A robust interrupted time series model for analyzing complex health care intervention data

Maricela Cruz; Miriam Bender; Hernando Ombao

doi:10.1002/sim.7443

A robust interrupted time series model for analyzing complex health care intervention data

Maricela Cruz, Miriam Bender, Hernando Ombao

Research output: Contribution to journal › Article › peer-review

47 Scopus citations

Abstract

Current health policy calls for greater use of evidence-based care delivery services to improve patient quality and safety outcomes. Care delivery is complex, with interacting and interdependent components that challenge traditional statistical analytic techniques, in particular, when modeling a time series of outcomes data that might be

Original language	English (US)
Pages (from-to)	4660-4676
Number of pages	17
Journal	Statistics in Medicine
Volume	36
Issue number	29
DOIs	https://doi.org/10.1002/sim.7443
State	Published - Aug 29 2017

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This study was funded in part by the Commission on Nurse Certification, and based upon work supported by the Eugene Cota-Robles Fellowship at the University of California, Irvine, the NSF Graduate Research Fellowship under Grant No. DGE-1321846, and by the NSF MMS1461534 and NSF DMS1509023 grants. 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.

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10.1002/sim.7443

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abstract = "Current health policy calls for greater use of evidence-based care delivery services to improve patient quality and safety outcomes. Care delivery is complex, with interacting and interdependent components that challenge traditional statistical analytic techniques, in particular, when modeling a time series of outcomes data that might be",

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note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: This study was funded in part by the Commission on Nurse Certification, and based upon work supported by the Eugene Cota-Robles Fellowship at the University of California, Irvine, the NSF Graduate Research Fellowship under Grant No. DGE-1321846, and by the NSF MMS1461534 and NSF DMS1509023 grants. 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.",

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AU - Bender, Miriam

AU - Ombao, Hernando

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A robust interrupted time series model for analyzing complex health care intervention data

Abstract

Bibliographical note

UN SDGs

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Dataset for: A robust interrupted time series model for analyzing complex healthcare intervention data

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A robust interrupted time series model for analyzing complex health care intervention data

Abstract

Bibliographical note

UN SDGs

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Dataset for: A robust interrupted time series model for analyzing complex healthcare intervention data

Dataset for: A robust interrupted time series model for analyzing complex healthcare intervention data

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