Harvard Catalyst Profiles

Contact, publication, and social network information about Harvard faculty and fellows.

Sebastian Schneeweiss, Sc.D., M.D.

Co-Author

This page shows the publications co-authored by Sebastian Schneeweiss and Jessica Franklin.
Connection Strength

14.407
  1. Real-World Evidence for Assessing Pharmaceutical Treatments in the Context of COVID-19. Clin Pharmacol Ther. 2021 Feb 02.
    View in: PubMed
    Score: 0.988
  2. 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.
    View in: PubMed
    Score: 0.980
  3. Emulation Differences vs. Biases When Calibrating Real-World Evidence Findings Against Randomized Controlled Trials. Clin Pharmacol Ther. 2020 04; 107(4):735-737.
    View in: PubMed
    Score: 0.924
  4. 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.
    View in: PubMed
    Score: 0.905
  5. Evaluating the Use of Nonrandomized Real-World Data Analyses for Regulatory Decision Making. Clin Pharmacol Ther. 2019 04; 105(4):867-877.
    View in: PubMed
    Score: 0.864
  6. Variation in adherence to medications across the healthcare system in two comparative effectiveness research cohorts. J Comp Eff Res. 2017 Oct; 6(7):613-625.
    View in: PubMed
    Score: 0.786
  7. When and How Can Real World Data Analyses Substitute for Randomized Controlled Trials? Clin Pharmacol Ther. 2017 Dec; 102(6):924-933.
    View in: PubMed
    Score: 0.783
  8. Variable Selection for Confounding Adjustment in High-dimensional Covariate Spaces When Analyzing Healthcare Databases. Epidemiology. 2017 03; 28(2):237-248.
    View in: PubMed
    Score: 0.753
  9. Comparing the performance of propensity score methods in healthcare database studies with rare outcomes. Stat Med. 2017 05 30; 36(12):1946-1963.
    View in: PubMed
    Score: 0.751
  10. 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.
    View in: PubMed
    Score: 0.675
  11. Prospective cohort studies of newly marketed medications: using covariate data to inform the design of large-scale studies. Epidemiology. 2014 Jan; 25(1):126-33.
    View in: PubMed
    Score: 0.605
  12. Metrics for covariate balance in cohort studies of causal effects. Stat Med. 2014 May 10; 33(10):1685-99.
    View in: PubMed
    Score: 0.602
  13. 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.
    View in: PubMed
    Score: 0.280
  14. One-to-many propensity score matching in cohort studies. Pharmacoepidemiol Drug Saf. 2012 May; 21 Suppl 2:69-80.
    View in: PubMed
    Score: 0.269
  15. Identifying Risk Factors for Diabetic Ketoacidosis Associated with SGLT2 Inhibitors: a Nationwide Cohort Study in the USA. J Gen Intern Med. 2021 Feb 09.
    View in: PubMed
    Score: 0.247
  16. Using Healthcare Databases to Replicate Trial Findings for Supplemental Indications: Adalimumab in Patients with Ulcerative Colitis. Clin Pharmacol Ther. 2020 Oct; 108(4):874-884.
    View in: PubMed
    Score: 0.235
  17. The EMPagliflozin compaRative effectIveness and SafEty (EMPRISE) study programme: Design and exposure accrual for an evaluation of empagliflozin in routine clinical care. Endocrinol Diabetes Metab. 2020 Jan; 3(1):e00103.
    View in: PubMed
    Score: 0.228
  18. Using Real-World Data to Predict Findings of an Ongoing Phase IV Cardiovascular Outcome Trial: Cardiovascular Safety of Linagliptin Versus Glimepiride. Diabetes Care. 2019 12; 42(12):2204-2210.
    View in: PubMed
    Score: 0.221
  19. Empagliflozin and the Risk of Heart Failure Hospitalization in Routine Clinical Care. Circulation. 2019 06 18; 139(25):2822-2830.
    View in: PubMed
    Score: 0.218
  20. Sequential Monitoring of the Comparative Effectiveness and Safety of Dabigatran in Routine Care. Circ Cardiovasc Qual Outcomes. 2019 02; 12(2):e005173.
    View in: PubMed
    Score: 0.215
  21. Claims Data Studies of Direct Oral Anticoagulants Can Achieve Balance in Important Clinical Parameters Only Observable in Electronic Health Records. Clin Pharmacol Ther. 2019 04; 105(4):979-993.
    View in: PubMed
    Score: 0.212
  22. Claims-based studies of oral glucose-lowering medications can achieve balance in critical clinical variables only observed in electronic health records. Diabetes Obes Metab. 2018 04; 20(4):974-984.
    View in: PubMed
    Score: 0.200
  23. Using Super Learner Prediction Modeling to Improve High-dimensional Propensity Score Estimation. Epidemiology. 2018 01; 29(1):96-106.
    View in: PubMed
    Score: 0.199
  24. Assessment of Confounders in Comparative Effectiveness Studies From Secondary Databases. Am J Epidemiol. 2017 03 15; 185(6):474-478.
    View in: PubMed
    Score: 0.189
  25. Incorporating linked healthcare claims to improve confounding control in a study of in-hospital medication use. Drug Saf. 2015 Jun; 38(6):589-600.
    View in: PubMed
    Score: 0.167
  26. Evaluating possible confounding by prescriber in comparative effectiveness research. Epidemiology. 2015 Mar; 26(2):238-41.
    View in: PubMed
    Score: 0.164
  27. Risk of venous thromboembolism in patients with rheumatoid arthritis: initiating disease-modifying antirheumatic drugs. Am J Med. 2015 May; 128(5):539.e7-17.
    View in: PubMed
    Score: 0.162
  28. Equity in the receipt of oseltamivir in the United States during the H1N1 pandemic. Am J Public Health. 2014 Jun; 104(6):1052-8.
    View in: PubMed
    Score: 0.154
  29. Plasmode simulation for the evaluation of pharmacoepidemiologic methods in complex healthcare databases. Comput Stat Data Anal. 2014 Apr; 72:219-226.
    View in: PubMed
    Score: 0.154
  30. Instrumental variable applications using nursing home prescribing preferences in comparative effectiveness research. Pharmacoepidemiol Drug Saf. 2014 Aug; 23(8):830-8.
    View in: PubMed
    Score: 0.154
  31. Clinical and health care use characteristics of patients newly starting allopurinol, febuxostat, and colchicine for the treatment of gout. Arthritis Care Res (Hoboken). 2013 Dec; 65(12):2008-14.
    View in: PubMed
    Score: 0.150
  32. 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.
    View in: PubMed
    Score: 0.147
  33. Matching by propensity score in cohort studies with three treatment groups. Epidemiology. 2013 May; 24(3):401-9.
    View in: PubMed
    Score: 0.144
  34. No differences in cancer screening rates in patients with rheumatoid arthritis compared to the general population. Arthritis Rheum. 2012 Oct; 64(10):3076-82.
    View in: PubMed
    Score: 0.069
  35. Effects of adjusting for instrumental variables on bias and precision of effect estimates. Am J Epidemiol. 2011 Dec 01; 174(11):1213-22.
    View in: PubMed
    Score: 0.065
  36. Use of Time-Dependent Propensity Scores to Adjust Hazard Ratio Estimates in Cohort Studies with Differential Depletion of Susceptibles. Epidemiology. 2020 01; 31(1):82-89.
    View in: PubMed
    Score: 0.057
  37. Evaluation of Socioeconomic Status Indicators for Confounding Adjustment in Observational Studies of Medication Use. Clin Pharmacol Ther. 2019 06; 105(6):1513-1521.
    View in: PubMed
    Score: 0.054
  38. Propensity score prediction for electronic healthcare databases using Super Learner and High-dimensional Propensity Score Methods. J Appl Stat. 2019; 46(12):2216-2236.
    View in: PubMed
    Score: 0.054
  39. 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.
    View in: PubMed
    Score: 0.050
  40. Collaborative-controlled LASSO for constructing propensity score-based estimators in high-dimensional data. Stat Methods Med Res. 2019 04; 28(4):1044-1063.
    View in: PubMed
    Score: 0.050
  41. Scalable collaborative targeted learning for high-dimensional data. Stat Methods Med Res. 2019 02; 28(2):532-554.
    View in: PubMed
    Score: 0.049
  42. 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.
    View in: PubMed
    Score: 0.045
  43. Selective Serotonin Reuptake Inhibitor Use and Perioperative Bleeding and Mortality in Patients Undergoing Coronary Artery Bypass Grafting: A Cohort Study. Drug Saf. 2015 Nov; 38(11):1075-82.
    View in: PubMed
    Score: 0.043
  44. Antipsychotics and mortality: adjusting for mortality risk scores to address confounding by terminal illness. J Am Geriatr Soc. 2015 Mar; 63(3):516-23.
    View in: PubMed
    Score: 0.041
  45. Type of stress ulcer prophylaxis and risk of nosocomial pneumonia in cardiac surgical patients: cohort study. BMJ. 2013 Sep 19; 347:f5416.
    View in: PubMed
    Score: 0.037
  46. Adjuvant vancomycin for antibiotic prophylaxis and risk of Clostridium difficile infection after coronary artery bypass graft surgery. J Thorac Cardiovasc Surg. 2013 Aug; 146(2):472-8.
    View in: PubMed
    Score: 0.036
  47. Comparative effectiveness of preventative therapy for venous thromboembolism after coronary artery bypass graft surgery. Circ Cardiovasc Interv. 2012 Aug 01; 5(4):590-6.
    View in: PubMed
    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.
Funded by the NIH National Center for Advancing Translational Sciences through its Clinical and Translational Science Awards Program, grant number UL1TR002541.