Abstract
An attractive way to control attribute data from high quality processes is to wait till r ≥ 1 failures have occurred. The choice of r in such negative binomial charts is dictated by how much the failure rate is supposed to change during Out-of-Control. However, these results have been derived for the case of homogeneous data. Especially in health care monitoring, (groups of) patients will often show large heterogeneity. In the present paper we will show how such overdispersion can be taken into account. In practice, typically neither the average failure rate, nor the overdispersion parameter(s), will be known. Hence we shall also derive and analyze the estimated version of the new chart.
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Open Access This is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License (https://creativecommons.org/licenses/by-nc/2.0), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
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Albers, W. Control charts for health care monitoring under overdispersion. Metrika 74, 67–83 (2011). https://doi.org/10.1007/s00184-009-0290-z
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DOI: https://doi.org/10.1007/s00184-009-0290-z
Keywords
- Statistical process control
- High-quality processes
- Geometric charts
- Average run length
- Estimated parameters
- Heterogeneity