@article{10.22454/PRiMER.2026.677619,
author = {Ryan, Christopher},
title = {Interrupted Time Series Analysis for Quality Improvement Projects},
journal = {PRiMER},
volume = {10},
year = {2026},
month = {6},
doi = {10.22454/PRiMER.2026.677619},
abstract = {Quality improvement (QI) projects often use a simple pre/post analysis. While easy and intuitive, this approach ignores underlying temporal trends, thus potentially leading to inaccurate conclusions. Interrupted times series (ITS) analysis takes trends into account and can yield more robust conclusions. This methodological brief provides an overview of time series data and their unique features, discusses the advantages of interrupted time series modeling over simple two-period pre/post approaches, and illustrates the concepts via an exploration of a published example.},
URL = {https://journals.stfm.org//primer/2026/ryan-0116/},
eprint = {https://journals.stfm.org//media/d15n5f50/primer-10-22.pdf},
}