A risk-adjusted EWMA chart with dynamic probability control limits for monitoring survival time

Tighkhorshid, Elahe; Amiri, Amirhossein; Amirkhani, Farzad

doi:10.1080/03610918.2019.1667393

Cited by 13 publications

(4 citation statements)

References 26 publications

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“…A similar issue can arise when the risk distribution of the patient population changes over time. For the second issue dynamic control limits have been proposed by multiple studies (an overview is given in Tighkhorshid et al [30]). Dynamic control limits could however be used to solve both of these issues simultaneously.…”

Section: Discussionmentioning

confidence: 99%

Inspecting the quality of care: a comparison of CUSUM methods for inter hospital performance

Gomon

Sijmons

Putter

et al. 2023

Preprint

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Background During the past 14 years, a clinical audit has been used in the Netherlands to provide hospitals with data on their performance in colorectal cancer care. Continuous feedback on the quality of care provided at each hospital is essential to improve patient outcomes. It is unclear which methods should be used to generate most informative output for the identification of potential quality issues. Our aim is to compare the commonly employed funnel plot with existing cumulative sum (CUSUM) methodology for the evaluation of postoperative survival and hospital stay outcomes of patients who underwent colorectal surgery in the Netherlands. Methods Data from the Dutch ColoRectal Audit on 25367 patients in the Netherlands who underwent surgical resection for colorectal cancer between 2019 and 2021 is used to compare four methods for the detection of deviations in the quality of care. Two methods based on binary outcomes (funnel plot, binary CUSUM) and two CUSUM charts based on survival outcomes (BK-CUSUM and CGR-CUSUM) are considered. A novel approach for determining hospital specific control limits for CUSUM charts is proposed. The ability to detect deviations as well as the time until detection are compared for the four methods. Charts were constructed for the inspection of both postoperative survival and hospital stay. Results Methods using survival outcomes always yielded faster detection times compared to approaches employing binary outcomes. Detections between methods mostly coincided for postoperative survival. For hospital stay detections varied strongly, with methods based on survival outcomes signalling over half the hospitals. Further pros and cons as well as pitfalls of all methods under consideration are discussed. Conclusions Methodology for the continuous inspection of the quality of care should be tailored to the specific outcome. Properly understanding how the mechanism of a control chart functions is crucial for the correct interpretation of results. This is particularly true for CUSUM charts, which require the choice of a parameter that greatly influences the results. When applying CUSUM charts, consideration of these issues is strongly recommended.

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Section: Discussionmentioning

confidence: 99%

Inspecting the quality of care: a comparison of CUSUM methods for inter hospital performance

Gomon

Sijmons

Putter

et al. 2023

Preprint

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“…The data for the first 2 years is used as in‐control data set by following the procedure of Tighkhorshid et al 22 . as they used the same dataset in their study.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…For the model building, AFT method is used by following the Tighkhorshid et al., 22 such that

\begin{equation}{Y}_t = \log ({T}_t) = {\beta }_0 + {\beta }_1{P}_1 + \sigma {\varepsilon }_t\end{equation}

where P t represents the personnet score of the t th patient,

{\beta }_0,{\rm{ }}{\beta }_1

are the two regression coefficients, σ is standard deviation and ԑ t is the error term of survival time distribution. The values of β 0 = 5.07026, β 1 = −0.03348 and σ = 0.57 are estimated by using the in‐control data set.…”

Section: Design Of Proposed Adaptive Risk Adjusted Ewma Control Chartmentioning

confidence: 99%

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Adaptive risk adjusted exponentially weighted moving average control chart based on accelerated failure time regression

Asif

Noor‐ul‐Amin

2022

Quality & Reliability Eng

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Control charts play an effective role in monitoring and controlling the performance of health care. It helps to determine the strategy to improve the process and identify the causes of variations. In recent years, the risk adjusted memory type control charts have received reasonable attention in monitoring surgical outcomes. In this research, we used the concept of an adaptive exponentially weighted moving average (AEWMA) control chart and proposed a new adaptive risk adjusted exponentially weighted moving average (ARAEWMA) control chart by using the accelerated failure time (AFT) regression. A real data set of the cardiac surgery patients is used for analysis and the patient condition is counted by personnet score method. The proposed ARAEWMA control chart is fitted by using the AFT model and run length profiles are computed at different shifts. The proposed ARAEWMA chart is better in shift detection and showed efficient results than the risk adjusted exponentially weighted moving average control chart.

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Adaptive Risk‐Adjusted Weighted CUSUM Chart for Monitoring Surgical Performance

Li,

Liu,

Shang

et al. 2024

Quality & Reliability Eng

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Detecting changes in surgical performance early is important to improve the quality of surgery. In recent years, numerous risk‐adjusted control charts have been applied to monitor surgical performance. The conventional risk‐adjusted cumulative sum (CUSUM) charts need to be preset with the out‐of‐control odds ratio, and the performance of the control charts can be significantly affected by the actual change level. To effectively monitor changes in surgical performance, this paper proposes a novel adaptive risk‐adjusted weighted cumulative sum (RA‐WCUSUM) chart for monitoring binary surgical outcomes, where a weighting function based on standardized residuals is applied to the log‐likelihood ratio statistic. The proposed method is compared with several competing charts through simulation, and the simulation results show the superiority of the proposed method. A cardiac surgery dataset is used to demonstrate the application of the proposed method.

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A risk-adjusted EWMA chart with dynamic probability control limits for monitoring survival time

Cited by 13 publications

References 26 publications

Inspecting the quality of care: a comparison of CUSUM methods for inter hospital performance

Inspecting the quality of care: a comparison of CUSUM methods for inter hospital performance

Adaptive risk adjusted exponentially weighted moving average control chart based on accelerated failure time regression

Adaptive Risk‐Adjusted Weighted CUSUM Chart for Monitoring Surgical Performance

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