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      Diagnostic Value of Run Chart Analysis: Using Likelihood Ratios to Compare Run Chart Rules on Simulated Data Series

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      PLoS ONE

      Public Library of Science

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          Abstract

          Run charts are widely used in healthcare improvement, but there is little consensus on how to interpret them. The primary aim of this study was to evaluate and compare the diagnostic properties of different sets of run chart rules. A run chart is a line graph of a quality measure over time. The main purpose of the run chart is to detect process improvement or process degradation, which will turn up as non-random patterns in the distribution of data points around the median. Non-random variation may be identified by simple statistical tests including the presence of unusually long runs of data points on one side of the median or if the graph crosses the median unusually few times. However, there is no general agreement on what defines “unusually long” or “unusually few”. Other tests of questionable value are frequently used as well. Three sets of run chart rules (Anhoej, Perla, and Carey rules) have been published in peer reviewed healthcare journals, but these sets differ significantly in their sensitivity and specificity to non-random variation. In this study I investigate the diagnostic values expressed by likelihood ratios of three sets of run chart rules for detection of shifts in process performance using random data series. The study concludes that the Anhoej rules have good diagnostic properties and are superior to the Perla and the Carey rules.

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          Diagnostic tests 4: likelihood ratios.

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

            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.
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              Author and article information

              Contributors
              Role: Academic Editor
              Journal
              PLoS One
              PLoS ONE
              plos
              plosone
              PLoS ONE
              Public Library of Science (San Francisco, CA USA )
              1932-6203
              23 March 2015
              2015
              : 10
              : 3
              Affiliations
              Centre of Diagnostic Evaluation, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
              Cardiff University, UNITED KINGDOM
              Author notes

              Competing Interests: The author has declared that no competing interests exist.

              Conceived and designed the experiments: JA. Performed the experiments: JA. Analyzed the data: JA. Contributed reagents/materials/analysis tools: JA. Wrote the paper: JA.

              Article
              PONE-D-14-50629
              10.1371/journal.pone.0121349
              4370372
              25799549

              This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

              Page count
              Figures: 3, Tables: 3, Pages: 9
              Product
              Funding
              The author has no support or funding to report.
              Categories
              Research Article
              Custom metadata
              All relevant data are within the paper and its Supporting Information files.

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