1.
Armstrong, KA, Metlay, JP. Annals clinical decision making: communicating risk and engaging patients in shared decision making. Ann Intern Med. 2020;172(10):688–92.
Google Scholar |
Crossref2.
Meid, AD, Ruff, C, Wirbka, L, et al. Using the causal inference framework to support individualized drug treatment decisions based on observational healthcare data. Clin Epidemiol. 2020;12:1223–34.
Google Scholar |
Crossref |
Medline3.
Kent, DM, Hayward, RA. Limitations of applying summary results of clinical trials to individual patients: the need for risk stratification. JAMA. 2007;298(10):1209–12.
Google Scholar |
Crossref |
Medline4.
Dahabreh, IJ, Hayward, R, Kent, DM. Using group data to treat individuals: understanding heterogeneous treatment effects in the age of precision medicine and patient-centred evidence. Int J Epidemiol. 2016;45(6):2184–93.
Google Scholar5.
Armstrong, KA, Metlay, JP. Annals clinical decision making: translating population evidence to individual patients. Ann Intern Med. 2020;172(9):610–6.
Google Scholar |
Crossref6.
Chen, S, Tian, L, Cai, T, Yu, M. A general statistical framework for subgroup identification and comparative treatment scoring. Biometrics. 2017;73(4):1199–209.
Google Scholar |
Crossref7.
Graham, DJ, Baro, E, Zhang, R, et al. Comparative stroke, bleeding, and mortality risks in older Medicare patients treated with oral anticoagulants for nonvalvular atrial fibrillation. Am J Med. 2019;132(5):596–604.e11.
Google Scholar |
Crossref8.
Lip, GYH, Keshishian, A, Li, X, et al. Effectiveness and safety of oral anticoagulants among nonvalvular atrial fibrillation patients. Stroke. 2018;49(12):2933–44.
Google Scholar |
Crossref9.
Duan, T, Rajpurkar, P, Laird, D, Ng, AY, Basu, S. Clinical value of predicting individual treatment effects for intensive blood pressure therapy. Circ Cardiovasc Qual Outcomes. 2019;12(3):e005010.
Google Scholar |
Crossref |
Medline10.
Yang, CY, Lin, WA, Su, PF, et al. Heterogeneous treatment effects on cardiovascular diseases with dipeptidyl peptidase-4 inhibitors versus sulfonylureas in type 2 diabetes patients. Clin Pharmacol Ther. 2021;109(3):772–81.
Google Scholar |
Crossref11.
Yao, X, Abraham, NS, Sangaralingham, LR, et al. Effectiveness and safety of dabigatran, rivaroxaban, and apixaban versus warfarin in nonvalvular atrial fibrillation. J Am Heart Assoc. 2016;5(6):e003725.
Google Scholar |
Crossref |
Medline12.
Gupta, K, Trocio, J, Keshishian, A, et al. Real-world comparative effectiveness, safety, and health care costs of oral anticoagulants in nonvalvular atrial fibrillation patients in the U.S. Department of Defense population. J Manag Care Spec Pharm. 2018;24(11):1116–27.
Google Scholar |
Medline13.
Amin, A, Keshishian, A, Trocio, J, et al. A real-world observational study of hospitalization and health care costs among nonvalvular atrial fibrillation patients prescribed oral anticoagulants in the U.S. Medicare population. J Manag Care Spec Pharm. 2018;24(9):911–20.
Google Scholar |
Medline14.
Cha, MJ, Choi, EK, Han, KD, et al. Effectiveness and safety of non-vitamin K antagonist oral anticoagulants in asian patients with atrial fibrillation. Stroke. 2017;48(11):3040–8.
Google Scholar |
Crossref15.
Marietta, M, Banchelli, F, Pavesi, P, et al. Direct oral anticoagulants vs non-vitamin K antagonist in atrial fibrillation: a prospective, propensity score adjusted cohort study. Eur J Intern Med. 2019;62:9–16.
Google Scholar |
Crossref |
Medline16.
Nielsen, PB, Skjoth, F, Sogaard, M, Kjaeldgaard, JN, Lip, GY, Larsen, TB. Effectiveness and safety of reduced dose non-vitamin K antagonist oral anticoagulants and warfarin in patients with atrial fibrillation: propensity weighted nationwide cohort study. BMJ. 2017;356:j510.
Google Scholar |
Crossref |
Medline17.
Staerk, L, Gerds, TA, Lip, GYH, et al. Standard and reduced doses of dabigatran, rivaroxaban and apixaban for stroke prevention in atrial fibrillation: a nationwide cohort study. J Intern Med. 2018;283(1):45–55.
Google Scholar |
Crossref |
Medline18.
Coleman, CI, Peacock, WF, Bunz, TJ, Alberts, MJ. Effectiveness and safety of apixaban, dabigatran, and rivaroxaban versus warfarin in patients with nonvalvular atrial fibrillation and previous stroke or transient ischemic attack. Stroke. 2017;48(8):2142–9.
Google Scholar |
Crossref |
Medline19.
Yu, A, Jeyakumar, Y, Wang, M, Lee, J, Marcucci, M, Holbrook, A. How personalized are benefit and harm results of randomized trials? A systematic review. J Clin Epidemiol. 2020;126:17–25.
Google Scholar |
Crossref |
Medline20.
Hu, L, Ji, J, Li, F. Estimating heterogeneous survival treatment effect in observational data using machine learning. Stat Med. 2021;40:4691–713.
Google Scholar |
Crossref21.
Fang, G, Annis, IE, Elston-Lafata, J, Cykert, S. Applying machine learning to predict real-world individual treatment effects: insights from a virtual patient cohort. J Am Med Inform Assoc. 2019;26:977–88.
Google Scholar |
Crossref |
Medline22.
Wendling, T, Jung, K, Callahan, A, Schuler, A, Shah, NH, Gallego, B. Comparing methods for estimation of heterogeneous treatment effects using observational data from health care databases. Stat Med. 2018;37:3309–24.
Google Scholar |
Crossref |
Medline23.
Collins, GS, Reitsma, JB, Altman, DG, Moons, KG. Transparent reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement. J Clin Epidemiol. 2015;68(2):134–43.
Google Scholar |
Crossref |
Medline24.
Wirbka, L, Haefeli, WE, Meid, AD. Estimated Thresholds of Minimum Necessary Adherence for Effective Treatment with Direct Oral Anticoagulants - A Retrospective Cohort Study in Health Insurance Claims Data. Patient Prefer Adherence. 2021;15:2209–2220.
Google Scholar |
Crossref |
Medline25.
Quan, H, Sundararajan, V, Halfon, P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43(11):1130–9.
Google Scholar |
Crossref |
Medline26.
van Walraven, C, Austin, PC, Jennings, A, Quan, H, Forster, AJ. A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data. Med Care. 2009;47(6):626–33.
Google Scholar |
Crossref |
Medline27.
Lip, GY, Nieuwlaat, R, Pisters, R, Lane, DA, Crijns, HJ. Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the euro heart survey on atrial fibrillation. Chest. 2010;137(2):263–72.
Google Scholar |
Crossref28.
Huling, JD, Yu, M, Liang, M, Smith, M. Risk prediction for heterogeneous populations with application to hospital admission prediction. Biometrics. 2018;74(2):557–65.
Google Scholar |
Crossref |
Medline29.
Kunzel, SR, Sekhon, JS, Bickel, PJ, Yu, B. Metalearners for estimating heterogeneous treatment effects using machine learning. Proc Natl Acad Sci U S A. 2019;116(10):4156–65.
Google Scholar |
Crossref |
Medline30.
Vock, DM, Wolfson, J, Bandyopadhyay, S, et al. Adapting machine learning techniques to censored time-to-event health record data: a general-purpose approach using inverse probability of censoring weighting. J Biomed Inform. 2016;61:119–31.
Google Scholar |
Crossref |
Medline31.
Probst, P, Wright, M, Boulesteix, A. Hyperparameters and tuning strategies for random forest. WIREs Data Mining Knowl Discov. 2019;9(3):e1301.
Google Scholar |
Crossref32.
Cartus, AR, Bodnar, LM, Naimi, AI. The impact of undersampling on the predictive performance of logistic regression and machine learning algorithms: a simulation study. Epidemiology. 2020;31(5):e42–4.
Google Scholar |
Crossref |
Medline33.
Dinstag, G, Amar, D, Ingelsson, E, Ashley, E, Shamir, R. Personalized prediction of adverse heart and kidney events using baseline and longitudinal data from SPRINT and ACCORD. PLoS One. 2019;14(8):e0219728.
Google Scholar |
Crossref |
Medline34.
van Klaveren, D, Steyerberg, EW, Serruys, PW, Kent, DM. The proposed ‘concordance-statistic for benefit’ provided a useful metric when modeling heterogeneous treatment effects. J Clin Epidemiol. 2018;94:59–68.
Google Scholar |
Crossref |
Medline35.
Sachs, MC, Sjolander, A, Gabriel, EE. Aim for clinical utility, not just predictive accuracy. Epidemiology. 2020;31(3):359–64.
Google Scholar |
Crossref |
Medline36.
Wirbka, L, Haefeli, WE, Meid, AD. A framework to build similarity-based cohorts for personalized treatment advice—a standardized, but flexible workflow with the R package SimBaCo. PLoS One. 2020;15(5):e0233686.
Google Scholar |
Crossref |
Medline37.
Wright, MN, Ziegler, A. ranger: a fast implementation of random forests for high dimensional data in C++ and R. J Stat Soft. 2017;77(1):1–17.
Google Scholar |
Crossref38.
Almutairi, AR, Zhou, L, Gellad, WF, et al. Effectiveness and safety of non-vitamin K antagonist oral anticoagulants for atrial fibrillation and venous thromboembolism: a systematic review and meta-analyses. Clin Ther. 2017;39(7):1456–78e36.
Google Scholar |
Crossref |
Medline39.
Ruff, CT, Giugliano, RP, Braunwald, E, et al. Comparison of the efficacy and safety of new oral anticoagulants with warfarin in patients with atrial fibrillation: a meta-analysis of randomised trials. Lancet. 2014;383(9921):955–62.
Google Scholar |
Crossref |
Medline40.
Centers for Disease Control and Prevention . Atrial fibrillation as a contributing cause of death and Medicare hospitalization—United States, 1999. MMWR Morb Mortal Wkly Rep. 2003;52(7):128, 130–1.
Google Scholar41.
Kannel, WB, Benjamin, EJ. Current perceptions of the epidemiology of atrial fibrillation. Cardiol Clin. 2009;27(1):13–24, vii.
Google Scholar |
Crossref |
Medline |
ISI42.
Li, X, Tse, VC, Au-Doung, LW, Wong, ICK, Chan, EW. The impact of ischaemic stroke on atrial fibrillation-related healthcare cost: a systematic review. Europace. 2017;19(6):937–47.
Google Scholar43.
Wang, G, Joo, H, Tong, X, George, MG. Hospital costs associated with atrial fibrillation for patients with ischemic stroke aged 18-64 years in the United States. Stroke. 2015;46(5):1314–20.
Google Scholar |
Crossref44.
Qazi, JZ, Schnitzer, ME, Cote, R, Martel, MJ, Dorais, M, Perreault, S. Predicting major bleeding among hospitalized patients using oral anticoagulants for atrial fibrillation after discharge. PLoS One. 2021;16(3):e0246691.
Google Scholar |
Crossref45.
Kent, DM, Paulus, JK, van Klaveren, D, et al. The Predictive Approaches to Treatment effect Heterogeneity (PATH) statement. Ann Intern Med. 2020;172(1):35–45.
Google Scholar |
Crossref |
Medline46.
Ruff, C, Koukalova, L, Haefeli, WE, Meid, AD. The role of adherence thresholds for development and performance aspects of a prediction model for direct oral anticoagulation adherence. Front Pharmacol. 2019;10:113.
Google Scholar |
Crossref |
Medline47.
van der Ploeg, T, Austin, PC, Steyerberg, EW. Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints. BMC Med Res Methodol. 2014;14:137.
Google Scholar |
Crossref |
Medline48.
Steyerberg, EW. Validation in prediction research: the waste by data splitting. J Clin Epidemiol. 2018;103:131–3.
Google Scholar |
Crossref |
Medline49.
Suwa, M, Morii, I, Kino, M. Rivaroxaban or apixaban for non-valvular atrial fibrillation-efficacy and safety of off-label under-dosing according to plasma concentration. Circ J. 2019;83:991–9.
Google Scholar |
Crossref50.
Bonner, C, Trevena, LJ, Gaissmaier, W, et al. Current best practice for presenting probabilities in patient decision aids: fundamental principles. Med DecisMaking. 2021;41(7):821–33.
Google Scholar51.
Chen, A, Stecker, E, Warden, BA. Direct oral anticoagulant use: a practical guide to common clinical challenges. J Am Heart Assoc. 2020;9(13):e017559.
Google Scholar |
Crossref52.
Han, PKJ, Strout, TD, Gutheil, C, et al. How physicians manage medical uncertainty: a qualitative study and conceptual taxonomy. Med Decis Making. 2021;41(3):275–91.
Google Scholar |
SAGE Journals53.
Han, PK, Klein, WM, Lehman, T, Killam, B, Massett, H, Freedman, AN. Communication of uncertainty regarding individualized cancer risk estimates: effects and influential factors. Med Decis Making. 2011;31(2):354–66.
Google Scholar |
SAGE Journals54.
Waters, EA, Maki, J, Liu, Y, et al. Risk ladder, table, or bulleted list? Identifying formats that effectively communicate personalized risk and risk reduction information for multiple diseases. Med Decis Making. 2021;41(1):74–88.
Google Scholar |
SAGE Journals55.
Seligson, ND, War
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