4.6 Review

Methodological systematic review recommends improvements to conduct and reporting when meta-analyzing interrupted time series studies

Journal

JOURNAL OF CLINICAL EPIDEMIOLOGY
Volume 145, Issue -, Pages 55-69

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.jclinepi.2022.01.010

Keywords

Systematic review; Meta-analysis; Interrupted time series; Segmented regression; Statistical methods; Reporting quality

Funding

  1. Australian Government Re-search Training Program (RTP) Scholarship [1143429]
  2. NHMRC Career Devel-opment Fellowship [GNT1145273]
  3. AustralianNational Health and Medical Research Council (NHMRC)
  4. Making the best use of avail-able evidence
  5. Australian Government Re-search Training Program (RTP) Scholarship
  6. NHMRC Career Devel-opment Fellowship [1143429]
  7. Australian National Health and Medical Research Council (NHMRC) [GNT1145273]

Ask authors/readers for more resources

This study aimed to examine the statistical approaches, methods, and completeness of reporting in reviews that meta-analyze results from interrupted time series (ITS). The results showed that although most reviews used two-stage meta-analysis and fitted random effects models, reporting of the statistical methods and ITS characteristics was often incomplete. The study suggests that improvement is needed in the conduct and reporting of these reviews.
Objectives: Interrupted Time Series (ITS) are a type of nonrandomized design commonly used to evaluate public health policy interventions, and the impact of exposures, at the population level. Meta-analysis may be used to combine results from ITS across studies (in the context of systematic reviews) or across sites within the same study. We aimed to examine the statistical approaches, methods, and completeness of reporting in reviews that meta-analyze results from ITS. Study Design and Settings: Eight electronic databases were searched to identify reviews (published 2000-2019) that meta-analyzed at least two ITS. Characteristics of the included reviews, the statistical methods used to analyze the ITS and meta-analyze their results, effect measures, and risk of bias assessment tools were extracted. Results: Of the 4213 identified records, 54 reviews were included. Nearly all reviews (94%) used two-stage meta-analysis, most commonly fitting a random effects model (69%). Among the 41 reviews that re-analyzed the ITS, linear regression (39%) and ARIMA (20%) were most commonly used; 38% adjusted for autocorrelation. The most common effect measure meta-analyzed was an immediate level-change (46/54). Reporting of the statistical methods and ITS characteristics was often incomplete. Conclusion: Improvement is needed in the conduct and reporting of reviews that meta-analyze results from ITS. (c) 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http:// creativecommons.org/ licenses/ by- nc- nd/ 4.0/ )

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