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.
- 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
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
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.
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