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Appears in Collections:Faculty of Health Sciences and Sport Journal Articles
Peer Review Status: Refereed
Title: A reanalysis of cluster randomized trials showed interrupted time-series studies were valuable in health system evaluation
Authors: Fretheim, Atle
Zhang, Fang
Ross-Degnan, Dennis
Oxman, Andrew D
Cheyne, Helen
Foy, Robbie
Goodacre, Steve
Herrin, Jeph
Kerse, Ngaire
McKinlay, R James
Wright, Adam
Soumerai, Stephen B
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Keywords: Evaluation methods
Randomized trials
Interrupted time-series
Quasi-experimental design
Impact evaluations
Health services research
Issue Date: Mar-2015
Publisher: Elsevier
Citation: Fretheim A, Zhang F, Ross-Degnan D, Oxman AD, Cheyne H, Foy R, Goodacre S, Herrin J, Kerse N, McKinlay RJ, Wright A & Soumerai SB (2015) A reanalysis of cluster randomized trials showed interrupted time-series studies were valuable in health system evaluation, Journal of Clinical Epidemiology, 68 (3), pp. 324-333.
Abstract: Objectives: There is often substantial uncertainty about the impacts of health system and policy interventions. Despite that, randomized controlled trials (RCTs) are uncommon in this field, partly because experiments can be difficult to carry out. An alternative method for impact evaluation is the interrupted time-series (ITS) design. Little is known, however, about how results from the two methods compare. Our aim was to explore whether ITS studies yield results that differ from those of randomized trials. Study Design and Setting: We conducted single-arm ITS analyses (segmented regression) based on data from the intervention arm of cluster randomized trials (C-RCTs), that is, discarding control arm data. Secondarily, we included the control group data in the analyses, by subtracting control group data points from intervention group data points, thereby constructing a time series representing the difference between the intervention and control groups. We compared the results from the single-arm and controlled ITS analyses with results based on conventional aggregated analyses of trial data. Results: The findings were largely concordant, yielding effect estimates with overlapping 95% confidence intervals (CI) across different analytical methods. However, our analyses revealed the importance of a concurrent control group and of taking baseline and follow-up trends into account in the analysis of C-RCTs. Conclusion: The ITS design is valuable for evaluation of health systems interventions, both when RCTs are not feasible and in the analysis and interpretation of data from C-RCTs.
Type: Journal Article
DOI Link:
Rights: Copyright 2015 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (
Affiliation: Harvard Medical School
Harvard Medical School
Harvard Medical School
Norwegian Knowledge Centre for the Health Services, Norway
NMAHP Research
University of Leeds
University of Sheffield
Yale University
University of Auckland
McMaster University
Brigham and Women's Hospital
Harvard Medical School

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