References
- Muir KW, Gupta C, Gill P, Stein JD. Accuracy of international classification of diseases, ninth revision, clinical modification billing codes for common ophthalmic conditions. JAMA Ophthalmol. 2013;131(1):119–120. doi:10.1001/jamaophthalmol.2013.577.
- Bearelly S, Mruthyunjaya P, Tzeng JP, et al. Identification of patients with diabetic macular edema from claims data: a validation study. Arch Ophthalmol. 2008;126(7):986–989. doi:10.1001/archopht.126.7.986.
- Lau M, Prenner JL, Brucker AJ, VanderBeek BL. Accuracy of billing codes used in the therapeutic care of diabetic retinopathy. JAMA Ophthalmol. 2017;135(7):791–794. doi:10.1001/jamaophthalmol.2017.1595.
- Quigley HA, Friedman DS, Hahn SR. Evaluation of practice patterns for the care of open-angle glaucoma compared with claims data: the glaucoma adherence and persistency study. Ophthalmology. 2007;114(9):1599–1606. doi:10.1016/j.ophtha.2007.03.042.
- Javitt JC, McBean AM, Sastry SS, DiPaolo F. Accuracy of coding in medicare part B claims. Cataract as a case study. Arch Ophthalmol. 1993;111:605–607.
- Coleman AL, Morgenstern H. Use of insurance claims databases to evaluate the outcomes of ophthalmic surgery. Surv Ophthalmol. 1997;42:271–278.
- Pimentel MA, Browne EN, Janardhana PM, et al. Assessment of the accuracy of using ICD-9 codes to identify uveitis, Herpes Zoster ophthalmicus, scleritis, and episcleritis. JAMA Ophthalmol. 2016;134(9):1001–1006. doi:10.1001/jamaophthalmol.2016.2166.
- Palestine AG, Merrill PT, Saleem SM, Jabs DA, Thorne JE. Assessing the precision of ICD-10 codes for uveitis in 2 electronic health record systems. JAMA Ophthalmol. 2018. doi:10.1001/jamaophthalmol.2018.3001.
- Uchiyama E, Faez S, Nasir H, et al. Accuracy of the international classification of diseases, ninth revision, clinical modification (ICD-9-CM) as a research tool for identification of patients with uveitis and scleritis. Ophthalmic Epidemiol. 2015;22(2):139–141. doi:10.3109/09286586.2015.1012274.
- Tu K, Campbell NR, Chen ZL, Cauch-Dudek KJ, McAlister FA. Accuracy of administrative databases in identifying patients with hypertension. Open Med. 2007;1:e18–26.
- Khokhar B, Jette N, Metcalfe A, et al. Systematic review of validated case definitions for diabetes in ICD-9-coded and ICD-10-coded data in adult populations. BMJ Open. 2016;6(8):e009952. doi:10.1136/bmjopen-2015-009952.
- Stein JD, Blachley TS, Musch DC. Identification of persons with incident ocular diseases using health care claims databases. Am J Ophthalmol. 2013;156(6):1169–1175, e1163. doi:10.1016/j.ajo.2013.06.035.
- Lipscombe LL, Hwee J, Webster L, Shah BR, Booth GL, Tu K. Identifying diabetes cases from administrative data: a population-based validation study. BMC Health Serv Res. 2018;18(1):316. doi:10.1186/s12913-018-3148-0.
- Magder LS, Hughes JP. Logistic regression when the outcome is measured with uncertainty. Am J Epidemiol. 1997;146:195–203.
- Meier AS, Richardson BA, Hughes JP. Discrete proportional hazards models for mismeasured outcomes. Biometrics. 2003;59:947–954.
- Fox MP, Lash TL, Greenland S. A method to automate probabilistic sensitivity analyses of misclassified binary variables. Int J Epidemiol. 2005;34(6):1370–1376. doi:10.1093/ije/dyi184.
- Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res. 2011;46(3):399–424. doi:10.1080/00273171.2011.568786.
- Kim DH, Addis VM, Pan W, VanderBeek BL. Comparative effectiveness of generic latanoprost versus branded prostaglandin analogs for primary open angle glaucoma. Ophthalmic Epidemiol. 2018;1–9.
- Rim TH, Yoo TK, Kwak J, et al. Long-term regular use of low-dose aspirin and neovascular age-related macular degeneration: national sample cohort 2010-2015. Ophthalmology. 2018. doi:10.1016/j.ophtha.2018.09.014.
- Kolomeyer AM, Maguire MG, Pan W, VanderBeek BL. Systemic beta-blockers and risk of progression to neovascular age-related macular degeneration. Retina. 2018. doi:10.1097/IAE.0000000000002059.