### Abstract

Original language | Undefined |
---|---|

Place of Publication | Enschede |

Publisher | Statistics and Probability (SP) |

Number of pages | 16 |

Publication status | Published - Feb 2009 |

### Publication series

Name | Memorandum / Department of Applied Mathematics |
---|---|

Publisher | Department 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

*Control charts for health care monitoring under overdispersion*. (Memorandum / Department of Applied Mathematics; No. 1891). Enschede: Statistics and Probability (SP).

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*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.

Research output: Book/Report › Report › Professional

TY - BOOK

T1 - Control charts for health care monitoring under overdispersion

AU - Albers, Willem/Wim

N1 - eemcs-eprint-15065

PY - 2009/2

Y1 - 2009/2

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.

AB - 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.

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

M3 - Report

T3 - Memorandum / Department of Applied Mathematics

BT - Control charts for health care monitoring under overdispersion

PB - Statistics and Probability (SP)

CY - Enschede

ER -