The Experts below are selected from a list of 282 Experts worldwide ranked by ideXlab platform
Stefan H Steiner - One of the best experts on this subject based on the ideXlab platform.
-
monitoring surgical performance using risk adjusted Cumulative Sum charts
Biostatistics, 2000Co-Authors: Stefan H Steiner, Richard J Cook, V T Farewell, Tom TreasureAbstract:SumMARY The Cumulative Sum (CUSum) procedure is a graphical method that is widely used for quality monitoring in industrial settings. More recently it has been used to monitor surgical outcomes whereby it ‘signals’ if sufficient evidence has accumulated that there has been a change in the surgical failure rate. A limitation of the standard CUSum procedure in this context is that since it is simply based on the observed surgical outcomes, it may signal as a result of changes in the referral pattern, such as an increased proportion of high-risk patients, rather than due to a change in the actual surgical performance. We describe a new CUSum procedure that adjusts for each patient’s pre-operative risk of surgical failure through the use of a likelihood-based scoring method. The procedure is therefore ideally suited for settings where there is a variable mix of patients over time.
-
monitoring paired binary surgical outcomes using Cumulative Sum charts
Statistics in Medicine, 1999Co-Authors: Stefan H Steiner, Richard J Cook, V T FarewellAbstract:Correlated binary data are encountered in many areas of medical research, system reliability and quality control. For monitoring failures rates in such situations, simultaneous bivariate Cumulative Sum (CUSum) charts with the addition of secondary control limits are proposed. Using an approach based on a Markov chain model, the run length properties of such a monitoring scheme can be determined for sudden, or gradual, changes in the failure rates. The proposed control charts are easy to implement, and are shown to be very effective at detecting small changes in the rate of undesirable outcomes, especially when the changes are gradual. This procedure is illustrated using bivariate outcome data arising from a series of paediatric surgeries. The methodology is sufficiently general that it may be adapted for multivariate normal, binomial or Poisson responses. Copyright © 1999 John Wiley & Sons, Ltd.
-
grouped data sequential probability ratio tests and Cumulative Sum control charts
Technometrics, 1996Co-Authors: Stefan H Steiner, Lee P Geyer, George O WesolowskyAbstract:Methodology is proposed for the design of sequential methods when data are obtained by gauging articles into groups, Exact expressions are obtained for the operating characteristics and average sampling number of Wald sequential probability ratio tests and for the average run length of Cumulative Sum (CUSum) schemes based on grouped data. Step-by-step design algorithms are provided to assist the practitioner. The methodology is illustrated asSuming a normal process with known standard deviation in which we wish to detect shifts in the mean. An example from a progressive die operation is presented. The methods proposed are simple to implement and are an economical alternative to variables-data-based sequential sampling plans and CUSum control charts.
V T Farewell - One of the best experts on this subject based on the ideXlab platform.
-
monitoring surgical performance using risk adjusted Cumulative Sum charts
Biostatistics, 2000Co-Authors: Stefan H Steiner, Richard J Cook, V T Farewell, Tom TreasureAbstract:SumMARY The Cumulative Sum (CUSum) procedure is a graphical method that is widely used for quality monitoring in industrial settings. More recently it has been used to monitor surgical outcomes whereby it ‘signals’ if sufficient evidence has accumulated that there has been a change in the surgical failure rate. A limitation of the standard CUSum procedure in this context is that since it is simply based on the observed surgical outcomes, it may signal as a result of changes in the referral pattern, such as an increased proportion of high-risk patients, rather than due to a change in the actual surgical performance. We describe a new CUSum procedure that adjusts for each patient’s pre-operative risk of surgical failure through the use of a likelihood-based scoring method. The procedure is therefore ideally suited for settings where there is a variable mix of patients over time.
-
monitoring paired binary surgical outcomes using Cumulative Sum charts
Statistics in Medicine, 1999Co-Authors: Stefan H Steiner, Richard J Cook, V T FarewellAbstract:Correlated binary data are encountered in many areas of medical research, system reliability and quality control. For monitoring failures rates in such situations, simultaneous bivariate Cumulative Sum (CUSum) charts with the addition of secondary control limits are proposed. Using an approach based on a Markov chain model, the run length properties of such a monitoring scheme can be determined for sudden, or gradual, changes in the failure rates. The proposed control charts are easy to implement, and are shown to be very effective at detecting small changes in the rate of undesirable outcomes, especially when the changes are gradual. This procedure is illustrated using bivariate outcome data arising from a series of paediatric surgeries. The methodology is sufficiently general that it may be adapted for multivariate normal, binomial or Poisson responses. Copyright © 1999 John Wiley & Sons, Ltd.
Lingyun Zhang - One of the best experts on this subject based on the ideXlab platform.
-
risk adjusted Cumulative Sum charting procedure based on multiresponses
Journal of the American Statistical Association, 2015Co-Authors: Xu Tang, Lingyun ZhangAbstract: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 st...
Tom Treasure - One of the best experts on this subject based on the ideXlab platform.
-
monitoring surgical performance using risk adjusted Cumulative Sum charts
Biostatistics, 2000Co-Authors: Stefan H Steiner, Richard J Cook, V T Farewell, Tom TreasureAbstract:SumMARY The Cumulative Sum (CUSum) procedure is a graphical method that is widely used for quality monitoring in industrial settings. More recently it has been used to monitor surgical outcomes whereby it ‘signals’ if sufficient evidence has accumulated that there has been a change in the surgical failure rate. A limitation of the standard CUSum procedure in this context is that since it is simply based on the observed surgical outcomes, it may signal as a result of changes in the referral pattern, such as an increased proportion of high-risk patients, rather than due to a change in the actual surgical performance. We describe a new CUSum procedure that adjusts for each patient’s pre-operative risk of surgical failure through the use of a likelihood-based scoring method. The procedure is therefore ideally suited for settings where there is a variable mix of patients over time.
Richard J Cook - One of the best experts on this subject based on the ideXlab platform.
-
monitoring surgical performance using risk adjusted Cumulative Sum charts
Biostatistics, 2000Co-Authors: Stefan H Steiner, Richard J Cook, V T Farewell, Tom TreasureAbstract:SumMARY The Cumulative Sum (CUSum) procedure is a graphical method that is widely used for quality monitoring in industrial settings. More recently it has been used to monitor surgical outcomes whereby it ‘signals’ if sufficient evidence has accumulated that there has been a change in the surgical failure rate. A limitation of the standard CUSum procedure in this context is that since it is simply based on the observed surgical outcomes, it may signal as a result of changes in the referral pattern, such as an increased proportion of high-risk patients, rather than due to a change in the actual surgical performance. We describe a new CUSum procedure that adjusts for each patient’s pre-operative risk of surgical failure through the use of a likelihood-based scoring method. The procedure is therefore ideally suited for settings where there is a variable mix of patients over time.
-
monitoring paired binary surgical outcomes using Cumulative Sum charts
Statistics in Medicine, 1999Co-Authors: Stefan H Steiner, Richard J Cook, V T FarewellAbstract:Correlated binary data are encountered in many areas of medical research, system reliability and quality control. For monitoring failures rates in such situations, simultaneous bivariate Cumulative Sum (CUSum) charts with the addition of secondary control limits are proposed. Using an approach based on a Markov chain model, the run length properties of such a monitoring scheme can be determined for sudden, or gradual, changes in the failure rates. The proposed control charts are easy to implement, and are shown to be very effective at detecting small changes in the rate of undesirable outcomes, especially when the changes are gradual. This procedure is illustrated using bivariate outcome data arising from a series of paediatric surgeries. The methodology is sufficiently general that it may be adapted for multivariate normal, binomial or Poisson responses. Copyright © 1999 John Wiley & Sons, Ltd.
