Control charts for health care monitoring under overdispersion

Willem/Wim Albers

    Research output: Book/ReportReportProfessional

    51 Downloads (Pure)

    Abstract

    An attractive way to control attribute data from high quality processes is to wait till r $\geq$ 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.
    Original languageUndefined
    Place of PublicationEnschede
    PublisherStatistics and Probability (SP)
    Number of pages16
    Publication statusPublished - Feb 2009

    Publication series

    NameMemorandum / Department of Applied Mathematics
    PublisherDepartment of Applied Mathematics, University of Twente
    No.1891
    ISSN (Print)1874-4850
    ISSN (Electronic)1874-4850

    Keywords

    • Average run length
    • IR-65386
    • METIS-263737
    • MSC-62P10
    • EWI-15065
    • Statistical Process Control
    • Estimated parameters
    • High quality processes
    • Heterogeneity
    • Geometric charts

    Cite this

    Albers, WW. (2009). Control charts for health care monitoring under overdispersion. (Memorandum / Department of Applied Mathematics; No. 1891). Enschede: Statistics and Probability (SP).
    Albers, Willem/Wim. / Control charts for health care monitoring under overdispersion. Enschede : Statistics and Probability (SP), 2009. 16 p. (Memorandum / Department of Applied Mathematics; 1891).
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    keywords = "Average run length, IR-65386, METIS-263737, MSC-62P10, EWI-15065, Statistical Process Control, Estimated parameters, High quality processes, Heterogeneity, Geometric charts",
    author = "Willem/Wim Albers",
    note = "eemcs-eprint-15065",
    year = "2009",
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    series = "Memorandum / Department of Applied Mathematics",
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    Albers, WW 2009, Control charts for health care monitoring under overdispersion. Memorandum / Department of Applied Mathematics, no. 1891, Statistics and Probability (SP), Enschede.

    Control charts for health care monitoring under overdispersion. / Albers, Willem/Wim.

    Enschede : Statistics and Probability (SP), 2009. 16 p. (Memorandum / Department of Applied Mathematics; No. 1891).

    Research output: Book/ReportReportProfessional

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    N2 - An attractive way to control attribute data from high quality processes is to wait till r $\geq$ 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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    KW - Average run length

    KW - IR-65386

    KW - METIS-263737

    KW - MSC-62P10

    KW - EWI-15065

    KW - Statistical Process Control

    KW - Estimated parameters

    KW - High quality processes

    KW - Heterogeneity

    KW - Geometric charts

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    Albers WW. Control charts for health care monitoring under overdispersion. Enschede: Statistics and Probability (SP), 2009. 16 p. (Memorandum / Department of Applied Mathematics; 1891).