- [1] Cardoso J, Van der Aalst W. (2009). Handbook of research on business process modelling. Information Science Reference.
- [2] Hu, J., Zhao, N., Kong, R., Wang, D., Sun, B., & Wu, L. (2016). Total thyroidectomy as primary surgical management for thyroid disease: surgical therapy experience from 5559 thyroidectomies in a less-developed region. World Journal of Surgical Oncology, 14(1), 1-7.
- [3] Dionigi, G., Frattini, F. (2013). Staged thyroidectomy: Time to consider intraoperative neuromonitoring as standard of care. Thyroid, 23(7), 906-908.
- [4] Rosen, K., Reid, R., Broemeling, A., & Rakovsky, C. (2003). Applying a risk-adjusted framework to primary care: Can we improve on existing measures? Annals of Family Medicine, 1(1), 44-55.
- [5] Juhnke, C., Bethge, S., & Muhlbacher, A. (2016). A review on methods of risk adjustment and their use in integrated healthcare systems. International Journal of Integrated Care, 16(4), 1-18.
- [6] Knaus, W.A., Zimmerman, J.E., Wagner, D.P., Draper, E.A., & Lawrence, D.E. (1981). APACHE-acute physiology and chronic health evaluation: a physiologically based classification system. Critical Care Medicine, 9(8), 591-597.
- [7] Knaus,A., Draper, E.A., Wagner, D.P., & Zimmerman, J.E. (1985). APACHE II: a severity of disease classification system. Critical Care Medicine, 13(10), 818-829.
- [8] Zimmerman, J.E., Kramer, A.A., McNair, D.S., & Malila, F.M. (2006). Acute physiology and chronic health evaluation (APACHE) IV: hospital mortality assessment for today's critically ill patients. Critical Care Medicine, 34(5), 1297-1310.
- [9] Or, Z., Renaud, T., & Thuilliez, J. (2012). Diagnosis related groups and variations in resource use for child delivery across 10 European countries. Health Economy, 21, 55-65.
- [10] Paat-Ahi, G., Swiderek, M., & Sakowski, P. (2012). DRGs in Europe: a cross country analysis for cholecystectomy. Health Economy, 21, 66-76.
- [11] De Cassai, A., Boscolo, A., Tonetti, T., Ban, I., & Ori, C. (2019). Assignment of ASA-physical status relates to anesthesiologists' experience: a survey-based national-study. Korean Journal of Anesthesiology, 72(1), 53-59.
- [12] Howard, R., Yin, S., McCandless, L., Wang, S., Englesbe, M., & Machado-Aranda, D. (2019). Taking Control of Your Surgery: Impact of a Prehabilitation Program on Major Abdominal Surgery. Journal of the American College of Surgeons, 228(1), 72-80.
- [13] Knuf, M., Maani, V., & Cummings, K. (2018). Clinical agreement in the American Society of Anesthesiologists physical status classification. Perioperative Medicine, 7, 7-14.
- [14] Zeng, L. (2016). Risk-adjusted performance monitoring in healthcare quality control, In Quality and Reliability Management and its Applications, Pham H (ed). Springer, London.
- [15] Verbeke, G., Fieuws, S., Molenberghs, G., & Davidian, M. (2015). The analysis of multivariate longitudinal data: A review. Statistical Methods in Medical Research, 23(1), 42-59.
- [16] Koetsier, A., De Keizer, N.F., De Jong, E., Cook, D.A., & Peek N. (2012). Performance of risk-adjusted control charts to monitor in-hospital mortality of intensive care unit patients: A simulation study. Critical Care Medicine, 40(6), 1799-1807.
- [17] Lie, R.T., Heuch, I., & Irgens, L.M. (1993). A New Sequential Procedure for Surveillance of Down’s Syndrome. Statistics in Medicine, 12, 13-25.
- [18] Alemi, F., Olivier, D. (2001). Tutorial on Risk-adjusted Quality Management in Healthcare, 10, 1-9.
- [19] Cook, D.A., Steiner, S.H., Cook, R.J., Farewell, V.T., & Morton, A.P. (2003). Monitoring the Evolutionary Process of Quality: Risk-adjusted Charting to Track Outcomes in Intensive Cares. Critical Care Medicine, 31(6), 1676-1682.
- [20] Sego, L.H., Woodall, W.H., & Reynolds, M.R Jr. (2008). A Comparison of Methods for Small Incidence Rate. Statistics in Medicine, 27(8), 1225-1247.
- [21] Grigg, O., Spiegelhalter, D.J. (2007). Simple Risk-adjusted Exponentially Weighted Moving average. Journal of the American Statistical Association, 102, 140-152.
- [22] Steiner, S.H., Jones, M. (2010). Risk-adjusted survival Time Monitoring with an Updating Exponentially Weighted Moving Average (EWMA) Control Chart. Statistics in Medicine, 29, 444-454.
- [23] Szarka, J.L., Woodall, W. (2011). A Review and Perspective on Surveillance of Bernoulli Processes. Quality and Reliability Engineering International, 27, 735-752.
- [24] Paynabar, K., Jin, J. (2012). Phase I Risk-adjusted Control Charts for Monitoring Surgical Performance by Considering Categorical Covariates. Journal of Quality Technology, 44(1), 39-53.
- [25] Shojaei, S.N., Niaki, T.A. (2013). A Risk-adjusted Multi-Attribute Cumulative Sum Control Scheme in Healthcare System. The 2013 IEEE International Conference of Industrial Engineering and Engineering Management (IEEM). Singapore.
- [26] Tian, W., Sun, H., Zhang, X., & Woodall, W. (2014). The Impact of Varying Patient Populations on the In-control Performance of the Risk-adjusted CUSUM Chart. International Journal of Quality in Healthcare, 27(1), 31-36.
- [27] Zhang, X., Woodall, W. (2015). Dynamic Probability Control Limits for Risk-adjusted Bernoulli CUSUM Charts. Statistics in Medicine, 34(25), 3336-3348.
- [28] Zhang, X., Woodall, W. (2016). Reduction of the Effect of Estimation Error on In-Control Performance for Risk-adjusted Bernoulli CUSUM Chart with Dynamic Probability Control Limits. Quality and Reliability Engineering International, 33(2), 381-386.
- [29] Zhang, X., Woodall, W.H. (2016). Dynamic Probability Control Limits for Lower and Two-Sided Risk-Adjusted Bernoulli CUSUM Charts. Quality and Reliability Engineering International, 33(3), 607-616.
- [30] Sparks, R. (2016). Linking EWMA p Charts and the Risk-Adjusted Control Charts. Quality and Reliability Engineering International, 33(3), 617-636.
- [31] Sachlas, A., Bersimis, S., & Psarakis, S. (2019). Risk-adjusted control charts: theory, methods, and applications in health. Statistics in Bioscience, 11(1), 1-29.
- [32] Begun, A., Kulinsakaya, E., & MacGregor, A.J. (2019). Risk-adjusted CUSUM control charts for shared frailty survival models with application to hip replacement outcomes: a study using the NJR dataset. BMC Medical Research Methodology, 19, 217.
- [33] Roy, A., Cutright, D., Gopalakrishnan, M., Yeh, A.B., & Mittal, B.B. (2020). A risk-adjusted control chart to evaluate intensity modulated radiation therapy plan quality. Advances in Radiation Oncology, 5(5), 1032-1041.
- [34] Ali, S., Altaf, N., Shah, I., Wang, L., & Raza, S.M.M. (2020). On the effect of estimation error for the risk-adjusted charts. Complexity, ID: 6258010.
- [35] Ding, N., He, , Shi, L., & Qu, L. (2020). A new risk‐adjusted EWMA control chart based on survival time for monitoring surgical outcome quality. Quality and Reliability Engineering International, Published Online.
- [36] Rafiei, N., Asadzadeh, S. (2020). Designing a risk-adjusted CUSUM control chart based on DEA and NSGA-II approaches (a case study in healthcare: Cardiovascular patients). Scientia Iranica, In Press.
- [37] Keshavarz, M., Asadzadeh, S., & Niaki, S.T.A. (2021). Risk-adjusted frailty-based CUSUM control chart for phase I monitoring of patients’ lifetime. Journal of Statistical Computation and Simulation, 91(2), 334-352.
- [38] Keshavarz, M., Asadzadeh, S. (2021). Phase II monitoring of survival times with categorical covariates. Quality and Reliability Engineering International, 37(2), 451-463.
- [39] Kazemi, S., Noorossana, R., Rasouli, M., Nayebpour, M., & Heidari, K. (2021). Monitoring therapeutic processes using risk-adjusted multivariate Tukey’s CUSUM control chart. Quality and Reliability Engineering International, In Press.
- [40] Grigg, O., Farewell, V. (2004). An Overview of Risk-Adjusted Charts. Journal of the Royal Statistical Society, 167(3), 523-539.
- [41] Woodall, W. (2006). The Use of Control Charts in Healthcare and Public Health Surveillance. Journal of Quality Technology, 38(2), 89-104.
- [42] Cook, D.A., Duke, G., Hart, G.K., Pilcher, D., & Mullany, D. (2008). Review of the Application of Risk-adjusted Charts to analyze Mortality Outcomes in Critical Care. Critical Care Resuscitation, 10(3), 239-251.
- [43] Woodall, W.H., Fogel, S.L., & Steiner, S.H. (2015). The Monitoring and Improvement of Surgical-Outcome Quality. Journal of Quality Technology, 47(4), 383-399.
- [44] Kalaei, M., Atashgar, K., Niaki, S.T.A., & Soleimani, P. (2018). Phase I monitoring of simple linear profiles in multistage process with cascade property. International Journal of Advanced Manufacturing and Technology, 94, 1745-1757.
- [45] Funatogawa, I., Funatogawa, T., & Ohashi, Y. (2007). An autoregressive linear mixed effects model for the analysis of longitudinal data which show profiles approaching asymptotes. Statistics in Medicine, 26, 2113-2130.
- [46] Funatogawa, I., Funatogawa, T., & Ohashi, Y. (2008). A bivariate autoregressive linear mixed effects model for the analysis of longitudinal data. Statistics in Medicine, 26, 6367-6378.
- [47] Funatogawa, I., Funatogawa, T., & Takeuhi, M. (2008). An autoregressive linear mixed effects model for the analysis of longitudinal data which include dropouts and show profiles asymptotes. Statistics in Medicine, 27, 6351-6366.
- [48] Funatogawa, I., Funatogawa, T. (2011). An aoutregressive linear mixed effect model for the analysis of unequally spaced longitudinal data with dose modification. Statistics in Medicine, 31, 589-599.
- [49] Funatogawa, I., Funatogawa, T. (2012). Dose-response relationship from longitudinal data with response-dependent dose modification using likelihood methods. Biometrics Journal, 54(4), 1-13.
- [50] Sibanda, N. (2014). Graphical model based O/E control Chart for Monitoring Multiple Outcomes from a Multistage Healthcare Process. Statistical Methods in Medicine Research, 0(0), 1-20.
- [51] Rastgoomoghadam, A., Samimi, Y., & Nasiri, S. (2016). A method for monitoring the quality characteristic for two-stage thyroid cancer surgery using risk-adjusted model. Journal of Quality Engineering and Management, 6, 92-102, In Persian.
- [52] Kazemian, P., Lavieri, M.S., Van Oyen, M.P., Andrews, C.N., & Stein, J. (2017). Personalized prediction of glaucoma progression under different target intraocular pressure levels using filtered forecasting methods. Ophtalmology, 125(4), 569-577.
- [53] Sogandi, F., Aminnayeri, M., Mohammadpour, A., Amiri, A. (2019). Risk-adjusted Bernoulli chart in multi-stage healthcare processes based on state-space model with a latent risk variable and dynamic probability control limits. Computer and Industrial Engineering, 130, 699-713.
- [54] Shi, J., Zhou, S. (2009). Quality control and improvement multistage systems: A survey. IIE Transactions, 41(9), 744-753.
- [55] The Royal College of Pathologists of Australasia (RCPA). (2020). Thyroid cancer structured reporting protocol, Second Edition.
- [56] The British Association of Endocrine and Thyroid Surgeons. (2017). Fifth national audit report, prepared by Chadwick, D., Kinsman, R., Walton, P.
- [57] Commandeur, J., Koopman, S. (2007). An introduction to state space time series analysis. New York, Oxford University Press Inc.
- [58] Kung, Y. (1978). A new identification and model reduction algorithm via singular value decompositions. 12th Asilomar Conference on Circuits, Systems and Computers, November 6-8.
- [59] Danilov, D., Zhigljavsky, A. (1997). Principle component of time series: the ‘caterpillar’ method. St. Petersburg, Russia, University of St. Petersburg.
- [60] Al-Saggaf, M., Franklin, F. (1987). An error bound for a discrete reduced order model of a linear multivariable system. IEEE Transaction, 32, 815-819.
- [61] Ljung, L. (1999). System identification: theory for the users. Second Edition. New York, Prentice Hall PTR.
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