Assessing Health Care Interventions via an Interrupted Time Series Model: Study Power and Design Considerations

• Activity Number: 197 - SPEED: Government and Health Policy
• Type: Contributed
• Date/Time: Monday, July 30, 2018 : 10:30 AM to 11:15 AM
• Sponsor: Health Policy Statistics Section
• Author(s): Maricela Cruz* and Miriam Bender and Daniel L. Gillen and Hernando Ombao
• Companies: University of California, Irvine and University of California, Irvine and University of California, Irvine and King Abdullah University of Science and Technology
• Keywords: Complex Interventions; Patient Satisfaction; Power Analysis; Time Series; Segmented Regression; Change Point Detection

Abstract

Interrupted time series (ITS) is a quasi-experimental design developed for inferring the effectiveness of a health policy intervention while accounting for temporal dependence within a single system or unit. Current standardized ITS methods do not simultaneously analyze data for several units, nor are there methods to test for the existence of a change point and to assess statistical power for study planning purposes. To address this limitation we propose the `Robust Multiple ITS' (R-MITS) model, appropriate for multi-unit ITS data, that allows for inference regarding the estimation of a global change point across units in the presence of a potentially lagged treatment effect. Under the R-MITS model, one can formally test for the existence of a change point and estimate the time delay between the formal intervention implementation and the over-all-unit effect. We conducted empirical simulation studies to assess type one error rate, power for detecting specified change-point alternatives, and accuracy of the proposed methodology. R-MITS is illustrated by analyzing patient satisfaction data from a hospital that implemented and evaluated a new care delivery model in multiple units.