Joeky Senders

104 Chapter 5 References 1. Ostrom QT, Gittleman H, Liao P, et al. CBTRUS Statistical Report: Primary brain and other central nervous system tumors diagnosed in the United States in 2010–2014. Neuro-Oncology. 2017;19(suppl_5):v1-v88. doi:10.1093/neuonc/nox158 2. Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015;350:g7594. doi:10.1136/ bmj.g7594 3. Mohanty S, Bilimoria KY. Comparing national cancer registries: The National Cancer Data Base (NCDB) and the Surveillance, Epidemiology, and End Results (SEER) program. J Surg Oncol. 2014;109(7):629-630. doi:10.1002/jso.23568 4. Altekruse SF, Rosenfeld GE, Carrick DM, et al. SEER Cancer Registry Biospecimen Research: Yesterday and Tomorrow. Cancer Epidemiol Biomarkers Prev. 2014;23(12):2681-2687. doi:10.1158/1055-9965.EPI- 14-0490 5. Waljee AK, Mukherjee A, Singal AG, et al. Comparison of imputation methods for missing laboratory data in medicine. BMJ Open. 2013;3(8). doi:10.1136/bmjopen-2013-002847 6. Senders JT, Arnaout O, Karhade AV, et al. Natural and Artificial Intelligence in Neurosurgery: A Systematic Review. Neurosurgery. 2017. doi:10.1093/neuros/nyx384 7. Dietterich TG. Ensemble Methods in Machine Learning. In: Multiple Classifier Systems. Vol 1857. Berlin, Heidelberg: Springer Berlin Heidelberg; 2000:1-15. doi:10.1007/3-540-45014-9_1 8. Zare A, HOSSEINI M, MAHMOODI M, MOHAMMAD K, ZERAATI H, HOLAKOUIE NAIENI K. A Comparison between Accelerated Failure-time and Cox Proportional Hazard Models in Analyzing the Survival of Gastric Cancer Patients. Iran J Public Health. 2015;44(8):1095-1102. 9. Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: a framework for some traditional and novel measures. Epidemiology. 2010;21(1):128-138. doi:10.1097/ EDE.0b013e3181c30fb2 10. Uno H, Cai T, Pencina MJ, D’Agostino RB, Wei LJ. On the C-statistics for Evaluating Overall Adequacy of Risk Prediction Procedures with Censored Survival Data. Stat Med. 2011;30(10):1105-1117. doi:10.1002/ sim.4154 11. R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/. Published 2008. Accessed June 11, 2018. 12. Kuhn M. Building Predictive Models in R Using the caret Package | Kuhn | Journal of Statistical Software. Journal of Statisticla Software. 2008. doi:10.18637/jss.v028.i05 13. Chang W, Cheng J, Allaire JJ, et al. Shiny: Web Application Framework for R.; 2018. https://CRAN.R-project. org/package=shiny. Accessed June 11, 2018. 14. Gorlia T, Bent MJ van den, Hegi ME, et al. Nomograms for predicting survival of patients with newly diagnosed glioblastoma: prognostic factor analysis of EORTC and NCIC trial 26981-22981/CE.3. The Lancet Oncology. 2008;9(1):29-38. doi:10.1016/S1470-2045(07)70384-4 15. Gittleman H, Lim D, Kattan MW, et al. An independently validated nomogram for individualized estimation of survival among patients with newly diagnosed glioblastoma: NRG Oncology RTOG 0525 and 0825. Neuro Oncol. 2017;19(5):669-677. doi:10.1093/neuonc/now208 16. Marko NF, Weil RJ, Schroeder JL, Lang FF, Suki D, Sawaya RE. Extent of Resection of Glioblastoma Revisited: Personalized Survival Modeling Facilitates More Accurate Survival Prediction and Supports a Maximum- Safe-Resection Approach to Surgery. JCO. 2014;32(8):774-782. doi:10.1200/JCO.2013.51.8886