Aetion

Science

Science At AETION

Aetion's science is based on trusted, peer-reviewed literature. Aetion scientists drive the field forward.


Academic Use of Aetion

The Aetion Evidence Platform is used by leading academics worldwide in their peer-reviewed research. The platform is also used in database analytics and comparative effectiveness courses taken by epidemiologists, outcomes researchers, health economists and biostatisticians. Courses are offered both at Harvard and in Europe.  

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Selected Publications Using the Aetion Evidence Platform

Below is a selection of peer-reviewed publications using Aetion Evidence Platform analytics.

Kim SC, Solomon DH, Rogers JR, Gale S, Klearman M, Sarsour K, Schneeweiss S. Cardiovascular Safety of Tocilizumab versus Tumor Necrosis Factor Inhibitors in Patients with Rheumatoid Arthritis – a Multi-database Cohort Study. Arthritis Rheumatol 2017 in press

Wang SV, Verpillat P, Rassen JA, Patrick A, Garry EM, Bartels DB. Transparency and Reproducibility of Observational Cohort Studies Using Large Healthcare Databases. Clinical Pharmacology & Therapeutics. 2016;99(3):325-332. doi:10.1002/cpt.329.

Coleman CI, Antz M, Bowrin K, et al. Real-world evidence of stroke prevention in patients with nonvalvular atrial fibrillation in the United States: the REVISIT-US study. Curr Med Res Opin. 2016;32(12):2047-2053. doi:10.1080/03007995.2016.1237937.

Nakasian S, Rassen JA, Franklin JM.  Effects of expanding the look-back period to all available data in the assessment of covariates.  PDS 2017; in press.

Simard EP, Ruehmkorf F, Hawes JCL, Verpillat P.  Can Rheumatoid Arthritis Associated With Interstitial Lung Disease (RA-ILD) Be Classified As An Orphan Disease In The U.S.?  31st ICPE, Boston, MA.


Selected Publications by the Aetion Team

Aetion team members publish frequently on real-world data methodological topics and applications. Below you will find a short list of publications; a full list is available here.

Schneeweiss S. A basic study design for expedited safety signal evaluation based on electronic healthcare data. Pharmaceopidemiol Drug Safety 2010;19:858-68.

Schneeweiss S. Learning from big healthcare data. N Engl J Med 2014;370:2161-3.

Schneeweiss S, Gagne JJ, Glynn RJ, Ruhl M, Rassen JA. Assessing the comparative effectiveness of newly marketed medications. Clin Pharm Ther 2011;90:777-90.

Schneeweiss S, Rassen JA, Glynn RJ, Avorn J, Mogun H, Brookhart MA. High-dimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology 2009;20:512-22. 

Rassen JA, Shelat AA, Myers J, Glynn RJ, Rothman KJ, Schneeweiss S. One-to-many propensity score matching in cohort studies. Pharmacoepidemiol Drug Saf 2012;21(S2):69-80.

Franklin JM, Rassen JA, Ackermann D, Bartels DB, Schneeweiss S. Metrics for covariate balance in cohort studies of causal effects: Varying associations with bias. Stat Med 2014;33:1685-99. 

Neugebauer R, Schmittdiel JA, Zhu Z, Rassen JA, Seeger JD, Schneeweiss S. High-dimensional propensity score algorithm in comparative effectiveness research with time-varying interventions. Stat Med 2014;34:753-81.

Schneeweiss S, Eddings W, Glynn RJ, Patorno E, Rassen JA, Franklin JM. Variable selection for confounding adjustment in high-dimensional covariate spaces when analyzing healthcare databases. Epidemiology 2017;28:237-48.

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