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dc.contributor.authorCruz, Maricela
dc.contributor.authorPinto-Orellana, Marco A
dc.contributor.authorGillen, Daniel L
dc.contributor.authorOmbao, Hernando C
dc.date.accessioned2021-07-12T06:57:54Z
dc.date.available2021-07-12T06:57:54Z
dc.date.issued2021-07-09
dc.date.submitted2021-03-03
dc.identifier.citationCruz, M., Pinto-Orellana, M. A., Gillen, D. L., & Ombao, H. C. (2021). RITS: a toolbox for assessing complex interventions via interrupted time series models. BMC Medical Research Methodology, 21(1). doi:10.1186/s12874-021-01322-w
dc.identifier.issn1471-2288
dc.identifier.issn1471-2288
dc.identifier.pmid34238221
dc.identifier.doi10.1186/s12874-021-01322-w
dc.identifier.urihttp://hdl.handle.net/10754/670138
dc.description.abstractBackgroundVarious interacting and interdependent components comprise complex interventions. These components create difficulty in assessing the true impact of interventions designed to improve patient-centered outcomes. Interrupted time series (ITS) designs borrow from case-crossover designs and serve as quasi-experimental methodology able to retrospectively assess the impact of an intervention while accounting for temporal correlation. While ITS designs are aptly situated for studying the impacts of large-scale public health policies, existing ITS software implement rigid ITS methodology that often assume the pre- and post-intervention phases are fully differentiated (by a known change-point or set of time points) and do not allow for changes in both the mean functions and correlation structure.ResultsThis article describes the Robust Interrupted Time Series (RITS) toolbox, a stand-alone user-friendly application researchers can use to implement flexible ITS models that estimate the lagged effect of an intervention on an outcome, level and trend changes, and post-intervention changes in the correlation structure, for single and multiple ITS. The RITS toolbox incorporates a formal test for the existence of a change in the outcome and estimates a change-point over a set of possible change-points defined by the researcher. In settings with multiple ITS, RITS provides a global over-all units change-point and allows for unit-specific changes in the mean functions and correlation structures.ConclusionsThe RITS toolbox is the first piece of software that allows researchers to use flexible ITS models that test for the existence of a change-point, estimate the change-point (if estimation is desired), and allow for changes in both the mean functions and correlation structures at the change point. RITS does not require any knowledge of a statistical (or otherwise) programming language, is freely available to the community, and may be downloaded and used on a local machine to ensure data protection.
dc.description.sponsorshipWe thank Dr. Miriam Bender (University of California Irvine) for providing us with data used to generate our example datasets.
dc.description.sponsorshipThis work was supported in part by KAUST research grant to the KAUST Biostatistics research group, the National Institute on Aging of the National Institutes of Health under award numbers R01AG053555 and P50AG16573, and the National Institute of Mental Health of the National Institutes of Health under award number R01MH115697. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
dc.relation.urlhttps://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-021-01322-w
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectComplex Interventions
dc.subjectSegmented Regression
dc.subjectInterrupted Time Series
dc.subjectChange-point Detection
dc.subjectToolbox
dc.subjectPolicy Interventions
dc.titleRITS: a toolbox for assessing complex interventions via interrupted time series models.
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
dc.identifier.journalBMC medical research methodology
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionKaiser Permanente Washington Health Research Institute, Seattle, WA, USA.
dc.contributor.institutionDepartment of Biostatistics, University of Washington, Seattle, WA, USA.
dc.contributor.institutionDepartment of Mechanical, Electronics and Chemical Engineering, Metropolitan Oslo University, Oslo, Norway.
dc.contributor.institutionDepartment of Statistics, University of California Irvine, Irvine, California, USA.
kaust.personOmbao, Hernando C
dc.date.accepted2021-05-19
refterms.dateFOA2021-07-12T07:01:35Z


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This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Except where otherwise noted, this item's license is described as This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.