A robust interrupted time series model for analyzing complex health care intervention data
KAUST DepartmentApplied Mathematics and Computational Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2017-08-29
Print Publication Date2017-12-20
Permanent link to this recordhttp://hdl.handle.net/10754/626000
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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 be
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.
SponsorsThis 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.
JournalStatistics in Medicine
RelationsIs Supplemented By:
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