据。该模型由两个部分组成：一是使用任何急诊科的概率，二是指定使用情况下急诊科就诊的次数。通过分层结构，模型结合了患者和区域级别的预测因子，以及每个模型组件的空间和时间相关的随机效应。随机效应被分配多元条件自回归先验，这会引起组件之间的依赖性，并提供跨相邻空间单元和时间段的空间和时间平滑，从而改进推理。为了适应潜在的过度分散，我们考虑了一系列正计数的参数规范，包括截断负二项分布和广义泊松分布。我们采用贝叶斯推理方法，后验计算可以在标准贝叶斯软件中方便地处理。我们的结果表明，负二项式和广义泊松障碍模型远远优于泊松障碍模型，这表明过度分散的障碍模型为分析零膨胀时空数据提供了一种有用的方法。
Motivated by a study exploring spatiotemporal trends in emergency department use, we develop a class of two-part hurdle models for the analysis of zero-inflated areal count data. The models consist of two components-one for the probability of any emergency department use and one for the number of emergency department visits given use. Through a hierarchical structure, the models incorporate both patient-and region-level predictors, as well as spatially and temporally correlated random effects for each model component. The random effects are assigned multivariate conditionally autoregressive priors, which induce dependence between the components and provide spatial and temporal smoothing across adjacent spatial units and time periods, resulting in improved inferences. To accommodate potential overdispersion, we consider a range of parametric specifications for the positive counts, including truncated negative binomial and generalized Poisson distributions. We adopt a Bayesian inferential approach, and posterior computation is handled conveniently within standard Bayesian software. Our results indicate that the negative binomial and generalized Poisson hurdle models vastly outperform the Poisson hurdle model, demonstrating that overdispersed hurdle models provide a useful approach to analyzing zero-inflated spatiotemporal data.