论文标题
边缘化的三部分中断时间序列回归模型，用于比例数据
A marginalized three-part interrupted time series regression model for proportional data
论文作者
论文摘要
中断的时间序列（ITS）通常用于评估卫生政策干预的有效性，该政策干预涉及结果的时间依赖性。当感兴趣的结果是百分比或百分位数时，数据可以高度偏斜，以$ [0，1] $的限制，并且具有许多零或零。三部分的β回归模型通常用于通过三个子模型明确地分离零，一个和正值。但是，将时间依赖性纳入三部分的beta回归模型是具有挑战性的。在本文中，我们提出了一个边缘化的零型beta时间序列模型，该模型通过Copula捕获了结果的时间依赖性，并允许研究人员检查对边际平均值的协变量影响。我们使用仿真研究研究了其实际性能，并将模型应用于实际研究。
Interrupted time series (ITS) is often used to evaluate the effectiveness of a health policy intervention that accounts for the temporal dependence of outcomes. When the outcome of interest is a percentage or percentile, the data can be highly skewed, bounded in $[0, 1]$, and have many zeros or ones. A three-part Beta regression model is commonly used to separate zeros, ones, and positive values explicitly by three submodels. However, incorporating temporal dependence into the three-part Beta regression model is challenging. In this article, we propose a marginalized zero-one-inflated Beta time series model that captures the temporal dependence of outcomes through copula and allows investigators to examine covariate effects on the marginal mean. We investigate its practical performance using simulation studies and apply the model to a real ITS study.
