Ignoring overdispersion in hierarchical loglinear models: Possible problems and solutions
Ignoring overdispersion in hierarchical loglinear models: Possible problems and solutions
复制标题
忽略分层对数线性模型中的过度分散：可能的问题和解决方案
作者：
E. Milanzi;Ariel Alonso;G. Molenberghs
泊松数据经常表现出过度分散;对于单变量模型，存在许多选项来规避这个问题。然而，在复杂的情况下，例如，在纵向研究中，解释过度分散是一项更具挑战性的任务。最近，Molenberghs et.al提出了一个模型，通过结合两组随机效应来解释过度分散。然而，引入一组新的随机效应意味着对本质上不可观察的变量进行额外的分布假设，这在以前没有考虑过。使用组合模型作为框架，我们通过模拟探索了在复杂的纵向设置中忽略过度分散的影响。此外，我们评估了错误指定随机效应分布对组合模型和经典泊松分层模型的影响。我们的研究结果表明，即使推论可能会受到忽略过度分散的影响，在这种情况下，组合模型是一个很有前途的工具。版权所有© 2012约翰威利父子有限公司.
Poisson data frequently exhibit overdispersion; and, for univariate models, many options exist to circumvent this problem. Nonetheless, in complex scenarios, for example, in longitudinal studies, accounting for overdispersion is a more challenging task. Recently, Molenberghs et.al, presented a model that accounts for overdispersion by combining two sets of random effects. However, introducing a new set of random effects implies additional distributional assumptions for intrinsically unobservable variables, which has not been considered before. Using the combined model as a framework, we explored the impact of ignoring overdispersion in complex longitudinal settings via simulations. Furthermore, we evaluated the effect of misspecifying the random‐effects distribution on both the combined model and the classical Poisson hierarchical model. Our results indicate that even though inferences may be affected by ignored overdispersion, the combined model is a promising tool in this scenario. Copyright © 2012 John Wiley & Sons, Ltd.