Boyle EM, Walsh SR. ‘Predictors of length of stay in patients having elective colorectal surgery within an enhanced recovery protocol’ Int J Surg 2010; 8: 628–32. Commentary on: Int J Surg. 2011;9:355–6.
Victorian Auditor-General's and Office. Hospital Performance: Length of Stay [Report]. Mlebourne (AUST): State Government of Victoria; 2016.
Cologne KG, Byers S, Rosen DR, et al. Factors Associated with a Short (<2 Days) or Long (>10 Days) Length of Stay after Colectomy: A Multivariate Analysis of over 400 Patients. Am Surg. 2016;82:960–3.
Jakobsen DH, Sonne E, Andreasen J, Kehlet H. Convalescence after colonic surgery with fast-track vs conventional care. Colorectal Dis. 2006;8:683–7.
Kehlet H. Fast-track colorectal surgery. Lancet. 2008;371:791–3.
Richardson B, Preskitt J, Lichliter W, et al. The effect of multidisciplinary teams for rectal cancer on delivery of care and patient outcome: Has the use of multidisciplinary teams for rectal cancer affected the utilization of available resources, proportion of patients meeting the standard of care, and does this translate into changes in patient outcome? Am J Surg. 2016;211:46–52.
Gatt M, Anderson AD, Reddy BS, Hayward-Sampson P, Tring IC, MacFie J. Randomized clinical trial of multimodal optimization of surgical care in patients undergoing major colonic resection. Br J Surg. 2005;92:1354–62.
Aravani A, Samy EF, Thomas JD, Quirke P, Morris EJ, Finan PJ. A retrospective observational study of length of stay in hospital after colorectal cancer surgery in England (1998–2010). Medicine (Baltimore). 2016;95:e5064.
Adams DF, Fraser DB, Abrams HL. The complications of coronary arteriography. Circulation. 1973;48:609–18.
Luft HS, Bunker JP, Enthoven AC. Should operations be regionalized? The empirical relation between surgical volume and mortality. N Engl J Med. 1979;301:1364–9.
Ahmed Ali U, Dunne T, Gurland B, Vogel JD, Kiran RP. Actual versus estimated length of stay after colorectal surgery: Which factors influence a deviation? Am J Surg. 2014;208:663–9.
Liu CJ, Chou YJ, Teng CJ, et al. Association of surgeon volume and hospital volume with the outcome of patients receiving definitive surgery for colorectal cancer: A nationwide population-based study. Cancer. 2015;121:2782–90.
Thompson BS, Coory MD, Gordon LG, Lumley JW. Cost savings for elective laparoscopic resection compared with open resection for colorectal cancer in a region of high uptake. Surg Endosc. 2014;28:1515–21.
Yasunaga H, Matsuyama Y, Ohe K. Effects of hospital and surgeon volumes on operating times, postoperative complications, and length of stay following laparoscopic colectomy. Surg Today. 2009;39:955–61.
Shida D, Tagawa K, Inada K, et al. Enhanced recovery after surgery (ERAS) protocols for colorectal cancer in Japan. BMC Surg. 2015;15:90.
Nygren J, Soop M, Thorell A, Hausel J, Ljungqvist O. An enhanced-recovery protocol improves outcome after colorectal resection already during the first year: A single-center experience in 168 consecutive patients. Dis Colon Rectum. 2009;52:978–85.
Boudourakis LD, Wang TS, Roman SA, Desai R, Sosa JA. Evolution of the surgeon-volume, patient-outcome relationship. Ann Surg. 2009;250:159–65.
Kelly M, Sharp L, Dwane F, Kelleher T, Comber H. Factors predicting hospital length-of-stay and readmission after colorectal resection: A population-based study of elective and emergency admissions. BMC Health Serv Res. 2012;12:77.
Burns EM, Bottle A, Almoudaris AM, et al. Hierarchical multilevel analysis of increased caseload volume and postoperative outcome after elective colorectal surgery. Br J Surg. 2013;100:1531–8.
Zheng Z, Hanna N, Onukwugha E, Bikov KA, Mullins CD. Hospital center effect for laparoscopic colectomy among elderly stage I-III colon cancer patients. Ann Surg. 2014;259:924–9.
Faiz O, Haji A, Burns E, Bottle A, Kennedy R, Aylin P. Hospital stay amongst patients undergoing major elective colorectal surgery: Predicting prolonged stay and readmissions in NHS hospitals. Colorectal Dis. 2011;13:816–22.
Harmon JW, Tang DG, Gordon TA, et al. Hospital volume can serve as a surrogate for surgeon volume for achieving excellent outcomes in colorectal resection. Ann Surg. 1999;230:404–11; discussion 11–3.
Patel SS, Patel MS, Mahanti S, et al. Laparoscopic versus open colon resections in California: A cross-sectional analysis. Am Surg. 2012;78:1063–5.
Burns EM, Mamidanna R, Currie A, et al. The role of caseload in determining outcome following laparoscopic colorectal cancer resection: An observational study. Surg Endosc. 2014;28:134–42.
Keller DS, Hashemi L, Lu M, Delaney CP. Short-term outcomes for robotic colorectal surgery by provider volume. J Am Coll Surg. 2013;217:1063–9. e1.
Rea JD, Lu KC, Diggs BS, Cone MM, Hardiman KM, Herzig DO. Specialized practice reduces inpatient mortality, length of stay, and cost in the care of colorectal patients. Dis Colon Rectum. 2011;54:780–6.
Damle RN, Macomber CW, Flahive JM, et al. Surgeon volume and elective resection for colon cancer: An analysis of outcomes and use of laparoscopy. J Am Coll Surg. 2014;218:1223–30.
Larson DW, Marcello PW, Larach SW, et al. Surgeon volume does not predict outcomes in the setting of technical credentialing: Results from a randomized trial in colon cancer. Ann Surg. 2008;248:746–50.
Chowdhury MM, Dagash H, Pierro A. A systematic review of the impact of volume of surgery and specialization on patient outcome. Br J Surg. 2007;94:145–61.
Huebner M, Hubner M, Cima RR, Larson DW. Timing of complications and length of stay after rectal cancer surgery. J Am Coll Surg. 2014;218:914–9.
Borowski DW, Bradburn DM, Mills SJ, et al. Volume-outcome analysis of colorectal cancer-related outcomes. Br J Surg. 2010;97:1416–30.
Austin PC, Rothwell DM, Tu JV. A comparison of statistical modeling strategies for analyzing length of stay after CABG surgery. Health Serv Outcomes Res Methodol. 2002;3:107–33.
Gruen RL, Pitt V, Green S, Parkhill A, Campbell D, Jolley D. The effect of provider case volume on cancer mortality: Systematic review and meta-analysis. CA Cancer J Clin. 2009;59:192–211.
Fenske N, Fahrmeir L, Hothorn T, Rzehak P, Hohle M. Boosting structured additive quantile regression for longitudinal childhood obesity data. Int J Biostat. 2013;9(1). DOI: 10.1515/ijb-2012–0035.
Borghi E, de Onis M, Garza C, et al. Construction of the World Health Organization child growth standards: Selection of methods for attained growth curves. Stat Med. 2006;25:247–65.
Wang X, Dey DK. Generalized extreme value regression for binary response data: An application to B2B electronic payments system adoption. Ann Appl Stat. 2010;4:2000–23.
Archampong D, Borowski D, Willejrgensen P, Iversen LH. Workload and surgeons specialty for outcome after colorectal cancer surgery. Cochrane Database Syst. 2012;3(3):CD005391.
Arlene SA, Stephen EF, Thomas AL, Sharon-lise TN, Thérèse AS, Utts J. Statistical Issues In Assessing Hospital Performance. Commissioned by the Committee of Presidents of Statistical Societies. Baltimore (MD): U.S. Centers for Medicare and Medicaid Services; 2011.
Bell A, Fairbrother M, Jones K. Fixed and Random effects models: Making an informed choice. Qual Quant. 2019;53(2):1051–74.
Feaster D, Brincks A, Robbins M, Szapocznik J. Multilevel models to identify contextual effects on individual group member outcomes: A family example [report]. Fam Process. 2011;50:167.
Dieleman JL, Templin T. Random-effects, fixed-effects and the within-between specification for clustered data in observational health studies: A simulation study. PloS One. 2014;9:e110257.
Danks L, Duckett S. All Complications Should Count: Using Our Data to Make Hospitals Safer. Carlton (AUST): Grattan Institute; 2018.
Hofner B, Mayr A, Robinzonov N, Schmid M. Model-based boosting in R: A hands-on tutorial using the R package mboost. Comput Stat. 2014;29:3–35.
Koenker R. Quantile Regression. Cambridge (UK): Cambridge University Press; 2005.
Koenker R. Additive models for quantile regression: Model selection and confidence bandaids. Braz J Probab Stat. 2011;25:239–62.
Koenker R. (2005). Quantile Regression in R: A Vignette. In: Quantile Regression (Econometric Society Monographs). Cambridge (UK): Cambridge University Press; 2005. p. 295–316.
Mundlak Y. On The Pooling Of Time Series And Cross Section Data. Econometrica. 1978;46:69.
van de Pol M, Wright J. A simple method for distinguishing within- versus between-subject effects using mixed models. Anim Behav. 2009;77:753–8.
Field K, Shapiro J, Wong HL, et al. Treatment and outcomes of metastatic colorectal cancer in Australia: Defining differences between public and private practice. Intern Med J. 2015;45:267–74.
Burns EM, Faiz O. Evolution of the surgeon-volume, patient outcome relationship. Ann Surg. 2010;251:991–2. Author Reply 2.
Gibson S, McConigley R. Unplanned oncology admissions within 14 days of non-surgical discharge: A retrospective study. Support Care Cancer. 2016;24:311–17.
Schisterman EF, Cole SR, Platt RW. Overadjustment bias and unnecessary adjustment in epidemiologic studies. Epidemiology. 2009;20:488–95.
Efron B, Hastie T. Computer Age Statistical Inference: Algorithms, Evidence, and Data Science. Cambridge (UK): Cambridge University Press; 2016.
Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York (NY): Springer; 2009.
Hofner B, Hothorn T, Kneib T, Schmid M. A framework for unbiased model selection based on boosting. J Comput Graph Stat. 2011;20:956–71.
Taieb SB, Huser R, Hyndman RJ, Genton MG. Forecasting uncertainty in electricity smart meter data by boosting additive quantile regression. IEEE Trans Smart Grid. 2016;7:2448–55.
R Core Team. The R Project for Statistical Computing. A Language and Environment for Statistical Computing. Vienna (AU): R Foundation for Statistical Computing; 2017.
Koenker R. quantreg: Quantile Regression. Vienna (AU): Institute for Statistics and Mathematics The R Project for Statistical Computing; 2018.
Hothorn T, Buehlmann P, Kneib T, Schmid M, Hofner B. mboost: Model-Based Boosting. R Package Version 2.9–1. Vienna (AU): R Foundation for Statistical Computing; 2017
Jurasinski G, Koebsch F, Guenther A, Beetz S. flux: Flux Rate Calculation from Dynamic Closed Chamber Measurements. Vienna (AU): R Foundation for Statistical Computing; 2014.
Skrondal A, Rabe-Hesketh S. Multilevel Modelling. Thousand Oaks (CA): SAGE Publications; 2010.
Kc DS, Terwiesch C. Impact of workload on service time and patient safety: an econometric analysis of hospital operations. Manage Sci. 2009;55:1486–98.
Faiz O. The volume–outcome relationship in colorectal surgery. Tech Coloproctol. 2014;18(10):961–2.
Kizer KW. The volume–outcome conundrum. N Engl J Med. 2003;349:2159–61.
McGrath DR, Leong DC, Gibberd R, Armstrong B, Spigelman AD. Surgeon and hospital volume and the management of colorectal cancer patients in Australia. ANZ J Surg. 2005;75:901–10.
Jones IT. More evidence of the benefits of case volume and specialization in colorectal surgery. ANZ J Surg. 2005;75:839.
Morris M, Platell CFE. Surgical volume influences survival in patients undergoing resections for stage ii colon cancers. ANZ J Surg. 2007;77:902–6.
Byrne BE, Pinto A, Aylin P, Bottle A, Faiz OD, Vincent CA. Understanding how colorectal units achieve short length of stay: An interview survey among representative hospitals in England. Patient Saf Surg. 2015;9:2.
Cornish JA, Tekkis PP, Tan E, Tilney HS, Thompson MR, Smith JJ. The national bowel cancer audit project: The impact of organisational structure on outcome in operative bowel cancer within the United Kingdom. Surg Oncol. 2011;20:e72–7.
Van Dorp DR, Boston A, Berri RN. Establishing a complex surgical oncology program with low morbidity and mortality at a community hospital. Am J Surg. 2015;209:536–41.
Hohl K. Beyond the average case: The mean focus fallacy of standard linear regression and the use of quantile regression for the social sciences. SSRN. 2009. DOI: 10.2139/ssrn.1434418.
Tukey J. More honest foundations for data analysis. J Stat Plan Inference. 1997;57:21–8.
John OO. Robustness of quantile regression to outliers. Am J Appl Math Stat. 2015;3:86–8.
