Evidence map›Paper›PMID 30881136›Full record

ArticleClinical epidemiology2019

Sample size and power considerations for ordinary least squares interrupted time series analysis: a simulation study.

Samuel Hawley, M Sanni Ali, Klara Berencsi, Andrew Judge, Daniel Prieto-Alhambra

Abstract read
In one paragraph

Article in Clinical epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed
–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

50 citing papers in PubMed.

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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

5 authors.

Samuel HawleyCentre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK, samuel.hawley@ndorms.ox.ac.uk.
M Sanni AliCentre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK, samuel.hawley@ndorms.ox.ac.uk.
Klara BerencsiCentre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK, samuel.hawley@ndorms.ox.ac.uk.
Andrew JudgeCentre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK, samuel.hawley@ndorms.ox.ac.uk.
Daniel Prieto-AlhambraCentre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK, samuel.hawley@ndorms.ox.ac.uk.

Funding

Department of Health CS-2013-13-012
6 · The paper itself

Abstract

Interrupted time series (ITS) analysis is being increasingly used in epidemiology. Despite its growing popularity, there is a scarcity of guidance on power and sample size considerations within the ITS framework. Our aim of this study was to assess the statistical power to detect an intervention effect under various real-life ITS scenarios. ITS datasets were created using Monte Carlo simulations to generate cumulative incidence (outcome) values over time. We generated 1,000 datasets per scenario, varying the number of time points, average sample size per time point, average relative reduction post intervention, location of intervention in the time series, and reduction mediated via a 1) slope change and 2) step change. Performance measures included power and percentage bias. We found that sample size per time point had a large impact on power. Even in scenarios with 12 pre-intervention and 12 post-intervention time points with moderate intervention effect sizes, most analyses were underpowered if the sample size per time point was low. We conclude that various factors need to be collectively considered to ensure adequate power for an ITS study. We demonstrate a means of providing insight into underlying sample size requirements in ordinary least squares (OLS) ITS analysis of cumulative incidence measures, based on prespecified parameters and have developed Stata code to estimate this.

Indexed as

biasepidemiologyinterrupted time seriespowersample size

Identifiers

PMID30881136
PMCPMC6394245

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.