The first randomized clinical trial testing a curative treatment was performed in England in 1946 by the Medical Research Council on the antibiotic drug streptomycin. In that era, striking examples of non-interventional studies had also been revealed in metabolic disorders, cancer, and rheumatic fever (Dawber, Meadors and Moore, 1951).
Driven by the evolution of ethics, safety and regulatory requirements, major steps, such as the Helsinki Declaration, the creation of Good Clinical Practices / Good Pharmacovigilance Practices, and the International Conference on Harmonization (1996) led to a high level of standardization for clinical trials. But real world data studies aiming at generating real world evidence also need a higher level of standardization. Even in the absence of intervention or randomization of participants, real world evidence needs to rely on harmonized good practices.
A striking example of Real World Evidence in Cardiovascular Disease
Cardiovascular disease (CVD) is the leading cause of death and serious illness in the United States. This was the case as far back as the 1950s when the associated risk factors of CVD were almost unknown. In 1948, the Framingham Heart Study was setup on behalf of the National Heart Institute. The objective of this study was to identify the risk factors of CVD by following more than 5000 people, who had not yet developed of CVD, over an extended period of time. In 1971, the study enrolled the second generation of the original participants' adult children (and their spouses) to take part in a similar follow-up. In 1994, the need to reflect a more diverse community led to the first Omni Cohort of the Framingham Heart Study. In April 2002, the study entered a new phase, the enrollment of a third generation of participants, and in 2003, a second group of Omni participants was enrolled.
Over time, the study became the subject of more than 1,000 articles in leading medical journals. It wasn’t until 2000 when the concept of CVD risk factors was progressively included in the modern medical era, ultimately leading to the development of effective treatments.
The State of Art and the Need for Harmonization
Major advances in RWE were made by the European Network of Centers for Pharmacoepidemiology and Pharmacovigilance (ENCePP) when it developed its Guide on Methodological Standards in Pharmacoepidemiology. It included explanations on primary data collection versus secondary data use, statistical and epidemiological methods, and study designs. The International Society for Pharmacoepidemiology (ISPE) produced a guideline for Good Pharmacoepidemiology Practices (GPP), which addresses the importance of formal steps in the evidence generation process and adverse event reporting.
Regulatory authorities also developed some guidance – the FDA draft guidance on the “Use of Real-World Evidence to Support Regulatory Decision Making for Medical Devices,” and the recent EMA “Guidance for companies considering the adaptive pathways approach.”
Real world data can address several questions such as:
- The compatibility of clinical trials in a real world setting
- The burden of illness/patient reported outcomes in the real world
- The long-term effects of the use of personalized medicines
The rising interest from different stakeholders on these topics may lead to both common and specific assessment criteria. Ultimately, this will necessitate the need for harmonization in terminology and a robust RWE generation practice in project planning, designing, conducting, and reporting.
Electronic Health Records and Big Data: Risks and Opportunities in the Evidence Generation Process
Over time, the rise of electronic health records (EHR) and big data has had the ability to inform decision makers. However, their use can be considered both a risk and an opportunity.
On the one hand, the increase in the amount of hospital data, administrative systems and registries can be viewed as a potentially unrivalled source of information. Neglecting them may lead to unawareness as well as poor decision making. On the other hand, when data are used with a scientific objective completely different from the reason why the database was created, it can lead to various kinds of misinterpretation. This is why it is crucial people with adequate skills operate with full knowledge of the database. Furthermore, different conclusions should not be drawn when addressed by two different teams operating on the same database! Secondly, the choice of the database and its representation should be considered and its biases addressed properly.
The quality of data should also modulate the level of evidence produced. In all situations, the research should follow fundamental high standards of evidence generation from setting the scientific question to reporting and publishing. Additionally, the level of independence for each role should be always very clear.
Recent Advances and Perspectives
Some clinical research networks, such as the EU Innovative Medicines Health Initiatives (IMI), the European Medical Information Network (EMIF), and the Electronic Health Records for Clinical Research (EHR4CR) are key players in determining the place of real world data. Such programs could provide support for harmonizing the rules and guidance for real world-based evidence generation.
- Dawber TR, Meadors GF, Moore FE, Epidemiological Approaches to Heart Disease: The Framingham Study. Am J Public Health Nations Health 1951; 41:279-286.
- Crofton J, The MRC randomized trial of streptomycin and its legacy: a view from the clinical front line. J.R. Soc. Med. 2006; 99(10):531-534.
- International Society for Pharmacoepidemiology. Guidelines for Good Pharmacoepidemiology (GPP) Rev June 2015. https://www.pharmacoepi.org/resources/guidelines_08027.cfm
- The European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP). Guide on Methodological Standards in Pharmacoepidemiology (Revision 3). EMA/95098/2010.http://www.encepp.eu/standards_and_guidances
- Draft Guidance for Industry and Food and Drug Administration Staff .Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices. Rev. Sept 2016.
- Guidance for companies considering the adaptive pathways approach.EMA/527726/2016.