Interrupted time series analysis is a useful and specialized tool for understanding the impact of a change in circumstances on a long-term trend. The data for interrupted time series is a specific type of longitudinal data and must meet two criteria.
The first requirement: it must have data about an outcome that is collected over many time points. The second requirement: there must be an intervention, policy or program that was implemented for the entire population at a specific time point.
In this training, we focus on how the design works:
- Explanation of data structure
- What data can be modeled
- How to organize the data
- Important terminology: Counterfactual, autocorrelation, moving averages
- Examples of studies utilizing this statistical approach
- Examples of plotting the data to understand the design
- Model parameter estimates of interest
About the Instructor
Jeff Meyer is a statistical consultant with The Analysis Factor, a stats mentor for Statistically Speaking membership, and a workshop instructor. Read more about Jeff here.