With the establishment and development of regional healthcare big data platforms, regional healthcare big data is playing an increasingly important role in health policy program evaluations. Regional healthcare big data is usually structured hierarchically. Traditional statistical models have limitations in analyzing hierarchical data, and multilevel models are powerful statistical analysis tools for processing hierarchical data. This method has frequently been used by healthcare researchers overseas, however, it lacks application in China. This paper aimed to introduce the multilevel model and several common application scenarios in medicine policy evaluations. We expected to provide a methodological framework for medicine policy evaluation using regional healthcare big data or hierarchical data.
Interrupted time series (ITS) analysis is a quasi-experimental design for evaluating the effectiveness of health interventions. By controlling the time trend before the intervention, ITS is often used to estimate the level change and slope change after the intervention. However, the traditional ITS modeling strategy might indicate aggregation bias when the data was collected from different clusters. This study introduced two advanced ITS methods of handling hierarchical data to provide the methodology framework for population-level health intervention evaluation.