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A risk adjusted self-starting Bernoulli CUSUM control chart with dynamic probability control limits | ||
| AUT Journal of Modeling and Simulation | ||
| مقاله 9، دوره 48، شماره 2، بهمن 2016، صفحه 103-110 اصل مقاله (626.51 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/miscj.2016.833 | ||
| نویسندگان | ||
| Majid Aminnayeri* 1؛ Fatemeh Sogandi2 | ||
| 1Associate Professor, Department of Industrial Engineering, Amirkabir University of Technology | ||
| 2Ph.D. Student, Department of Industrial Engineering, Amirkabir University of Technology | ||
| چکیده | ||
| Usually, in monitoring schemes the nominal value of the process parameter is assumed known. However, this assumption is violated owing to costly sampling and lack of data particularly in healthcare systems. On the other hand, applying a fixed control limit for the risk-adjusted Bernoulli chart causes to a variable in-control average run length performance for patient populations with dissimilar risk score distributions in monitoring clinical and surgical performance. To solve these problems, a self-starting scheme is proposed based on a parametric bootstrap method and dynamic probability control limits for the risk-adjusted Bernoulli cumulative sum control charts. The advantage of the proposed control charts lies in the use of probability control limits when any assumptions about the patients’ risk distributions and process parameter. Simulation studies show that both proposed schemes have good performance under various shifts. | ||
| کلیدواژه‌ها | ||
| Average Run Length؛ Self-Starting Monitoring؛ Bernoulli Process؛ Probability Control Limits؛ Surgical Performance | ||
| عنوان مقاله [English] | ||
| A risk adjusted self-starting Bernoulli CUSUM control chart with dynamic probability control limits | ||
| نویسندگان [English] | ||
| Majid Aminnayeri1؛ Fatemeh Sogandi2 | ||
| 1Amirkabir University of Technology | ||
| 2Amirkabir University of Technology | ||
| چکیده [English] | ||
| Usually, in monitoring schemes the nominal value of the process parameter is assumed known. However, this assumption is violated owing to costly sampling and lack of data particularly in healthcare systems. On the other hand, applying a fixed control limit for the risk-adjusted Bernoulli chart causes to a variable in-control average run length performance for patient populations with dissimilar risk score distributions in monitoring clinical and surgical performance. To solve these problems, a self-starting scheme is proposed based on a parametric bootstrap method and dynamic probability control limits for the risk-adjusted Bernoulli cumulative sum control charts. The advantage of the proposed control charts lies in the use of probability control limits when any assumptions about the patients’ risk distributions and process parameter. Simulation studies show that both proposed schemes have good performance under various shifts. | ||
| کلیدواژه‌ها [English] | ||
| Average Run Length, Self-Starting Monitoring, Bernoulli Process, Probability Control Limits, Surgical Performance | ||
| مراجع | ||
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[17] Zeng, L.; “Risk-Adjusted Performance Monitoring in Healthcare Quality Control,” Quality and Reliability Management and Its Applications, Springer, London, pp. 27-45, 2016.
[18] Tang, X.; Gan, F. F. and Zhang, L.; “Risk- Adjusted Cumulative Sum Charting Procedure Based on Multiresponses,” Journal of the American Statistical Association, Vol. 110, No. 509, pp. 16-26, 2016.
[19] Cook, D. A.; Coory, M. and Webster, R. A.; “Exponentially Weighted Moving Average Charts to Compare Observed and Expected Values for Monitoring Risk-Adjusted Hospital Indicators,” BMJ Quality and Safety, Vol. 20, No. 6, pp. 469–474, 2011.
[20] Fang, Y. Y.; Khoo, M. B.; Teh, S. Y. and Xie, M.; “Monitoring of Time between Events with a Generalized Group Runs Control Chart,” Quality and Reliability Engineering International, 2015.
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[22] Zhang, X. and Woodall, W. H.; “Dynamic Probability Control Limits for Risk-Adjusted Bernoulli CUSUM Charts,” Statistics in Medicine, Vol. 34, No. 25, pp. 3336-3348, 2014.
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[24] Paynabar, K.; Jin, J. and Yeh, A.; “Phase I Risk Adjusted Control Charts for Monitoring Surgical Performance with Considering Categorical Covariates,” Journal of Quality Technology, Vol. 44, No. 1, pp. 39-53, 2012.
[25] Asadayyoobi, N. and Niaki, S. T. A.; “Monitoring Patient Survival Times in Surgical Systems Using a Risk-Adjusted AFT Regression,” Quality Technology and Quantitative Management Chart, 2015 [Accepted].
[26] Mohammadian, F.; Niaki, S. T. A. and Amiri, A.; “Phase I Risk-Adjusted Geometric Control Charts to Monitor Health Care Systems,” Quality and Reliability Engineering International, 2014.
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