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首页> 外文期刊>Health services & outcomes research methodology >Are marginalized two-part models superior to non-marginalized two-part models for count data with excess zeroes? Estimation of marginal effects, model misspecification, and model selection
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Are marginalized two-part models superior to non-marginalized two-part models for count data with excess zeroes? Estimation of marginal effects, model misspecification, and model selection

机译：边缘化的两部分模型优于非边缘化的两件模型，用于数量多余的零点？ 估计边缘效应，模型拼写和模型选择

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The marginalized two-part models, including the marginalized zero-inflated Poisson and negative binomial models, have been proposed in the literature for modelling cross-sectional healthcare utilization count data with excess zeroes and overdispersion. The motivation for these proposals was to directly capture the overall marginal effects and to avoid post-modelling effect calculations that are needed for the non-marginalized conventional two-part models. However, are marginalized two-part models superior to non-marginalized two-part models because of their structural property? Is it true that the marginalized two-part models can provide direct marginal inference? This article aims to answer these questions through a comprehensive investigation. We first summarize the existing non-marginalized and marginalized two-part models and then develop marginalized hurdle Poisson and negative binomial models for cross-sectional count data with abundant zero counts. Our interest in the investigation lies particularly in the (average) marginal effect and (average) incremental effect and the comparison of these effects. The estimators of these effects are presented, and variance estimators are derived by using delta methods and Taylor series approximations. Though the marginalized models attract attention because of the alleged convenience of direct marginal inference, we provide evidence for the impact of model misspecification of the marginalized models over the conventional models, and provide evidence for the importance of goodness-of-fit evaluation and model selection in differentiating between the marginalized and non-marginalized models. An empirical analysis of the German Socioeconomic Panel data is presented.
机译：在文献中提出了边缘化的两部分模型，包括边缘化的零充气泊松和负二项式模型，用于建模横断面医疗保健利用计数数据，具有过量的零和过度分散。这些提案的动机是直接捕获整体边缘效应，并避免非边缘化的传统两部分模型所需的建模效果计算。但是，由于结构性属性，边缘化的两部分模型优于非边缘化的两件模型？边缘化的两部分模型可以提供直接边缘推理吗？本文旨在通过全面调查回答这些问题。我们首先总结了现有的非边缘化和边缘化的两部分模型，然后开发边缘化的障碍泊松和负二进制型，用于零零计数的横截面计数数据。我们对调查的兴趣特别是（平均）边际效应和（平均）增量效应和这些影响的比较。提出了这些效果的估计，通过使用Delta方法和泰勒序列近似来导出方差估计器。虽然边缘化模型由于指称的方便极端推断而引起关注，但我们提供了模型误操作对传统模型的模型误操作的影响的证据，并为符合健康评估和模型选择的重要性提供了证据在边缘化和非边缘化模型之间区分。介绍了德国社会经济面板数据的实证分析。

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