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Run Charts Revisited: A Simulation Study of Run Chart Rules for Detection of Non-Random Variation in Health Care Processes

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  • Jacob Anhøj
  • Anne Vingaard Olesen

Abstract

Background: A run chart is a line graph of a measure plotted over time with the median as a horizontal line. The main purpose of the run chart is to identify process improvement or degradation, which may be detected by statistical tests for non-random patterns in the data sequence. Methods: We studied the sensitivity to shifts and linear drifts in simulated processes using the shift, crossings and trend rules for detecting non-random variation in run charts. Results: The shift and crossings rules are effective in detecting shifts and drifts in process centre over time while keeping the false signal rate constant around 5% and independent of the number of data points in the chart. The trend rule is virtually useless for detection of linear drift over time, the purpose it was intended for.

Suggested Citation

  • Jacob Anhøj & Anne Vingaard Olesen, 2014. "Run Charts Revisited: A Simulation Study of Run Chart Rules for Detection of Non-Random Variation in Health Care Processes," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-13, November.
    • Handle: RePEc:plo:pone00:0113825
    DOI: 10.1371/journal.pone.0113825
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    Cited by:

    1. Jacob Anhøj, 2015. "Diagnostic Value of Run Chart Analysis: Using Likelihood Ratios to Compare Run Chart Rules on Simulated Data Series," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-9, March.
    2. Tore Wentzel-Larsen & Jacob Anhøj, 2019. "Joint distribution for number of crossings and longest run in independent Bernoulli observations. The R package crossrun," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-11, October.
    3. Puisa, Romanas & Montewka, Jakub & Krata, Przemyslaw, 2023. "A framework estimating the minimum sample size and margin of error for maritime quantitative risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    4. Jacob Anhøj & Tore Wentzel-Larsen, 2020. "Smooth operator: Modifying the Anhøj rules to improve runs analysis in statistical process control," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-13, June.

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