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

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

doi:10.12688/f1000research.22226.2

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…Each meta‐analysis was comprised of multiple ITS studies, and therefore the data generation process involved generating data for each of the multiple ITS studies within each meta‐analysis. Meta‐analyses were generated with 3, 5 or 20 included ITS studies, to reflect the size of meta‐analyses seen in real‐world datasets [a median of 7 ITS (IQR: 6–10)], 55 and to include scenarios where estimation of between‐study variance is likely to be suboptimal (few studies, 3 ITS) and likely to be more accurate (many studies 20 ITS). Furthermore, to incorporate heterogeneity, level changes of the ITS studies within each meta‐analysis were generated with a between‐study variance of

τ_{2}^{2} =

0, 0.1, 2 0.3 2 and the slope changes with a between‐study variance of

τ_{3}^{2} =

0, 0.01, 2 0.05 28,17 …”

Section: Methodsmentioning

confidence: 99%

Evaluation of statistical methods used to meta‐analyse results from interrupted time series studies: A simulation study

Korevaar,

Turner,

Forbes

et al. 2023

Research Synthesis Methods

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Interrupted time series (ITS) are often meta‐analysed to inform public health and policy decisions but examination of the statistical methods for ITS analysis and meta‐analysis in this context is limited. We simulated meta‐analyses of ITS studies with continuous outcome data, analysed the studies using segmented linear regression with two estimation methods [ordinary least squares (OLS) and restricted maximum likelihood (REML)], and meta‐analysed the immediate level‐ and slope‐change effect estimates using fixed‐effect and (multiple) random‐effects meta‐analysis methods. Simulation design parameters included varying series length; magnitude of lag‐1 autocorrelation; magnitude of level‐ and slope‐changes; number of included studies; and, effect size heterogeneity. All meta‐analysis methods yielded unbiased estimates of the interruption effects. All random effects meta‐analysis methods yielded coverage close to the nominal level, irrespective of the ITS analysis method used and other design parameters. However, heterogeneity was frequently overestimated in scenarios where the ITS study standard errors were underestimated, which occurred for short series or when the ITS analysis method did not appropriately account for autocorrelation. The performance of meta‐analysis methods depends on the design and analysis of the included ITS studies. Although all random effects methods performed well in terms of coverage, irrespective of the ITS analysis method, we recommend the use of effect estimates calculated from ITS methods that adjust for autocorrelation when possible. Doing so will likely to lead to more accurate estimates of the heterogeneity variance.

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τ_{2}^{2} =

0, 0.1, 2 0.3 2 and the slope changes with a between‐study variance of

τ_{3}^{2} =

0, 0.01, 2 0.05 28,17 …”

Section: Methodsmentioning

confidence: 99%

Evaluation of statistical methods used to meta‐analyse results from interrupted time series studies: A simulation study

Korevaar,

Turner,

Forbes

et al. 2023

Research Synthesis Methods

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“…Empirical study 2 -Korevaar et al (13): We sourced 283 ITS datasets from 17 meta-analyses (included in 17 reviews investigating the impacts of, primarily, public health interruptions) that included results from at least two ITS studies. Full details of the methods used to select reviews, meta-analyses, and studies are available in Korevaar et al (10,21). We sourced the ITS datasets using the same methods as for Turner et al (16).…”

Section: Description Of How the Its Datasets Were Sourcedmentioning

confidence: 99%

Interrupted time series datasets from studies investigating the impact of interventions or exposures in public health and social science: A data note

Turner,

Korevaar,

Karahalios

et al. 2023

Preprint

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Objectives: The interrupted time series (ITS) design is commonly used to investigate the impact of an intervention or exposure in public health. There are many statistical methods that can be used to analyse ITS data and to meta-analyse their results. We undertook two empirical studies to investigate: i) how effect estimates (and associated statistics) compared when six statistical methods were applied to 190 real-world datasets; and ii) how meta-analysis effect estimates (and associated statistics) compared when the combinations of two ITS analysis methods and five meta-analysis methods were applied to 17 real-world meta-analyses including 283 ITS datasets. Here we present a curated repository of a subset of ITS datasets from these studies. Data description: The repository includes 430 ITS datasets curated from the two empirical studies. The datasets are diverse in the populations, interruptions and outcomes examined, and are methodologically diverse in the outcome types, aggregation time intervals, number of timepoints and segments. Most of the datasets are from public health. For each dataset, we provide the outcome value at each timepoint and the segment (indicating different interruptions), along with characteristics of the dataset. This repository may be of value for future research of ITS studies, and as a source of examples of ITS for use in teaching.

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

Korevaar¹,

Karahalios²,

Turner³

et al. 2022

Journal of Clinical Epidemiology

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

Cited by 3 publications

References 21 publications

Evaluation of statistical methods used to meta‐analyse results from interrupted time series studies: A simulation study

Evaluation of statistical methods used to meta‐analyse results from interrupted time series studies: A simulation study

Interrupted time series datasets from studies investigating the impact of interventions or exposures in public health and social science: A data note

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

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