A Control Chart Based on Cumulative Scores

A number of methods have been proposed for detecting an increase in the incidence rate of a rare health event, such as a congenital malformation. Among these are the sets method, two modifications of the sets method, and the CUSUM method based on the Poisson distribution. We consider the situation where data are observed as a sequence of Bernoulli trials and propose the Bernoulli CUSUM chart as a desirable method for the surveillance of rare health events. We compared the performance of the sets method and its modifications with that of the Bernoulli CUSUM chart under a wide variety of circumstances. Chart design parameters were chosen to satisfy a minimax criteria. We used the steady-state average run length to measure chart performance instead of the average run length (ARL), which was used in nearly all previous comparisons involving the sets method or its modifications. Except in a very few instances, we found that the Bernoulli CUSUM chart has better steady-state ARL performance than the sets method and its modifications for the extensive number of cases considered. Thus, we recommend the use of the Bernoulli CUSUM chart to monitor small incidence rates and provide practical advice for its implementation.

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“…Sitter et al [1] derived the ANM for the SHDA method, and the ANM for the CUSCORE was given originally by Munford [15].…”

Section: Criteria For Determining the Design Parametersmentioning

confidence: 99%

A comparison of surveillance methods for small incidence rates

Sego

Woodall

Reynolds

2008

Statistics in Medicine

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“…Recently, Radaelli (1992) discussed a very simple CUSUM-type chart, i.e. the CUSCORE chart, which was derived from a quality control scheme introduced by Munford (1980) for controlling processes with independent identically distributed normal data.…”

Section: Introductionmentioning

confidence: 99%

Poisson and negative binomial dynamics for counted data under CUSUM-type charts

Radaelli

1994

Journal of Applied Statistics

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Control charts for counted data are commonly designed 'assuming that counts follow Poisson dynamics. However, in various real situations, the true underlying dynamics of the events are more properly modelled by a negative binomial process. This paper examines the consequences of the Poisson approximation to negative binomial dynamics for counts under CUSUM-type schemes. It is essentially found that, on setting up Poisson dyamics for an underlying negative binomial data stmcture, the real incontrol average run length decreases, whereas the sensitivity of the chart is affected less. These results warn against the routine use of the Poisson assumption in planning control charts for counts.

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“…3, (4)], and showed that it has much better performance than the original version. Munford (1980) proposcd a cumulative scoring scheme in which a score of -1, +1 or 0 is assigned to each sample mean depending on whether the sampie mean lies below, above or within the interval (7 -L,F + L), respectively. The Downloaded by [University of Chicago Library] at 12:17 08 December 2014 scheme will issue a warning signal as soon as the accumulated score reaches a criticai vaiue, h .…”

mentioning

confidence: 99%

A cumulative score control scheme for detecting process shifts

Cheng

1995

Communications in Statistics - Theory and Methods

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A Control Chart Based on Cumulative Scores

Cited by 33 publications

References 15 publications

A comparison of surveillance methods for small incidence rates

A comparison of surveillance methods for small incidence rates

Poisson and negative binomial dynamics for counted data under CUSUM-type charts

A cumulative score control scheme for detecting process shifts

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