TY - BOOK
T1 - Control charts for health care monitoring under overdispersion
AU - Albers, Willem
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 - Statistical Process Control
KW - Estimated parameters
KW - High quality processes
KW - Heterogeneity
KW - Geometric charts
M3 - Report
T3 - Memorandum
BT - Control charts for health care monitoring under overdispersion
PB - University of Twente, Faculty of Mathematical Sciences
CY - Enschede
ER -