1. Interrupted Time Series Jay D. Jennings PAPA 6224 March 12, 2008

2. What is Interrupted Time Series Quasi-experimental method Used mostly to determine the impact of complex interventions Observational study Measurements taken before and after intervention Usually carried out in pharmacy practice It is a quasi-experimental method (that cannot be standardized) Used in determining the impact of a complex intervention, both immediately and over time. It in effect is an observation study IN and ITS analysis, multiple measurements are taken before and after the intervention is applied. It is used mostly with the pharmacy filedIt is a quasi-experimental method (that cannot be standardized) Used in determining the impact of a complex intervention, both immediately and over time. It in effect is an observation study IN and ITS analysis, multiple measurements are taken before and after the intervention is applied. It is used mostly with the pharmacy filed

3. How is it carried out? Taking numerous measurements Suggests 40-50 measurements Closer and Longer measurements increase validity Example Measurements Waiting times Number of items dispensed Number of prescriptions Information inquires To conduct an ITS analysis numerous measurements are taken at discrete points in time Usually 40-50 measurements is suggested If the measurements are closer together and the time period is longer the data is more valid Some examples of measurements that can be taken are: waiting times, number of items dispensed, number of prescriptions and the information inquiresTo conduct an ITS analysis numerous measurements are taken at discrete points in time Usually 40-50 measurements is suggested If the measurements are closer together and the time period is longer the data is more valid Some examples of measurements that can be taken are: waiting times, number of items dispensed, number of prescriptions and the information inquires

4. Types applied to Pharmacy Simple ITS ITS with a non-equivalent, no control group ITS with non-equivalent dependant variables ITS with removed intervention ITS with switch replications Short ITS These are the types of ITS usually applied to pharmacy Simple ITS requires only one experimental group with multiple observations ITS with a non-equivalent, no control group. The control group is not subjected to the intervention ITS with a non-equivalent dependent variable, data is collected for dependent variables that should be affected during the intervention ITS with removed intervention, two ITS analysis are combined ITS with switch replications uses two non-equivalent experimental groups Short ITS uses only a short series of measurements to be analyzed These are the types of ITS usually applied to pharmacy Simple ITS requires only one experimental group with multiple observations ITS with a non-equivalent, no control group. The control group is not subjected to the intervention ITS with a non-equivalent dependent variable, data is collected for dependent variables that should be affected during the intervention ITS with removed intervention, two ITS analysis are combined ITS with switch replications uses two non-equivalent experimental groups Short ITS uses only a short series of measurements to be analyzed

5. Interpreting the Results Usually using graphs Followed by statistical analysis Then Serial correlation (one result affects the other) Time series regression (small data points) In interpreting the results of an ITS usually three steps are followed. Usually graphing the information first, then from that graph and statistical analysis, ending with a serial correlation which is analysis of how one result affects another. Time series regression can be used when the research has a small number of data pointsIn interpreting the results of an ITS usually three steps are followed. Usually graphing the information first, then from that graph and statistical analysis, ending with a serial correlation which is analysis of how one result affects another. Time series regression can be used when the research has a small number of data points

6. Some Policy Examples