Open Access<p>MultiCenter Interrupted Time Series Analysis: Incorporating Within and Between-Center Heterogeneity</p>
Author(s) -
Joycelyne Ewusie,
Lehana Thabane,
Joseph Beyene,
Sharon E. Straus,
Jemila S Hamid
Publication year - 2020
Publication title -
clinical epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.868
H-Index - 58
ISSN - 1179-1349
DOI - 10.2147/clep.s231843
Subject(s) - statistics , spurious relationship , interrupted time series analysis , regression analysis , statistic , regression , logistic regression , statistical power , type i and type ii errors , linear regression , power analysis , medicine , econometrics , mathematics , algorithm , cryptography
Segmented regression (SR) is the most common statistical method used in the analysis of interrupted time series (ITS) data. However, this modeling strategy is indicated to produce spurious results when applied to aggregated data. For multicenter ITS studies, data at a given time point are often aggregated across different participants and settings; thus, conventional segmented regression analysis may not be an optimal approach. Our objective is to provide a robust method for analysis of ITS data, while accounting for two sources of heterogeneity, between participants and across sites.