Methods for Monitoring Multiple Proportions When Inspecting Continuously

Ryan, Anne G.; Wells, Lee J.; Woodall, William H.

doi:10.1080/00224065.2011.11917860

Cited by 45 publications

(30 citation statements)

References 17 publications

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“…It makes good sense to consider more than two outcomes if we wish to extract more information and hence improve the sensitivity in monitoring surgical performance. The idea of using more than two outcomes was also mentioned by Ryan, Wells, and Woodall (2011). In Section 2, we develop a risk-adjusted CUSUM charting procedure based on more than two outcomes.…”

Section: Introductionmentioning

confidence: 99%

Risk-Adjusted Cumulative Sum Charting Procedure Based on Multiresponses

Tang¹,

Gan²,

Zhang³

2015

Journal of the American Statistical Association

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The cumulative sum charting procedure is traditionally used in the manufacturing industry for monitoring the quality of products. Recently, it has been extended to monitoring surgical outcomes. Unlike a manufacturing process where the raw material is usually reasonably homogeneous, patients' risks of surgical failure are usually different. It has been proposed in the literature that the binary outcomes from a surgical procedure be adjusted using the preoperative risk based on a likelihood-ratio scoring method. Such a crude classification of surgical outcome is naive. It is unreasonable to regard a patient who has a full recovery, the same quality outcome as another patient who survived but remained bed-ridden for life. For a patient who survives an operation, there can be many different grades of recovery. Thus, it makes sense to consider a risk-adjusted cumulative sum charting procedure based on more than two outcomes to better monitor surgical performance. In this article, we develop such a chart and study its performance.

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Section: Introductionmentioning

confidence: 99%

Risk-Adjusted Cumulative Sum Charting Procedure Based on Multiresponses

Tang¹,

Gan²,

Zhang³

2015

Journal of the American Statistical Association

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“…Montgomery [13]), and recently there has been a trend in the literature to investigate monitoring products that are characterized by multiple attributes, i.e., the data is captured overtime and a control charting procedure is used to identify whether a process issue exists (see e.g. Topalidou and Psarakis [18], and Ryan et al [15]), which showed that there is a continued interest in this area and the multiple-class sampling might be needed for some practical applications and theoretical researches. This paper introduces six new three-class attributes single sampling plans and different OC functions are constructed separately.…”

Section: Introductionmentioning

confidence: 99%

A Design of Attributes Single Sampling Plans for Three-Class Products

Liu

Cui

2013

Quality Technology & Quantitative Management

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______________________________________________________________________ Abstract: In this paper, six new three-class attributes single sampling plans are introduced, and the corresponding operating characteristic (OC) functions are constructed separately. An important concept is introduced which is named non-conforming equivalent number and then all sampling plans can be considered as . In order to design sampling plans, the propositions of OC function are summarized and an approach is proposed to build a set of equations by using the four specified quantities: ( , ) e n c D E 0 1 , , , P P and important points on the 1 p p 2 plane. Several principles should be followed and the number of equations should be equal to the number of parameters required in a sampling plan when using this approach. Finally, numerical examples and some discussions based on numerical computation results are given to illustrate the obtained results.

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“…Recent developments for monitoring binomial data include Xie et al, Huang et al, Huang et al, and Wang and Reynolds . As to the monitoring of multinomial data, one can refer to the probability tree method with h − 1 stages for h categories developed by Duran and Albin and cumulative sum (CUSUM) chart based on likelihood ratio test suggested by Ryan et al To follow up, Weiß and Yashchin made more recent contributions to monitoring multinomial data. These works have been advanced by the latest generalized likelihood ratio (GLR) control chart suggested by Lee et al, who considered unknown direction and size of the process shifts.…”

Section: Introductionmentioning

confidence: 99%

Latent change‐point detection in ordinal categorical data

Wang

Ding

2018

Quality & Reliability Eng

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Statistical process control (SPC) techniques have been widely used for online surveillance and offline diagnosis in many applications. Because of cost constraint or technical difficulty, it is quite common that the quality of products or service is measured by ordinal factors with ordered attribute levels such as excellent, acceptable, and unacceptable. This article studies phase I analysis of such ordinal categorical processes to identify change points. By assuming that attribute levels are determined by a latent continuous variable, this work suggests a modified log‐linear model to characterize ordinal information among the attribute levels. Then a change‐point detection method is proposed on the basis of the generalized likelihood ratio test (GLRT). Simulation results prove the method's strong detection power, high diagnostic accuracy, and robustness under various distributions of the latent variable.

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Methods for Monitoring Multiple Proportions When Inspecting Continuously

Cited by 45 publications

References 17 publications

Risk-Adjusted Cumulative Sum Charting Procedure Based on Multiresponses

Risk-Adjusted Cumulative Sum Charting Procedure Based on Multiresponses

A Design of Attributes Single Sampling Plans for Three-Class Products

Latent change‐point detection in ordinal categorical data

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