Sample size and power considerations for ordinary least squares interrupted time series analysis : a simulation study
Hawley, Samuel (University of Oxford. Centre for Statistics in Medicine)
Ali, M. Sanni (Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine)
Berencsi, Klara (University of Oxford. Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences (NDORMS))
Judge, Andrew (Department of Translational Health Sciences, University of Bristol, Bristol, UK)
Prieto-Alhambra, Daniel (Universitat Autònoma de Barcelona)

Fecha:	2019
Resumen:	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.
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Epidemiology ; Interrupted time series ; Sample size ; Power ; Bias
Publicado en:	Clinical Epidemiology, Vol. 11 (2019) , p. 197-205, ISSN 1179-1349

DOI: 10.2147/CLEP.S176723
PMID: 30881136

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