Robert James Glynn, Sc.D., Ph.D.
Co-Author
This page shows the publications co-authored by Robert Glynn and Jessica Franklin.
Connection Strength
3.211
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Evaluating possible confounding by prescriber in comparative effectiveness research. Epidemiology. 2015 Mar; 26(2):238-41.
Score: 0.626
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Effects of adjusting for instrumental variables on bias and precision of effect estimates. Am J Epidemiol. 2011 Dec 01; 174(11):1213-22.
Score: 0.248
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Real-World Evidence for Assessing Pharmaceutical Treatments in the Context of COVID-19. Clin Pharmacol Ther. 2021 04; 109(4):816-828.
Score: 0.237
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Emulating Randomized Clinical Trials With Nonrandomized Real-World Evidence Studies: First Results From the RCT DUPLICATE Initiative. Circulation. 2021 03 09; 143(10):1002-1013.
Score: 0.234
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Emulation Differences vs. Biases When Calibrating Real-World Evidence Findings Against Randomized Controlled Trials. Clin Pharmacol Ther. 2020 04; 107(4):735-737.
Score: 0.220
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Nonrandomized Real-World Evidence to Support Regulatory Decision Making: Process for a Randomized Trial Replication Project. Clin Pharmacol Ther. 2020 04; 107(4):817-826.
Score: 0.216
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Multinomial Extension of Propensity Score Trimming Methods: A Simulation Study. Am J Epidemiol. 2019 03 01; 188(3):609-616.
Score: 0.206
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Evaluating the Use of Nonrandomized Real-World Data Analyses for Regulatory Decision Making. Clin Pharmacol Ther. 2019 04; 105(4):867-877.
Score: 0.206
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Matching Weights to Simultaneously Compare Three Treatment Groups: Comparison to Three-way Matching. Epidemiology. 2017 05; 28(3):387-395.
Score: 0.182
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Variable Selection for Confounding Adjustment in High-dimensional Covariate Spaces When Analyzing Healthcare Databases. Epidemiology. 2017 03; 28(2):237-248.
Score: 0.180
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Regularized Regression Versus the High-Dimensional Propensity Score for Confounding Adjustment in Secondary Database Analyses. Am J Epidemiol. 2015 Oct 01; 182(7):651-9.
Score: 0.161
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Supplementing claims data with outpatient laboratory test results to improve confounding adjustment in effectiveness studies of lipid-lowering treatments. BMC Med Res Methodol. 2012 Nov 26; 12:180.
Score: 0.067
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A randomized study of how physicians interpret research funding disclosures. N Engl J Med. 2012 Sep 20; 367(12):1119-27.
Score: 0.066
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One-to-many propensity score matching in cohort studies. Pharmacoepidemiol Drug Saf. 2012 May; 21 Suppl 2:69-80.
Score: 0.064
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Using Healthcare Databases to Replicate Trial Findings for Supplemental Indications: Adalimumab in Patients with Ulcerative Colitis. Clin Pharmacol Ther. 2020 10; 108(4):874-884.
Score: 0.056
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Generalized boosted modeling to identify subgroups where effect of dabigatran versus warfarin may differ: An observational cohort study of patients with atrial fibrillation. Pharmacoepidemiol Drug Saf. 2018 04; 27(4):383-390.
Score: 0.048
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Association Between Patient-Centered Medical Homes and Adherence to Chronic Disease Medications: A Cohort Study. Ann Intern Med. 2017 Jan 17; 166(2):81-88.
Score: 0.044
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Prediction of rates of thromboembolic and major bleeding outcomes with dabigatran or warfarin among patients with atrial fibrillation: new initiator cohort study. BMJ. 2016 May 24; 353:i2607.
Score: 0.043
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Risk of venous thromboembolism in patients with rheumatoid arthritis: initiating disease-modifying antirheumatic drugs. Am J Med. 2015 May; 128(5):539.e7-17.
Score: 0.039
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Sequential value-of-information assessment for prospective drug safety monitoring using claims databases: the comparative safety of prasugrel v. clopidogrel. Med Decis Making. 2013 10; 33(7):949-60.
Score: 0.035
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Matching by propensity score in cohort studies with three treatment groups. Epidemiology. 2013 May; 24(3):401-9.
Score: 0.034
Connection Strength
The connection strength for co-authors is the sum of the scores for each of their shared publications.
Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.