References
- Steyerberg EW. Clinical prediction models: a practical approach to development, validation, and updating. New York: Springer, 2009
- D'Agostino RB Sr, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation 2008;117:743-753 https://doi.org/10.1161/CIRCULATIONAHA.107.699579
- Yang HI, Yuen MF, Chan HL, Han KH, Chen PJ, Kim DY, et al. Risk estimation for hepatocellular carcinoma in chronic hepatitis B (REACH-B): development and validation of a predictive score. Lancet Oncol 2011;12:568-574 https://doi.org/10.1016/S1470-2045(11)70077-8
- Kwak JY, Jung I, Baek JH, Baek SM, Choi N, Choi YJ, et al. Image reporting and characterization system for ultrasound features of thyroid nodules: multicentric Korean retrospective study. Korean J Radiol 2013;14:110-117 https://doi.org/10.3348/kjr.2013.14.1.110
- Kim SY, Lee HJ, Kim YJ, Hur J, Hong YJ, Yoo KJ, et al. Coronary computed tomography angiography for selecting coronary artery bypass graft surgery candidates. Ann Thorac Surg 2013;95:1340-1346 https://doi.org/10.1016/j.athoracsur.2013.01.004
- Yoon YE, Lim TH. Current roles and future applications of cardiac CT: risk stratification of coronary artery disease. Korean J Radiol 2014;15:4-11 https://doi.org/10.3348/kjr.2014.15.1.4
- Shaw LJ, Giambrone AE, Blaha MJ, Knapper JT, Berman DS, Bellam N, et al. Long-term prognosis after coronary artery calcification testing in asymptomatic patients: a cohort study. Ann Intern Med 2015;163:14-21 https://doi.org/10.7326/M14-0612
- Lee K, Hur J, Hong SR, Suh YJ, Im DJ, Kim YJ, et al. Predictors of recurrent stroke in patients with ischemic stroke: comparison study between transesophageal echocardiography and cardiac CT. Radiology 2015;276:381-389 https://doi.org/10.1148/radiol.15142300
- Suh YJ, Hong YJ, Lee HJ, Hur J, Kim YJ, Lee HS, et al. Prognostic value of SYNTAX score based on coronary computed tomography angiography. Int J Cardiol 2015;199:460-466 https://doi.org/10.1016/j.ijcard.2015.07.067
- Sunshine JH, Applegate KE. Technology assessment for radiologists. Radiology 2004;230:309-314 https://doi.org/10.1148/radiol.2302031277
- Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med 2015;162:55-63 https://doi.org/10.7326/M14-0697
- Bossuyt PM, Leeflang MM. Chapter 6: Developing Criteria for Including Studies. In: Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy Version 0.4 [updated September 2008]. Oxford: The Cochrane Collaboration, 2008
- Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 1996;49:1373-1379 https://doi.org/10.1016/S0895-4356(96)00236-3
- Peduzzi P, Concato J, Feinstein AR, Holford TR. Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates. J Clin Epidemiol 1995;48:1503-1510 https://doi.org/10.1016/0895-4356(95)00048-8
- Vittinghoff E, McCulloch CE. Relaxing the rule of ten events per variable in logistic and Cox regression. Am J Epidemiol 2007;165:710-718 https://doi.org/10.1093/aje/kwk052
- Steyerberg EW, Harrell FE Jr, Borsboom GJ, Eijkemans MJ, Vergouwe Y, Habbema JD. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol 2001;54:774-781 https://doi.org/10.1016/S0895-4356(01)00341-9
- Royston P, Altman DG, Sauerbrei W. Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med 2006;25:127-141 https://doi.org/10.1002/sim.2331
- Altman DG, Bland JM. Absence of evidence is not evidence of absence. BMJ 1995;311:485 https://doi.org/10.1136/bmj.311.7003.485
- Austin PC, Tu JV. Automated variable selection methods for logistic regression produced unstable models for predicting acute myocardial infarction mortality. J Clin Epidemiol 2004;57:1138-1146 https://doi.org/10.1016/j.jclinepi.2004.04.003
- Austin PC, Tu JV. Bootstrap methods for developing predictive models. Am Stat 2004;58:131-137 https://doi.org/10.1198/0003130043277
- Austin PC. Bootstrap model selection had similar performance for selecting authentic and noise variables compared to backward variable elimination: a simulation study. J Clin Epidemiol 2008;61:1009-1017.e1 https://doi.org/10.1016/j.jclinepi.2007.11.014
- Little RJA, Rubin DB. Statistical analysis with missing data, 2nd ed. New York: John Wiley & Sons, 2014
- Pepe MS, Janes H, Longton G, Leisenring W, Newcomb P. Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Am J Epidemiol 2004;159:882-890 https://doi.org/10.1093/aje/kwh101
- Nagelkerke NJ. A note on a general definition of the coefficient of determination. Biometrika 1991;78:691-692 https://doi.org/10.1093/biomet/78.3.691
- Tjur T. Coefficients of determination in logistic regression models-A new proposal: the coefficient of discrimination. Am Stat 2009;63;366-372 https://doi.org/10.1198/tast.2009.08210
- Rufibach K. Use of Brier score to assess binary predictions. J Clin Epidemiol 2010;63:938-939; author reply 939 https://doi.org/10.1016/j.jclinepi.2009.11.009
- Hosmer Jr DW, Lemeshow S. Applied logistic regression. New York: John Wiley & Sons, 2004
- D'Agostino R, Nam, BH. Evaluation of the performance of survival analysis models: discrimination and calibration measures. In: Balakrishnan N, Rao CO, eds. Handbook of statistics: advances in survival analysis. Vol 23. Amsterdam: Elsevier, 2004:1-25
- Hosmer DW, Hosmer T, Le Cessie S, Lemeshow S. A comparison of goodness-of-fit tests for the logistic regression model. Stat Med 1997;16:965-980 https://doi.org/10.1002/(SICI)1097-0258(19970515)16:9<965::AID-SIM509>3.0.CO;2-O
- Park SH, Goo JM, Jo CH. Receiver operating characteristic (ROC) curve: practical review for radiologists. Korean J Radiol 2004;5:11-18 https://doi.org/10.3348/kjr.2004.5.1.11
- Harrell FE. Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. New York: Springer, 2001
- Pencina MJ, D'Agostino RB Sr, Song L. Quantifying discrimination of Framingham risk functions with different survival C statistics. Stat Med 2012;31:1543-1553 https://doi.org/10.1002/sim.4508
- Van Calster B, Van Belle V, Vergouwe Y, Timmerman D, Van Huffel S, Steyerberg EW. Extending the c-statistic to nominal polytomous outcomes: the Polytomous Discrimination Index. Stat Med 2012;31:2610-2626 https://doi.org/10.1002/sim.5321
- Van Oirbeek R, Lesaffre E. An application of Harrell's C-index to PH frailty models. Stat Med 2010;29:3160-3171 https://doi.org/10.1002/sim.4058
- Wolbers M, Blanche P, Koller MT, Witteman JC, Gerds TA. Concordance for prognostic models with competing risks. Biostatistics 2014;15:526-539 https://doi.org/10.1093/biostatistics/kxt059
- Vergouwe Y, Steyerberg EW, Eijkemans MJ, Habbema JD. Substantial effective sample sizes were required for external validation studies of predictive logistic regression models. J Clin Epidemiol 2005;58:475-483 https://doi.org/10.1016/j.jclinepi.2004.06.017
- Collins GS, Ogundimu EO, Altman DG. Sample size considerations for the external validation of a multivariable prognostic model: a resampling study. Stat Med 2016;35:214-226 https://doi.org/10.1002/sim.6787
- Collins GS, de Groot JA, Dutton S, Omar O, Shanyinde M, Tajar A, et al. External validation of multivariable prediction models: a systematic review of methodological conduct and reporting. BMC Med Res Methodol 2014;14:40 https://doi.org/10.1186/1471-2288-14-40
- DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988;44:837-845 https://doi.org/10.2307/2531595
- Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 2007;115:928-935 https://doi.org/10.1161/CIRCULATIONAHA.106.672402
- Demler OV, Pencina MJ, D'Agostino RB Sr. Misuse of DeLong test to compare AUCs for nested models. Stat Med 2012;31:2577-2587 https://doi.org/10.1002/sim.5328
- Ware JH. The limitations of risk factors as prognostic tools. N Engl J Med 2006;355:2615-2617 https://doi.org/10.1056/NEJMp068249
- Pencina MJ, D'Agostino RB Sr, D'Agostino RB Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 2008;27:157-172; discussion 207-212 https://doi.org/10.1002/sim.2929
- Pepe MS. Problems with risk reclassification methods for evaluating prediction models. Am J Epidemiol 2011;173:1327-1335 https://doi.org/10.1093/aje/kwr013
- Leening MJ, Vedder MM, Witteman JC, Pencina MJ, Steyerberg EW. Net reclassification improvement: computation, interpretation, and controversies: a literature review and clinician's guide. Ann Intern Med 2014;160:122-131
- Pepe MS, Janes H. Commentary: reporting standards are needed for evaluations of risk reclassification. Int J Epidemiol 2011;40:1106-1108 https://doi.org/10.1093/ije/dyr083
- Widera C, Pencina MJ, Bobadilla M, Reimann I, Guba-Quint A, Marquardt I, et al. Incremental prognostic value of biomarkers beyond the GRACE (Global Registry of Acute Coronary Events) score and high-sensitivity cardiac troponin T in non-ST-elevation acute coronary syndrome. Clin Chem 2013;59:1497-1505 https://doi.org/10.1373/clinchem.2013.206185
- Pencina MJ, D'Agostino RB Sr, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med 2011;30:11-21 https://doi.org/10.1002/sim.4085
- Pepe MS, Kerr KF, Longton G, Wang Z. Testing for improvement in prediction model performance. Stat Med 2013;32:1467-1482 https://doi.org/10.1002/sim.5727
- Pepe MS, Janes H, Li CI. Net risk reclassification p values: valid or misleading? J Natl Cancer Inst 2014;106:dju041
- Kerr KF, McClelland RL, Brown ER, Lumley T. Evaluating the incremental value of new biomarkers with integrated discrimination improvement. Am J Epidemiol 2011;174:364-374 https://doi.org/10.1093/aje/kwr086
- Pencina MJ, D'Agostino RB, Pencina KM, Janssens AC, Greenland P. Interpreting incremental value of markers added to risk prediction models. Am J Epidemiol 2012;176:473-481 https://doi.org/10.1093/aje/kws207
- Sullivan LM, Massaro JM, D'Agostino RB Sr. Presentation of multivariate data for clinical use: The Framingham Study risk score functions. Stat Med 2004;23:1631-1660 https://doi.org/10.1002/sim.1742
- Imperiale TF, Monahan PO, Stump TE, Glowinski EA, Ransohoff DF. Derivation and Validation of a Scoring System to Stratify Risk for Advanced Colorectal Neoplasia in Asymptomatic Adults: A Cross-sectional Study. Ann Intern Med 2015;163:339-346 https://doi.org/10.7326/M14-1720
- Schnabel RB, Sullivan LM, Levy D, Pencina MJ, Massaro JM, D'Agostino RB Sr, et al. Development of a risk score for atrial fibrillation (Framingham Heart Study): a community-based cohort study. Lancet 2009;373:739-745 https://doi.org/10.1016/S0140-6736(09)60443-8
- Janssen KJ, Moons KG, Kalkman CJ, Grobbee DE, Vergouwe Y. Updating methods improved the performance of a clinical prediction model in new patients. J Clin Epidemiol 2008;61:76-86 https://doi.org/10.1016/j.jclinepi.2007.04.018
- Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig L, et al. STARD 2015: An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies. Radiology 2015;277:826-832 https://doi.org/10.1148/radiol.2015151516
- Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 2015;162:W1-W73 https://doi.org/10.7326/M14-0698
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