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Design characteristics and statistical methods used in interrupted time series studies evaluating public health interventions: protocol for a review
  1. Simon L Turner1,
  2. Amalia Karahalios1,
  3. Andrew B Forbes1,
  4. Monica Taljaard2,3,
  5. Jeremy M Grimshaw2,3,4,
  6. Allen C Cheng1,5,
  7. Lisa Bero6,
  8. Joanne E McKenzie1
  1. 1 School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
  2. 2 Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
  3. 3 School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, Ontario, Canada
  4. 4 Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada
  5. 5 Infection Prevention and Healthcare Epidemiology Unit, Alfred Health, Melbourne, Victoria, Australia
  6. 6 Faculty of Pharmacy and Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
  1. Correspondence to Associate Professor Joanne E McKenzie; joanne.mckenzie{at}monash.edu

Abstract

Introduction An interrupted time series (ITS) design is an important observational design used to examine the effects of an intervention or exposure. This design has particular utility in public health where it may be impracticable or infeasible to use a randomised trial to evaluate health system-wide policies, or examine the impact of exposures (such as earthquakes). There have been relatively few studies examining the design characteristics and statistical methods used to analyse ITS designs. Further, there is a lack of guidance to inform the design and analysis of ITS studies.

This is the first study in a larger project that aims to provide tools and guidance for researchers in the design and analysis of ITS studies. The objectives of this study are to (1) examine and report the design characteristics and statistical methods used in a random sample of contemporary ITS studies examining public health interventions or exposures that impact on health-related outcomes, and (2) create a repository of time series data extracted from ITS studies. Results from this study will inform the remainder of the project which will investigate the performance of a range of commonly used statistical methods, and create a repository of input parameters required for sample size calculation.

Methods and analysis We will collate 200 ITS studies evaluating public health interventions or the impact of exposures. ITS studies will be identified from a search of the bibliometric database PubMed between the years 2013 and 2017, combined with stratified random sampling. From eligible studies, we will extract study characteristics, details of the statistical models and estimation methods, effect metrics and parameter estimates. Further, we will extract the time series data when available. We will use systematic review methods in the screening, application of inclusion and exclusion criteria, and extraction of data. Descriptive statistics will be used to summarise the data.

Ethics and dissemination Ethics approval is not required since information will only be extracted from published studies. Dissemination of the results will be through peer-reviewed publications and presentations at conferences. A repository of data extracted from the published ITS studies will be made publicly available.

  • interrupted time series
  • segmented regression
  • public health
  • statistical methods

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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Footnotes

  • Patient consent for publication Not required.

  • Contributors JEM conceived the study and all authors contributed to its design. SLT wrote the first draft of the manuscript, with contributions from JEM and AK. All authors contributed to revisions of the manuscript and take public responsibility for its content.

  • Funding SLT was funded through an Australian Postgraduate Award administered through Monash University, Australia. This project is funded by the Australian National Health and Medical Research Council (NHMRC) project grant (1145273). JEM is supported by an NHMRC Career Development Fellowship (1143429). ACC is supported by an NHMRC Career Development Fellowship (1068732). JMG holds a Canada Research Chair in Health Knowledge Uptake and Transfer. The funders had no role in study design, decision to publish, or preparation of the manuscript.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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