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

Maricela Cruz, Miriam Bender, Hernando Ombao

Research output: Contribution to journalArticlepeer-review

39 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 languageEnglish (US)
Pages (from-to)4660-4676
Number of pages17
JournalStatistics in Medicine
Volume36
Issue number29
DOIs
StatePublished - 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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