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Statistical process control and interrupted time series: a golden opportunity for impact evaluation in quality improvement
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  • Published on: 23 September 2016
    SPC Versus GAM for hospital adverse events arising in a complex system

    Statistical process control works well when there is independence and linearity. Complex systems produce data that are often not independent, often nonlinear and display self-organisation and emergent behaviour. To say that statistical process control works when behaviour is emergent may make little sense. Increasingly adverse events like colonisation with antibiotic-resistant organisms arise in a complex system. Although...

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    Conflict of Interest:
    None declared.
  • Published on: 7 April 2016
    Statistical Process Control and Interrupted Time Series

    I read Fretheim and Tomic's article [1] with interest as I trained in frequentist stastistics and now work primarily with Stastistical Process Control (SPC) in quality improvement (QI) initiatives.

    I concur that there are missed opportunities for using Interrupted Time Series (ITS) in QI; however, I note cautions in doing so:

    Regression models applied in ITS often have the assumption of homoscedastici...

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    Conflict of Interest:
    None declared.
  • Published on: 18 December 2015
    Public website for interrupted time series and segmented regression
    • Robert G Badgett, Professor of Preventive Medicine and Public Health
    • Other Contributors:
      • Kelsey Lu, MS

    We agree with the authors that interrupted time series should be used more often (1). We also agree that the statistics are difficult. We find segmented regression to be the preferable form of interrupted time series (ITS) as traditional ITS with the Davies tests only looks for a change in slope at the breakpoint. This works well if there is not a simultaneous change or shift in the level of the outcome at the breakpoint; howev...

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    Conflict of Interest:
    None declared.