Sample size and power considerations for ordinary least squares interrupted time series analysis: a simulation study
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 eff...
| المؤلفون الرئيسيون: | , , , , |
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| التنسيق: | Journal article |
| منشور في: |
Dove Medical Press
2019
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| _version_ | 1826305411160997888 |
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| author | Hawley, S Ali, MS Berencsi, K Judge, A Prieto-Alhambra, D |
| author_facet | Hawley, S Ali, MS Berencsi, K Judge, A Prieto-Alhambra, D |
| author_sort | Hawley, S |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T06:32:28Z |
| format | Journal article |
| id | oxford-uuid:f67a8b5d-525c-4a74-a69a-c408f59f7324 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T06:32:28Z |
| publishDate | 2019 |
| publisher | Dove Medical Press |
| record_format | dspace |
| spelling | oxford-uuid:f67a8b5d-525c-4a74-a69a-c408f59f73242022-03-27T12:35:21ZSample size and power considerations for ordinary least squares interrupted time series analysis: a simulation studyJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:f67a8b5d-525c-4a74-a69a-c408f59f7324Symplectic Elements at OxfordDove Medical Press2019Hawley, SAli, MSBerencsi, KJudge, APrieto-Alhambra, DInterrupted 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. |
| spellingShingle | Hawley, S Ali, MS Berencsi, K Judge, A Prieto-Alhambra, D Sample size and power considerations for ordinary least squares interrupted time series analysis: a simulation study |
| title | Sample size and power considerations for ordinary least squares interrupted time series analysis: a simulation study |
| title_full | Sample size and power considerations for ordinary least squares interrupted time series analysis: a simulation study |
| title_fullStr | Sample size and power considerations for ordinary least squares interrupted time series analysis: a simulation study |
| title_full_unstemmed | Sample size and power considerations for ordinary least squares interrupted time series analysis: a simulation study |
| title_short | Sample size and power considerations for ordinary least squares interrupted time series analysis: a simulation study |
| title_sort | sample size and power considerations for ordinary least squares interrupted time series analysis a simulation study |
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