Assessing health care interventions via an interrupted time series model: Study power and design considerations

Maricela Cruz, Daniel L. Gillen, Miriam Bender, Hernando Ombao

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


The delivery and assessment of quality health care is complex with many interacting and interdependent components. In terms of research design and statistical analysis, this complexity and interdependency makes it difficult to assess the true impact of interventions designed to improve patient health care outcomes. 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 in this context. To address this limitation, we propose the “Robust Multiple ITS” (R-MITS) model, appropriate for multiunit ITS data, that allows for inference regarding the estimation of a global change point across units in the presence of a potentially lagged (or anticipatory) 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 intervention effect. We conducted empirical simulation studies to assess the type one error rate of the testing procedure, power for detecting specified change-point alternatives, and accuracy of the proposed estimating 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.
Original languageEnglish (US)
Pages (from-to)1734-1752
Number of pages19
JournalStatistics in Medicine
Issue number10
StatePublished - Jan 9 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported in part by the Eugene Cota-Robles Fellowship at the University of California, Irvine, by the National Science Foundation Graduate Research Fellowship Program under grant DGE-1321846, by the National Science Foundation under grants MMS 1461534 (National Science Foundation Division of Mathematical Sciences) and 1509023 (National Institute on Aging of the National Institutes of Health), R01AG053555 and P50AG16573, and by the National Institute of Mental Health MH115697. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors(s) and do not necessarily reflect the views of the National Science Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.


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