Transparency and Validation

The only scientifically-validated rapid-cycle analytics platform for healthcare databases.

Aetion’s healthcare data scientists and a network of academic and industry partners test the Aetion Evidence Platform to ensure that decision-makers are always informed accurately and transparently.

Validation against published studies

A team at Harvard Medical School led by Shirley V. Wang published the largest-ever real-world data reproducibility study using the Aetion platform,[1] reproducing 32 published studies in two commercial databases and one EHR database. The Aetion Evidence Platform showed the flexibility to implement a wide range of study designs with high accuracy; an important secondary finding was that the transparency of current approaches that is sufficient for reproducing findings is often lacking. Funded by the John and Laura Arnold Foundation, Dr. Wang is embarking on an even larger validation study to define the field of healthcare database analytics: using the Platform to reproduce 250 published studies across commercial and Medicare claims databases and EHR databases.

Validation against FDA Sentinel

The FDA Sentinel system has established itself as a standard for decision-makers using healthcare database analytics. Through extensive testing, the Aetion Evidence Platform has demonstrated extremely high accuracy against a variety of FDA modular programs.

Validation against randomized trials

A collaborative team from the Brigham and Women’s Hospital and a Sponsor conducted a multi-database cardiovascular safety study as a post marketing commitment, and completed the study in a 6-month period. At the time of publication, an RCT was ongoing; when the trial was completed, it reaffirmed the exact results obtained using real-world evidence and the Aetion Evidence Platform.[2] Today, Aetion is embarking on a systematic reproduction of completed and ongoing RCTs using real world data in collaboration with healthcare data scientists from Harvard,.

Validation through teaching

Aetion works closely with academic partners in teaching database analytics. Annually, about 100 students take courses on database analytics for effectiveness research using the Aetion platform at Harvard and in Europe. Students in the course come from ministries of health, health plans, the biopharma industry and academia to implement their own projects using a variety of designs to evaluate the effectiveness of healthcare interventions and medical products.

Validation through leadership in transparency and reproducibility

Aetion co-founder Sebastian Schneeweiss is co-lead of the joint ISPE/ISPOR Task Force on transparency in database research in healthcare. The Task Force is creating consensus papers on how to ensure transparency for the process of designing and executing implementing database studies.

Validation of the MVET framework of RWE that is fit for decision making

A joint work group of NEWDIGS (MIT), European Medicines Agency, and IMI ADAPT SMART has develop a framework that helps evaluate whether real-world evidence is fit for decision making. The consensus document is supported by regulators (EMA, FDA), Health Technology Assessment agencies, payers, biopharma companies and academics.[3]

The four components of the so-called MVET framework -- meaningful evidence, valid evidence, expedited evidence and transparent evidence -- are the pillars of the Aetion platform.


[1] Wang SV, Verpillat P, Rassen JA, Patrick A, Garry EM, Bartels DB. Transparency and reproducibility of observational cohort studies using large healthcare databases. Clin Pharm Ther 2016;99:325-32

[2] 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 Feb 21. [Epub ahead of print]

[3] Schneeweiss S, Eichler H-G, Garcia-Altes A, Chinn C, Eggimann A-V, Garer S, Goettsch W, Lim R, Loebker W, Martin D, Mueller T, Park BJ, Platt R, Priddy S, Ruhl M, Spooner A, Vannieuwenhuyse, Willke RJ. Real world data in adaptive biomedical innovation: A framework for generating evidence fit for decision making. Clin Pharm Ther 2016;100:633-46