References
- Ferris FL, Davis MD, Clemons TE, et al. A simplified severity scale for age-related macular degeneration: AREDS Report No. 18. Arch Ophthalmol. 2005;123(11):1570–1574.
- Hardy RJ, Palmer EA, Dobson V, et al. Risk analysis of prethreshold retinopathy of prematurity. Arch Ophthalmol. 2003;121(12):1697–1701.
- Ocular Hypertension Treatment Study Group, European Glaucoma Prevention Study Group. Validated prediction model for the development of primary open-angle glaucoma in individuals with ocular hypertension. Ophthalmology. 2007;114(1):10–19. doi:https://doi.org/10.1016/j.ophtha.2006.08.031.
- Ying GS, Maguire MG. Complications of age-related macular degeneration prevention trial research G. Development of a risk score for geographic atrophy in complications of the age-related macular degeneration prevention trial. Ophthalmology. 2011;118(2):332–338. doi:https://doi.org/10.1016/j.ophtha.2010.06.030.
- Ying GS, VanderVeen D, Daniel E, Quinn GE, Baumritter A. Telemedicine approaches to evaluating acute-phase retinopathy of prematurity cooperative G. Risk score for predicting treatment-requiring retinopathy of prematurity (ROP) in the telemedicine approaches to evaluating acute-phase ROP study. Ophthalmology. 2016;123(10):2176–2182. doi:https://doi.org/10.1016/j.ophtha.2016.06.037.
- Maguire MG. Assessing intereye symmetry and its implications for study design. Invest Ophthalmol Vis Sci. 2020;61(6):27. doi:https://doi.org/10.1167/iovs.61.6.27.
- Ying GS, Maguire MG, Glynn RJ, Rosner B. Calculating sensitivity, specificity, and predictive values for correlated eye data. Invest Ophthalmol Vis Sci. 2020;61(11):29. doi:https://doi.org/10.1167/iovs.61.11.29.
- Seddon JM, Reynolds R, Yu Y, Rosner B. Validation of a prediction algorithm for progression to advanced macular degeneration subtypes. JAMA Ophthalmol. 2013;131(4):448–455. doi:https://doi.org/10.1001/jamaophthalmol.2013.2578.
- Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143(1):29–36. doi:https://doi.org/10.1148/radiology.143.1.7063747.
- Choi BC. Slopes of a receiver operating characteristic curve and likelihood ratios for a diagnostic test. Am J Epidemiol. 1998;148(11):1127–1132. doi:https://doi.org/10.1093/oxfordjournals.aje.a009592.
- 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(3):837–845. doi:https://doi.org/10.2307/2531595.
- Demler OV, Pencina MJ, D’Agostino RB. Sr. Misuse of DeLong test to compare AUCs for nested models. Stat Med. 2012;31(23):2577–2587. doi:https://doi.org/10.1002/sim.5328.
- Margolis DJ, Bilker W, Boston R, Localio R, Berlin JA. Statistical characteristics of area under the receiver operating characteristic curve for a simple prognostic model using traditional and bootstrapped approaches. J Clin Epidemiol. 2002;55(5):518–524. doi:https://doi.org/10.1016/S0895-4356(01)00512-1.
- Gonen M. Analyzing Receiver Operating Characteristic Curves with SAS. Cary, NC: SAS Institute Inc; 2007.
- Obuchowski NA. Nonparametric analysis of clustered ROC curve data. Biometrics. 1997;53(2):567–578. doi:https://doi.org/10.2307/2533958.
- Huang FL. Using cluster bootstrapping to analyze nested data with a few clusters. Educ Psychol Meas. 2018;78(2):297–318. doi:https://doi.org/10.1177/0013164416678980.
- Rao JN, Scott AJ. A simple method for the analysis of clustered binary data. Biometrics. 1992;48(2):577–585. doi:https://doi.org/10.2307/2532311.
- Efron B, Gong G. A leisurely look at the bootstrap, the jackknife, and cross-validation. Am Statistician. 1983;37:36–48.
- Rutter CM. Bootstrap estimation of diagnostic accuracy with patient-clustered data. Acad Radiol. 2000;7(6):413–419. doi:https://doi.org/10.1016/S1076-6332(00)80381-5.
- Efron B. Better bootstrap confidence intervals (with discussion). J Am Stat Assoc. 1987;82:171–200. doi:https://doi.org/10.1080/01621459.1987.10478410.
- Evangelou N, Konz D, Esiri MM, Smith S, Palace J, Matthews PM. Size-selective neuronal changes in the anterior optic pathways suggest a differential susceptibility to injury in multiple sclerosis. Brain. 2001;124(Pt 9):1813–1820. doi:https://doi.org/10.1093/brain/124.9.1813.
- Zaveri MS, Conger A, Salter A, et al. Retinal imaging by laser polarimetry and optical coherence tomography evidence of axonal degeneration in multiple sclerosis. Arch Neurol. 2008;65(7):924–928. doi:https://doi.org/10.1001/archneur.65.7.924.
- The Age-related Eye Disease Study Research Group. The Age-Related Eye Disease Study (AREDS): design implications. AREDS report no. 1. Control Clin Trials. 1999;20(6):573–600. doi:https://doi.org/10.1016/S0197-2456(99)00031-8.
- Davis MD, Gangnon RE, Lee LY, et al. The age-related eye disease study severity scale for age-related macular degeneration: AREDS Report No. 17. Arch Ophthalmol. 2005;123(11):1484–1498.
- Quinn GE, Ying GS, Daniel E, et al. Validity of a telemedicine system for the evaluation of acute-phase retinopathy of prematurity. JAMA Ophthalmol. 2014;132(10):1178–1184. doi:https://doi.org/10.1001/jamaophthalmol.2014.1604.
- Cicchetti DV, Alison T. A new procedure for assessing reliability of scoring EEG sleep recordings. Am J EEG Technol. 1971;11:101–109. doi:https://doi.org/10.1080/00029238.1971.11080840.
- Cameron AG, Miller JB. DL. Bootstrap-basead improvements for inference with clustered errors. Rev Econ Stat. 2008;90(3):414–427. doi:https://doi.org/10.1162/rest.90.3.414.