Choosing where to set the threshold between low- and high-risk patients: Evaluating a classification tool within a simulation

References

• Agresti, A. (2013). Categorical data analysis (3rd ed.) John Wiley & Sons.
   Google Scholar
• Ayer, T., Chhatwal, J., Alagoz, O., Kahn, C. E., Woods, R. W., & Burnside, E. S. (2010). Comparison of logistic regression and artificial neural network models in breast cancer risk estimation. Radiographics, 30(1), 13–22.
   PubMed Web of Science ®Google Scholar
• Banks, J., Carson, J. S., II, Nelson, B. L., & Nicol, D. M. (2010). Introduction to simulation. In W. J. Fabrycky & J. H. Mize (Eds.), Discrete-event system simulation (5th ed., pp. 1–22). Pearson Prentice Hall.
   Google Scholar
• Bayer, S., Petsoulas, C., Cox, B., Honeyman, A., & Barlow, J. (2010). Facilitating stroke care planning through simulation modelling. Health Informatics Journal, 16(2), 129–143. https://doi.org/10.1177/1460458209361142
   PubMed Web of Science ®Google Scholar
• Bhattacharjee, P., & Ray, P. K. (2016). Simulation modelling and analysis of appointment system performance for multiple classes of patients in a hospital: A case study. Operations Research for Health Care, 8, 71–84. https://doi.org/10.1016/j.orhc.2015.07.005
   Web of Science ®Google Scholar
• Burr, J. M., Botello-Pinzon, P., Takwoingi, Y., Hernández, R., Vazquez-Montes, M., Elders, A., Asaoka, R., Banister, K., van der Schoot, J., Fraser, C., King, A., Lemij, H., Sanders, R., Vernon, S., Tuulonen, A., Kotecha, A., Glasziou, P., Garway-Heath, D., Crabb, D., … Cook, J. (2012). Surveillance for ocular hypertension: An evidence synthesis and economic evaluation. Health Technology Assessment, 16(29), 1–271. https://doi.org/10.3310/hta16290
   Web of Science ®Google Scholar
• Cancer Research UK. (2011). People fear cancer more than other serious illness.
   Google Scholar
• Cancer Research UK. (2016a). Breast cancer diagnosis and treatment statistics. Retrieved October 11, 2016, from http://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/breast-cancer/diagnosis-and-treatment#heading-Zero
   Google Scholar
• Cancer Research UK. (2016b). Breast cancer statistics. Retrieved October 11, 2016, from http://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/breast-cancer#heading-Zero
   Google Scholar
• Cancer Research UK. (2018). Breast cancer survival by stage at diagnosis.
   Google Scholar
• Cannon, J. W., Mueller, U. A., Hornbuckle, J., Larson, A., Simmer, K., Newnham, J. P., & Doherty, D. A. (2013). Economic implications of poor access to antenatal care in rural and remote Western Australian Aboriginal communities: An individual sampling model of pregnancy. European Journal of Operational Research, 226(2), 313–324. https://doi.org/10.1016/j.ejor.2012.10.041
   Web of Science ®Google Scholar
• Ceglowski, R., Churilov, L., & Wasserthiel, J. (2007). Combining data mining and discrete event simulation for a value-added view of a hospital emergency department. Journal of the Operational Research Society, 58(2), 246–254. https://doi.org/10.1057/palgrave.jors.2602270
   Web of Science ®Google Scholar
• Chemweno, P., Thijs, V., Pintelon, L., & van Horenbeek, A. (2014). Discrete event simulation case study: Diagnostic path for stroke patients in a stroke unit. Simulation Modelling Practice and Theory, 48, 45–57. https://doi.org/10.1016/j.simpat.2014.07.006
   Web of Science ®Google Scholar
• Cooper, K., Davies, R., Roderick, P., Chase, D., & Raftery, J. (2002). The development of a simulation model of the treatment of coronary heart disease. Health Care Management Science, 5(4), 259–267. https://doi.org/10.1023/a:1020378022303
   PubMedGoogle Scholar
• Crane, G. J., Kymes, S. M., Hiller, J. E., Casson, R., & Karnon, J. D. (2013). Development and calibration of a constrained resource health outcomes simulation model of hospital-based glaucoma services. Health Systems, 2(3), 181–197. https://doi.org/10.1057/hs.2013.5
   Google Scholar
• Crawford, E. A., Parikh, P. J., Kong, N., & Thakar, C. V. (2014). Analyzing discharge strategies during acute care: A discrete-event simulation study. Medical Decision Making, 34(2), 231–241. https://doi.org/10.1177/0272989X13503500
   PubMed Web of Science ®Google Scholar
• Eatock, J., Clarke, M., Picton, C., & Young, T. (2011). Meeting the four-hour deadline in an A&E department. Journal of Health Organization and Management, 25(6), 606–624. https://doi.org/10.1108/14777261111178510
   PubMedGoogle Scholar
• Gillespie, J., McClean, S., Garg, L., Barton, M., Scotney, B., & Fullerton, K. (2016). A multi-phase DES modelling framework for patient-centred care. Journal of the Operational Research Society, 67(10), 1239–1249. https://doi.org/10.1057/jors.2016.8
   Web of Science ®Google Scholar
• Hanna, T. P., Aggarwal, A., Booth, C. M., & Sullivan, R. (2020). Counting the invisible costs of covid-19: The cancer pandemic. The BMJ Opinion.
   Google Scholar
• Harper, P. R. (2002). A framework for operational modelling of hospital resources. Health Care Management Science, 5(3), 165–173. https://doi.org/10.1023/A:1019767900627
   PubMedGoogle Scholar
• Harper, P. R., Sayyad, M. G., De Senna, V., Shahani, A. K., Yajnik, C. S., & Shelgikar, K. M. (2003). A systems modelling approach for the prevention and treatment of diabetic retinopathy. European Journal of Operational Research, 150(1), 81–91. https://doi.org/10.1016/S0377-2217(02)00787-7
   Web of Science ®Google Scholar
• Harvey, J., Down, S., Bright-Thomas, R., Winstanley, J., & Bishop, H. (2014). Breast disease management: A multidisciplinary manual. Oxford University Press.
   Google Scholar
• Huang, Y.-L., & Hanauer, D. A. (2016). Time dependent patient no-show predictive modelling development. International Journal of Health Care Quality Assurance, 29(4), 475–488. https://doi.org/10.1108/09526860710819440
   PubMedGoogle Scholar
• Isken, M. W., & Rajagopalan, B. (2002). Data mining to support simulation modeling of patient flow in hospitals. Journal of Medical Systems, 26(2), 179–197.
   PubMed Web of Science ®Google Scholar
• Keogh, B. (2009). Operational standards for the cancer waiting times commitments. https://webarchive.nationalarchives.gov.uk/ukgwa/20111116004920/http://www.sph.nhs.uk/ebc/sph-qarc/sph-files/cervical-screening-files/operational-standards-for-the-cancer-waiting-times-cimmitments/at_download/file
   Google Scholar
• Khanna, S., Sier, D., Boyle, J., & Zeitz, K. (2016). Discharge timeliness and its impact on hospital crowding and emergency department flow performance. Emergency Medicine Australasia, 28(2), 164–170. https://doi.org/10.1111/1742-6723.12543
   PubMed Web of Science ®Google Scholar
• Lord, J., Willis, S., Eatock, J., Tappenden, P., Trapero-Bertran, M., Miners, A., Crossan, C., Westby, M., Anagnostou, A., Taylor, S., Mavranezouli, I., Wonderling, D., Alderson, P., & Ruiz, F. (2013). Economic modelling of diagnostic and treatment pathways in National Institute for Health and Care Excellence clinical guidelines: The Modelling Algorithm Pathways in Guidelines (MAPGuide) project. Health Technology Assessment, 17(58), 1–150. https://doi.org/10.3310/hta17580
   Web of Science ®Google Scholar
• Mangasarian, O. L., Street, W. N., & Wolberg, H. (1995). Breast cancer diagnosis and prognosis via linear programming. Operations Research, 43(4), 570–577. https://doi.org/10.1287/opre.43.4.570
   Web of Science ®Google Scholar
• McCowan, C., Donnan, P. T., Dewar, J., Thompson, A., & Fahey, T. (2011). Identifying suspected breast cancer: Development and validation of a clinical prediction rule. The British Journal of General Practice, 61(586), e205–e214. https://doi.org/10.3399/bjgp11X572661
   PubMed Web of Science ®Google Scholar
• Monks, T., Currie, C. S. M., Onggo, B. S., Robinson, S., Kunc, M., & Taylor, S. J. E. (2019). Strengthening the reporting of empirical simulation studies: Introducing the STRESS guidelines. Journal of Simulation, 13(1), 55–67. https://doi.org/10.1080/17477778.2018.1442155
   Web of Science ®Google Scholar
• Monks, T., Worthington, D., Allen, M., Pitt, M., Stein, K., & James, M. A. (2016). A modelling tool for capacity planning in acute and community stroke services. BMC Health Services Research, 16(1), 1–8. https://doi.org/10.1186/s12913-016-1789-4
   PubMedGoogle Scholar
• Pazzani, M., Merz, C., Murphy, P., Ali, K., Hume, T., & Brunk, C. (1994). Reducing misclassification costs [Paper presentation]. In Proceedings of the Eleventh International Conference on Machine Learning (pp. 217–225).
   Google Scholar
• Pendharkar, P. C., Rodger, J. A., Yaverbaum, G. J., Herman, N., & Benner, M. (1999). Association, statistical, mathematical and neural approaches for mining breast cancer patterns. Expert Systems with Applications, 17(3), 223–232. https://doi.org/10.1016/S0957-4174(99)00036-6
   Web of Science ®Google Scholar
• Pilgrim, H., Tappenden, P., Chilcott, J., Bending, M., Trueman, P., Shorthouse, A., & Tappenden, J. (2009). The costs and benefits of bowel cancer service developments using discrete event simulation. Journal of the Operational Research Society, 60(10), 1305–1314. https://doi.org/10.1057/jors.2008.109
   Web of Science ®Google Scholar
• Revankar, N., Ward, A. J., Pelligra, C. G., Kongnakorn, T., Fan, W., & LaPensee, K. T. (2014). Modeling economic implications of alternative treatment strategies for acute bacterial skin and skin structure infections. Journal of Medical Economics, 17(10), 730–740. https://doi.org/10.3111/13696998.2014.941065
   PubMedGoogle Scholar
• Santibáñez, P., Chow, V. S., French, J., Puterman, M. L., & Tyldesley, S. (2009). Reducing patient wait times and improving resource utilization at British Columbia Cancer Agency’s ambulatory care unit through simulation. Health Care Management Science, 12(4), 392–407. https://doi.org/10.1007/s10729-009-9103-1
   PubMed Web of Science ®Google Scholar
• SAS. (2013). Developing credit scorecards using credit scoring for SAS® Enterprise MinerTM 13.1. https://support.sas.com/documentation/cdl/en/emcsgs/66024/HTML/default/viewer.htm#bookinfo.htm
   Google Scholar
• Saville, C., Smith, H., & Bijak, K. (2019). Operational research techniques applied throughout cancer care services: A review. Health Systems (Basingstoke, England), 8(1), 52–73. https://doi.org/10.1080/20476965.2017.1414741
   PubMedGoogle Scholar
• Sullivan, L. M., Massaro, J. M., & D’Agostino, R. B. (2004). Presentation of multivariate data for clinical use: The Framingham Study risk score functions. Statistics in Medicine, 23(10), 1631–1660. https://doi.org/10.1002/sim.1742
   PubMed Web of Science ®Google Scholar
• Thomas, L. C. (2009). Using logistic regression to build scorecards. In Consumer credit models: Pricing, profit and portfolios (1st ed., pp. 79–84). Oxford University Press.
   Google Scholar
• Tran-Duy, A., Boonen, A., Kievit, W., van Riel, P. L. C. M., van de Laar, M. A. F. J., & Severens, J. L. (2014). Modelling outcomes of complex treatment strategies following a clinical guideline for treatment decisions in patients with rheumatoid arthritis. PharmacoEconomics, 32(10), 1015–1028. https://doi.org/10.1007/s40273-014-0184-4
   PubMed Web of Science ®Google Scholar
• Vataire, A.-L., Aballéa, S., Antonanzas, F., Roijen, L. H-v., Lam, R. W., McCrone, P., Persson, U., & Toumi, M. (2014). Core discrete event simulation model for the evaluation of health care technologies in major depressive disorder. Value in Health, 17(2), 183–195. https://doi.org/10.1016/j.jval.2013.11.012
   PubMed Web of Science ®Google Scholar
• Wang, H.-I., Smith, A., Aas, E., Roman, E., Crouch, S., Burton, C., & Patmore, R. (2017). Treatment cost and life expectancy of diffuse large B-cell lymphoma (DLBCL): A discrete event simulation model on a UK population-based observational cohort. The European Journal of Health Economics, 18(2), 255–267. https://doi.org/10.1007/s10198-016-0775-4
   PubMed Web of Science ®Google Scholar
• Whittington Health NHS. (2019a). About us. Retrieved August 9, 2019, from http://www.whittington.nhs.uk/default.asp?c=3920
   Google Scholar
• Whittington Health NHS. (2019b). Breast cancer. Retrieved August 9, 2019, from https://www.whittington.nhs.uk/default.asp?c=27104
   Google Scholar
• Willett, A. M., Michell, M. J., & Lee, M. J. R. (2010). Best practice diagnostic guidelines for patients presenting with breast symptoms. Department of Health, London. https://associationofbreastsurgery.org.uk/media/1416/best-practice-diagnostic-guidelines-for-patients-presenting-with-breast-symptoms.pdf
   Google Scholar
• Zhao, H. (2008). Instance weighting versus threshold adjusting for cost-sensitive classification. Knowledge and Information Systems, 15(3), 321–334. https://doi.org/10.1007/s10115-007-0079-1
   Web of Science ®Google Scholar