In ablack3/Hades: Health Analytics Data-to-Evidence Suite

These papers were written using HADES.

Clinical application

• Lane JCE, Weaver J, Kostka K, et al. Risk of hydroxychloroquine alone and in combination with azithromycin in the treatment of rheumatoid arthritis: a multinational, retrospective study. Lancet Rheumatology. 2020 Aug 21.

• Kim Y, Tian Y, Yang J, et al. Comparative safety and effectiveness of alendronate versus raloxifene in women with osteoporosis. Sci Rep. 2020 Jul 6;10(1):11115.

• Reps JM, Williams RD, You SC, et al. Feasibility and evaluation of a large-scale external validation approach for patient-level prediction in an international data network: validation of models predicting stroke in female patients newly diagnosed with atrial fibrillation. BMC Med Res Methodol. 2020 May 6;20(1):102.

• Hripcsak G, Suchard MA, Shea S, et al. Comparison of Cardiovascular and Safety Outcomes of Chlorthalidone vs Hydrochlorothiazide to Treat Hypertension. JAMA Intern Med. 2020 Feb 17.

• Reps JM, Cepeda MS, Ryan PB. Wisdom of the CROUD: Development and validation of a patient-level prediction model for opioid use disorder using population-level claims data. PLoS One. 2020 Feb 13;15(2):e0228632.

• Wang Q, Reps JM, Kostka KF, et al. Development and validation of a prognostic model predicting symptomatic hemorrhagic transformation in acute ischemic stroke at scale in the OHDSI network. PLoS One. 2020 Jan 7;15(1):e0226718.

• Suchard MA, Schuemie MJ, Krumholz HM, et al. Comprehensive comparative effectiveness and safety of first-line antihypertensive drug classes: a systematic, multinational, large-scale analysis. Lancet. 2019 Nov 16;394(10211):1816-1826.

• You SC, Jung S, Swerdel JN, Ryan PB, et al. Comparison of First-Line Dual Combination Treatments in Hypertension: Real-World Evidence from Multinational Heterogeneous Cohorts. Korean Circ J. 2020 Jan;50(1):52-68.

• Johnston SS, Morton JM, Kalsekar I, et al. Using Machine Learning Applied to Real-World Healthcare Data for Predictive Analytics: An Applied Example in Bariatric Surgery. Value Health. 2019 May;22(5):580-586.

• Weinstein RB, Ryan PB, Berlin JA, et al. Channeling Bias in the Analysis of Risk of Myocardial Infarction, Stroke, Gastrointestinal Bleeding, and Acute Renal Failure with the Use of Paracetamol Compared with Ibuprofen. Drug Saf. 2020 Sep;43(9):927-942.

• Vashisht R, Jung K, Schuler A, et al. Association of hemoglobin a1c levels with use of sulfonylureas, dipeptidyl peptidase 4 inhibitors, and thiazolidinediones in patients with type 2 diabetes treated with metformin: Analysis from the observational health data sciences and informatics initiative. JAMA Network Open. 2018; 1: e181755.

• Ryan PB, Buse JB, Schuemie MJ, et al. Comparative effectiveness of canagliflozin, SGLT2 inhibitors and non-SGLT2 inhibitors on the risk of hospitalization for heart failure and amputation in patients with type 2 diabetes mellitus: A real-world meta-analysis of 4 observational databases (OBSERVE-4D). Diabetes, obesity & metabolism. 2018; 20: 2585-97.

• Yuan Z, DeFalco FJ, Ryan PB, et al. Risk of lower extremity amputations in people with type 2 diabetes mellitus treated with sodium-glucose co-transporter-2 inhibitors in the USA: A retrospective cohort study. Diabetes, obesity & metabolism. 2018; 20: 582-9.

• Weinstein RB, Ryan P, Berlin JA, et al. Channeling in the Use of Nonprescription Paracetamol and Ibuprofen in an Electronic Medical Records Database: Evidence and Implications. Drug safety. 2017; 40: 1279-92.

• Wang Y, Desai M, Ryan PB, et al. Incidence of diabetic ketoacidosis among patients with type 2 diabetes mellitus treated with SGLT2 inhibitors and other antihyperglycemic agents. Diabetes Res Clin Pract. 2017; 128: 83-90.

• Ryan PB, Schuemie MJ, Ramcharran D and Stang PE. Atypical Antipsychotics and the Risks of Acute Kidney Injury and Related Outcomes Among Older Adults: A Replication Analysis and an Evaluation of Adapted Confounding Control Strategies. Drugs & aging. 2017; 34: 211-9.

• Ramcharran D, Qiu H, Schuemie MJ, et al. Atypical Antipsychotics and the Risk of Falls and Fractures Among Older Adults: An Emulation Analysis and an Evaluation of Additional Confounding Control Strategies. J Clin Psychopharmacol. 2017; 37: 162-8.

• Boland MR, Parhi P, Li L, et al. Uncovering exposures responsible for birth season - disease effects: a global study. J Am Med Inform Assoc. 2017 Sep 28.

• Duke JD, Ryan PB, Suchard MA, et al. Risk of angioedema associated with levetiracetam compared with phenytoin: Findings of the observational health data sciences and informatics research network. Epilepsia. 2017; 58: e101-e6.

Methods research

• Schuemie MJ, Ryan PB, Pratt N, et al. Large-scale evidence generation and evaluation across a network of databases (LEGEND): assessing validity using hypertension as a case study. J Am Med Inform Assoc. 2020 Aug 1;27(8):1268-1277.

• Schuemie MJ, Cepeda MS, Suchard MA, et al. How Confident Are We About Observational Findings in Health Care: A Benchmark Study Harvard Data Science Review, 2(1).

• Schuemie MJ, Ryan PB, Man KKC, et al. A plea to stop using the case-control design in retrospective database studies. Stat Med. 2019 Sep 30;38(22):4199-4208.

• Reps JM, Rijnbeek PR, Ryan PB. Identifying the DEAD: Development and Validation of a Patient-Level Model to Predict Death Status in Population-Level Claims Data. Drug Saf. 2019 May 3.

• Reps JM, Schuemie MJ, Suchard MA, et al. Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data. J Am Med Inform Assoc. 2018;25(8):969-975.

• Schuemie MJ, Ryan PB, Hripcsak G, et al. Improving reproducibility by using high-throughput observational studies with empirical calibration. Philosophical transactions Series A, Mathematical, physical, and engineering sciences. 2018; 376.

• Tian Y, Schuemie MJ and Suchard MA. Evaluating large-scale propensity score performance through real-world and synthetic data experiments. International journal of epidemiology. 2018.

• Schuemie MJ, Hripcsak G, Ryan PB, et al. Empirical confidence interval calibration for population-level effect estimation studies in observational healthcare data. Proceedings of the National Academy of Sciences of the United States of America. 2018; 115: 2571-7.

• Schuemie MJ, Hripcsak G, Ryan PB, et al. Robust empirical calibration of p-values using observational data. Statistics in medicine. 2016; 35: 3883-8.

• Schuemie MJ, Ryan PB, DuMouchel W, et al. Interpreting observational studies: why empirical calibration is needed to correct p-values. Statistics in medicine. 2014; 33: 209-18.

ablack3/Hades documentation built on Oct. 31, 2020, 9:21 a.m.