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RITS: a toolbox for assessing complex interventions via interrupted time series models

BACKGROUND: Various 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 s...

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Autores principales: Cruz, Maricela, Pinto-Orellana, Marco A., Gillen, Daniel L., Ombao, Hernando C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8265076/
https://www.ncbi.nlm.nih.gov/pubmed/34238221
http://dx.doi.org/10.1186/s12874-021-01322-w
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author Cruz, Maricela
Pinto-Orellana, Marco A.
Gillen, Daniel L.
Ombao, Hernando C.
author_facet Cruz, Maricela
Pinto-Orellana, Marco A.
Gillen, Daniel L.
Ombao, Hernando C.
author_sort Cruz, Maricela
collection PubMed
description BACKGROUND: Various 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. RESULTS: This 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. CONCLUSIONS: The 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.
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spelling pubmed-82650762021-07-08 RITS: a toolbox for assessing complex interventions via interrupted time series models Cruz, Maricela Pinto-Orellana, Marco A. Gillen, Daniel L. Ombao, Hernando C. BMC Med Res Methodol Software BACKGROUND: Various 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. RESULTS: This 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. CONCLUSIONS: The 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. BioMed Central 2021-07-08 /pmc/articles/PMC8265076/ /pubmed/34238221 http://dx.doi.org/10.1186/s12874-021-01322-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access 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. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Cruz, Maricela
Pinto-Orellana, Marco A.
Gillen, Daniel L.
Ombao, Hernando C.
RITS: a toolbox for assessing complex interventions via interrupted time series models
title RITS: a toolbox for assessing complex interventions via interrupted time series models
title_full RITS: a toolbox for assessing complex interventions via interrupted time series models
title_fullStr RITS: a toolbox for assessing complex interventions via interrupted time series models
title_full_unstemmed RITS: a toolbox for assessing complex interventions via interrupted time series models
title_short RITS: a toolbox for assessing complex interventions via interrupted time series models
title_sort rits: a toolbox for assessing complex interventions via interrupted time series models
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8265076/
https://www.ncbi.nlm.nih.gov/pubmed/34238221
http://dx.doi.org/10.1186/s12874-021-01322-w
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