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

Handle URI:
http://hdl.handle.net/10754/626000
Title:
A robust interrupted time series model for analyzing complex health care intervention data
Authors:
Cruz, Maricela; Bender, Miriam ( 0000-0003-2457-1652 ) ; Ombao, Hernando
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
KAUST Department:
Statistics Program; King Abdullah University of Science and Technology (KAUST); Thuwal Saudi Arabia
Citation:
Cruz M, Bender M, Ombao H (2017) A robust interrupted time series model for analyzing complex health care intervention data. Statistics in Medicine. Available: http://dx.doi.org/10.1002/sim.7443.
Publisher:
Wiley-Blackwell
Journal:
Statistics in Medicine
Issue Date:
29-Aug-2017
DOI:
10.1002/sim.7443
Type:
Article
ISSN:
0277-6715
Sponsors:
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.
Additional Links:
http://onlinelibrary.wiley.com/doi/10.1002/sim.7443/abstract
Appears in Collections:
Articles

Full metadata record

DC FieldValue Language
dc.contributor.authorCruz, Maricelaen
dc.contributor.authorBender, Miriamen
dc.contributor.authorOmbao, Hernandoen
dc.date.accessioned2017-10-30T08:39:49Z-
dc.date.available2017-10-30T08:39:49Z-
dc.date.issued2017-08-29en
dc.identifier.citationCruz M, Bender M, Ombao H (2017) A robust interrupted time series model for analyzing complex health care intervention data. Statistics in Medicine. Available: http://dx.doi.org/10.1002/sim.7443.en
dc.identifier.issn0277-6715en
dc.identifier.doi10.1002/sim.7443en
dc.identifier.urihttp://hdl.handle.net/10754/626000-
dc.description.abstractCurrent 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 been
dc.description.sponsorshipThis 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.en
dc.publisherWiley-Blackwellen
dc.relation.urlhttp://onlinelibrary.wiley.com/doi/10.1002/sim.7443/abstracten
dc.subjecttime seriesen
dc.subjectComplex Interventionsen
dc.subjectHealth Care Outcomesen
dc.subjectSegmented Regressionen
dc.subjectIntervention Analysisen
dc.titleA robust interrupted time series model for analyzing complex health care intervention dataen
dc.typeArticleen
dc.contributor.departmentStatistics Program; King Abdullah University of Science and Technology (KAUST); Thuwal Saudi Arabiaen
dc.identifier.journalStatistics in Medicineen
dc.contributor.institutionDepartment of Statistics; University of California; Irvine CA USAen
dc.contributor.institutionSue & Bill Gross School of Nursing; University of California; Irvine CA USAen
kaust.authorOmbao, Hernandoen
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