Dynamic probability control limits for risk-adjusted Bernoulli CUSUM charts

Zhang, Xiang; Woodall, William H.

doi:10.1002/sim.6547

Cited by 45 publications

(40 citation statements)

References 26 publications

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“…With their approach, the control limit for a particular time period was not determined until the sample size became known. Zhang and Woodall applied this dynamic method of determining probability control limits to the risk‐adjusted Bernoulli CUSUM chart. The risk‐adjusted Bernoulli CUSUM chart was first developed by Steiner et al in order to account for the variability in the prior risks of surgical adverse outcomes for different patients.…”

Section: Introductionmentioning

confidence: 99%

Dynamic probability control limits for CUSUM charts for monitoring proportions with time‐varying sample sizes

Aytaçoğlu

Woodall

2019

Quality & Reliability Eng

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We consider the problem of monitoring a proportion with time‐varying sample sizes. Control charts are generally designed by assuming a fixed sample size or a priori knowledge of a sample size probability distribution. Sometimes, it is not possible to know, or accurately estimate, a sample size distribution or the distribution may change over time. An improper assumption for the sample size distribution could lead to undesirable performance of the control chart. To handle this problem, we propose the use of dynamic probability control limits (DPCLs) which are determined successively as the sample sizes become known. The method is based on keeping the conditional probability of a false alarm at a predetermined level given that there has not been any earlier false alarm. The control limits dynamically change, and the in‐control performance of the chart can be controlled at the desired level for any sequence of sample sizes. The simulation results support this result showing that there is no need for any assumption of a sample size distribution with the use of this proposed approach.

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Section: Introductionmentioning

confidence: 99%

Dynamic probability control limits for CUSUM charts for monitoring proportions with time‐varying sample sizes

Aytaçoğlu

Woodall

2019

Quality & Reliability Eng

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“…The dynamic probability control limits are determined by applying the method proposed by Zhang and Woodall . The charts are referred to as RA‐CUSUM charts with DPCLs.…”

Section: Methodsmentioning

confidence: 99%

“…The advantage of the DPCLs is that one can obtain the desired in‐control ARL and maintain the conditional false alarm rates at the specified level without requiring any information or assumptions about the patient population. Also, with DPCLs, it is possible to signal sooner for process changes that occur near the start of the monitoring process …”

Section: Methodsmentioning

confidence: 99%

“…Unlike the RA‐CUSUM charts with a constant control limit assessed by Jones and Steiner, the ones with DPCLs have been shown to be able to deliver consistent desirable in‐control performance regardless of the patient population . Therefore, one does not have to estimate the patient mix as with their approach.…”

Section: Methodsmentioning

confidence: 99%

“…Zhang and Woodall recently developed a simulation‐based method to determine the dynamic probability control limits (DPCLs) for RA‐CUSUM charts based on the method of Shen et al The Shen et al method was developed for an application involving the exponentially weighted moving average chart to monitor Poisson count data with time‐varying population sizes. By maintaining the probability of a false alarm at a constant level conditional on no false alarm for previous observations, a concept initially proposed by Margavio et al , the charts with DPCLs give desirably consistent in‐control performance with approximately geometrically distributed in‐control run lengths.…”

Section: Introductionmentioning

confidence: 99%

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Reduction of the Effect of Estimation Error on In-control Performance for Risk-adjusted Bernoulli CUSUM Chart with Dynamic Probability Control Limits

Zhang

Woodall

2016

Qual. Reliab. Engng. Int.

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The in‐control performance of any control chart is highly associated with the accuracy of estimation for the in‐control parameter(s). For the risk‐adjusted Bernoulli cumulative sum (CUSUM) chart with a constant control limit, it had been shown that the estimation error could have a substantial effect on the in‐control performance. In our study, we examine the effect of estimation error on the in‐control performance of the risk‐adjusted Bernoulli CUSUM chart with dynamic probability control limits (DPCLs). Our simulation results show that the in‐control performance of risk‐adjusted Bernoulli CUSUM chart with DPCLs is also affected by the estimation error. The most important factors affecting estimation error are the specified desired in‐control average run length, the Phase I sample size, and the adverse event rate. However, the effect of estimation error is uniformly smaller for the risk‐adjusted Bernoulli CUSUM chart with DPCLs than for the corresponding chart with a constant control limit under various realistic scenarios. In addition, we found a substantial reduction in the mean and variation of the standard deviation of the in‐control run length when DPCLs are used. Therefore, use of DPCLs has yet another advantage when designing a risk‐adjusted Bernoulli CUSUM chart. Copyright © 2016 John Wiley & Sons, Ltd.

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Medical device active surveillance of spontaneous reports: A literature review of signal detection methods

Chung

Etter

Yoo

2020

Pharmacoepidemiology and Drug

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Purpose: The collection and analysis of real-world data for the active monitoring of medical device performance and safety has become increasingly important. Spontaneous reports, such as those in the Food & Drug Administration's (FDA's) Manufacturer and User Facility Device Experience (MAUDE), provide early warning of potential issues with marketed devices. This review synthesizes the current literature on medical device surveillance signal detection and provides a framework for application of methods to active surveillance of spontaneous reports.Methods: Ovid MEDLINE, Ovid Embase, Scopus, and PubMed databases were systematically searched up to January 2019. Additionally, five methods articles from pharmacovigilance were added that had potential applications to medical devices.Results: Among 105 articles included, the most common source of data (84%) was registries; median time between data collection and publication was 8 years. Surgical procedure outcome signal detection articles comprised 83% while 14% were on device outcome signal detection. The most common family of methods cited (70%) was Sequential Probability Ratio.Conclusion: Application of any signal detection algorithm requires careful consideration of influential factors, data limitations, and algorithmic assumptions.We recommend approaches using disproportionality, statistical process control, and sequential probability tests and provide R packages to further development efforts. The small number of published examples suggest that further development of statistical methods and technological solutions to analyze large amounts of data for device safety and performance is needed. Fundamental differences in products, data infrastructure, and the regulatory landscape suggest that medical device vigilance requires its own body of research distinct from pharmacovigilance.

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Dynamic probability control limits for risk-adjusted Bernoulli CUSUM charts

Cited by 45 publications

References 26 publications

Dynamic probability control limits for CUSUM charts for monitoring proportions with time‐varying sample sizes

Dynamic probability control limits for CUSUM charts for monitoring proportions with time‐varying sample sizes

Reduction of the Effect of Estimation Error on In-control Performance for Risk-adjusted Bernoulli CUSUM Chart with Dynamic Probability Control Limits

Medical device active surveillance of spontaneous reports: A literature review of signal detection methods

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