Evidence map›Paper›PMID 38341540›Full record

SynthesisBMC medical research methodology2024

Comparison of statistical methods used to meta-analyse results from interrupted time series studies: an empirical study.

Elizabeth Korevaar, Simon L Turner, Andrew B Forbes, Amalia Karahalios, Monica Taljaard, Joanne E McKenzie

Abstract readMeta-Analysis
In one paragraph

Synthesis in BMC medical research methodology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Elizabeth KorevaarSchool of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia.ORCID 0000-0001-5808-7813
Simon L TurnerSchool of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia.ORCID 0000-0001-9163-4524
Andrew B ForbesSchool of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia.ORCID 0000-0003-4269-914X
Amalia KarahaliosCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, 3010, Australia.ORCID 0000-0002-7497-1681
Monica TaljaardClinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, K1Y 4E9, Canada.ORCID 0000-0002-3978-8961
Joanne E McKenzieSchool of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia. joanne.mckenzie@monash.edu.ORCID 0000-0003-3534-1641

Funding

National Health and Medical Research Council GNT2009612
6 · The paper itself

Abstract

backgroundThe Interrupted Time Series (ITS) is a robust design for evaluating public health and policy interventions or exposures when randomisation may be infeasible. Several statistical methods are available for the analysis and meta-analysis of ITS studies. We sought to empirically compare available methods when applied to real-world ITS data.

methodsWe sourced ITS data from published meta-analyses to create an online data repository. Each dataset was re-analysed using two ITS estimation methods. The level- and slope-change effect estimates (and standard errors) were calculated and combined using fixed-effect and four random-effects meta-analysis methods. We examined differences in meta-analytic level- and slope-change estimates, their 95% confidence intervals, p-values, and estimates of heterogeneity across the statistical methods.

resultsOf 40 eligible meta-analyses, data from 17 meta-analyses including 282 ITS studies were obtained (predominantly investigating the effects of public health interruptions (88%)) and analysed. We found that on average, the meta-analytic effect estimates, their standard errors and between-study variances were not sensitive to meta-analysis method choice, irrespective of the ITS analysis method. However, across ITS analysis methods, for any given meta-analysis, there could be small to moderate differences in meta-analytic effect estimates, and important differences in the meta-analytic standard errors. Furthermore, the confidence interval widths and p-values for the meta-analytic effect estimates varied depending on the choice of confidence interval method and ITS analysis method.

conclusionsOur empirical study showed that meta-analysis effect estimates, their standard errors, confidence interval widths and p-values can be affected by statistical method choice. These differences may importantly impact interpretations and conclusions of a meta-analysis and suggest that the statistical methods are not interchangeable in practice.

Indexed as

Public HealthHumansInterrupted Time Series AnalysisEmpirical studyInterrupted time seriesMeta-analysisSegmented regressionStatistical methods

Identifiers

PMID38341540
PMCPMC10858609

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.