Abstract:
|
Interrupted time series (ITS) -- a quasi-experimental design -- is often used to evaluate the effectiveness of a health policy intervention that accounts for the temporal dependence between outcomes. When an aggregated-level percentage is the outcome of interest, 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 which captures the temporal dependence between 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.
|