Methods used to meta-analyse results from interrupted time series studies: A methodological systematic review protocol

Elizabeth Korevaar, Amalia Karahalios, Andrew B. Forbes, Simon L. Turner, Steve McDonald, Monica Taljaard, Jeremy M. Grimshaw, Allen C. Cheng, Lisa Bero, Joanne E. McKenzie

Research output: Contribution to journalArticleOtherpeer-review

3 Citations (Scopus)


Background: Systematic reviews are used to inform healthcare decision making. In reviews that aim to examine the effects of organisational, policy change or public health interventions, or exposures, evidence from interrupted time series (ITS) studies may be included. A core component of many systematic reviews is meta-analysis, which is the statistical synthesis of results across studies. There is currently a lack of guidance informing the choice of meta-analysis methods for combining results from ITS studies, and there have been no studies examining the meta-analysis methods used in practice. This study therefore aims to describe current meta-analysis methods used in a cohort of reviews of ITS studies. Methods: We will identify 100 reviews that include meta-analyses of ITS studies from a search of eight electronic databases covering several disciplines (public health, psychology, education, economics). Study selection will be undertaken independently by two authors. Data extraction will be undertaken by one author, and for a random sample of the reviews, two authors. From eligible reviews we will extract details at the review level including discipline and type of interruption; at the meta-analytic level we will extract type of outcome, effect measure(s), meta-analytic methods, and any methods used to re-analyse the individual ITS studies. Descriptive statistics will be used to summarise the data. Conclusions: This review will describe the methods used to meta-analyse results from ITS studies. Results from this review will inform future methods research examining how different meta-analysis methods perform, and ultimately, the development of guidance.

Original languageEnglish
Article number110
Number of pages20
Publication statusPublished - 23 Jul 2020


  • Interrupted time series
  • meta-analysis
  • systematic review

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