A new risk‐adjusted EWMA control chart based on survival time for monitoring surgical outcome quality

Ding, Ning; He, Zhen; Shi, Liangxing; Qu, Liang

doi:10.1002/qre.2818

Cited by 14 publications

(6 citation statements)

References 30 publications

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“…Step 2: Setting up control limits (i) A sample of size one is selected from 𝑒 𝑡 that is computed in (iii) of step 1. (ii) The statistic of the new proposed control chart is computed by using Equation (11) and evaluation of the process is done by following the design of the new proposed control chart. (iii) Recurring the last two steps of the process unless it is declared as out-of-control.…”

Section: Performance Evaluationmentioning

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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Section: Performance Evaluationmentioning

confidence: 99%

“…studied to evaluate the effect of risk adjusted EWMA and CUSUM control chart conducted by the estimation error. Ding et al 11 . persist in the use of the most accurate method for surgical outcomes.…”

Section: Introductionmentioning

confidence: 99%

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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“…To lessen the negative effects of outliers present in the historical data, Asadzadeh and Baghaei [19] presented control schemes based on the AFT model to examine a Weibulldistributed outgoing quality characteristic with reliability data in a two-stage dependent process. A new risk-adjusted EWMA control chart was developed by Ding et al [20] to track ongoing surgical outcomes assuming log-logistic distribution. Recently, Ali et al [21] proposed memory-type control charts for censored reliability data following generalized exponential distribution assuming a frailty model.…”

Section: Introductionmentioning

confidence: 99%

Robust Surveillance Schemes Based on Proportional Hazard Model for Monitoring Reliability Data

et al. 2023

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Product reliability is a crucial component of the industrial production process. Several statistical process control techniques have been successfully employed in industrial manufacturing processes to observe changes in reliability-related quality variables. These methods, however, are only applicable to single-stage processes. In reality, manufacturing processes consist of several stages, and the quality variable of the previous stages influences the quality of the present stage. This interdependence between the stages of a multistage process is an important characteristic that must be taken into account in process monitoring. In addition, sometimes datasets contain outliers and consequently, the analysis produces biased results. This study discusses the issue of monitoring reliability data with outliers. To this end, a proportional hazard model has been assumed to model the relationship between the significant quality variables of a two-stage dependent manufacturingprocess. Robust regression technique known as the M-estimation has been implemented to lessen the effect of outliers present in the dataset corresponding to reliability-related quality characteristics in the second stage of the process assuming Nadarajah and Haghighi distribution. The three monitoring approaches, namely, one lower-sided cumulative sum and two one-sided exponentially weighted moving average control charts have been designed to effectively monitor the two-stage dependent process. Using Monte Carlo simulations, the efficiency of the suggested monitoring schemes has been examined. Finally, two real-world examples of the proposed control approaches are provided in the study.

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“…Asadzadeh and Baghaei 13 discussed control schemes using AFT model to monitor a Weibull distributed output quality characteristic with reliability data in a two-stage dependent process to minimize the deleterious effects of outliers present in the historical data. Ding et al 14 designed a new riskadjusted exponentially weighted moving average (EWMA) control chart to monitor continuous surgical outcomes. For this purpose, the AFT model is implemented by assuming log-logistic distribution for the survival time of patients.…”

Section: Introductionmentioning

confidence: 99%

Proportional hazard‐based robust monitoring schemes using logistic distribution

Nabeel

Ali

Shah

2022

Quality & Reliability Eng

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In the manufacturing industry, process surveillance plays an important role in improving product reliability. Many monitoring procedures have been devised to improve the reliability of a product in the literature. Due to cascading nature of multistage processes, the quality variable of the final stage can be influenced by the quality variable in the previous stages. Furthermore, the ordinary least squares method produces biased estimates if there are outliers in the historical data. The existence of cascade property in the multistage process and the presence of influential observations (outliers) in the historical data make the analysis of control schemes more challenging. Therefore, it is essential to take into account the impact of effective covariates and outliers in the historical dataset. In this paper, a cumulative sum (CUSUM) and two exponentially weighted moving average (EWMA) charts have been developed to monitor a two-stage-dependent process. The proportional hazard (PH) model has been applied to model the relationship among the incoming and outgoing variables of the two stage process.To deal with the deleterious effects of outliers on the results, a robust regression technique known as the M-estimation has been implemented and the performance of the proposed monitoring procedures has been analyzed through Monte Carlo simulations. Lastly, two real-life applications of the proposed control schemes are presented.

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A new risk‐adjusted EWMA control chart based on survival time for monitoring surgical outcome quality

Cited by 14 publications

References 30 publications

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

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

Robust Surveillance Schemes Based on Proportional Hazard Model for Monitoring Reliability Data

Proportional hazard‐based robust monitoring schemes using logistic distribution

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