Dataset for: A robust interrupted time series model for analyzing complex healthcare intervention data
Type
DatasetAuthors
Cruz, MaricelaBender, Miriam
Ombao, Hernando
KAUST Department
Biostatistics GroupComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Statistics Program
Date
2017Abstract
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 inter- acting and interdependent components that challenge traditional statistical analytic techniques, in particular when modeling a time series of outcomes data that might be “interrupted" by a change in a particular method of health care delivery. Interrupted time series (ITS) is a robust quasi-experimental design with the ability to infer the effectiveness of an intervention that accounts for data dependency. Current standardized methods for analyzing ITS data do not model changes in variation and correlation following the intervention. This is a key limitation since it is plausible for data variability and dependency to change because of the intervention. Moreover, present methodology either assumes a pre-specified interruption time point with an instantaneous effect or removes data for which the effect of intervention is not fully realized. In this paper, we describe and develop a novel `Robust-ITS' model that overcomes these omissions and limitations. The Robust-ITS model formally performs inference on: (a) identifying the change point; (b) differences in pre- and post-intervention correlation; (c) differences in the outcome variance pre- and post-intervention; and (d) differences in the mean pre- and post-intervention. We illustrate the proposed method by analyzing patient satisfaction data from a hospital that implemented and evaluated a new nursing care delivery model as the intervention of interest. The Robust-ITS model is implemented in a R Shiny toolbox which is freely available to the cCitation
Cruz, M. F., Bender, M., Ombao, H., & Admin, W. (2017). Dataset for: A robust interrupted time series model for analyzing complex healthcare intervention data. Figshare. https://doi.org/10.6084/M9.FIGSHARE.C.3839242Publisher
figshareDOI
10.6084/m9.figshare.c.3839242Relations
Is Supplement To:- [Article]
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.. DOI: 10.1002/sim.7443 HANDLE: 10754/626000