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Activity Number: 119 - SPEED: Government and Health Policy
Type: Contributed
Date/Time: Monday, July 30, 2018 : 8:30 AM to 10:20 AM
Sponsor: Health Policy Statistics Section
Abstract #330052 Presentation
Title: Assessing Health Care Interventions via an Interrupted Time Series Model: Study Power and Design Considerations
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

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.

Authors who are presenting talks have a * after their name.

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