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

Authors Hawley S, Ali MS, Berencsi K, Judge A, Prieto-Alhambra D

Received 9 June 2018

Accepted for publication 14 September 2018

Published 25 February 2019 Volume 2019:11 Pages 197—205


Checked for plagiarism Yes

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Peer reviewer comments 3

Editor who approved publication: Professor Vera Ehrenstein

Samuel Hawley,1 M Sanni Ali,1,2 Klara Berencsi,1 Andrew Judge1,3,4 Daniel Prieto-Alhambra1,5

1Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK; 2Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK; 3MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK; 4Department of Translational Health Sciences, University of Bristol, Bristol, UK; 5GREMPAL Research Group, Idiap Jordi Gol and CIBERFes, Universitat Autònoma de Barcelona and Instituto de Salud Carlos III, Barcelona, Spain

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.

Keywords: epidemiology, interrupted time series, sample size, power, bias

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