Special Interest Groups - Real-World Evidence and Big Data Group

Background

Big data are defined as “extremely large datasets that may be complex, multi-dimensional, unstructured heterogeneous and which are accumulating rapidly and may be analyzed computationally to reveal patterns, trends, and associations”¹.

A key source of big data is real-world data (RWD), defined as the data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources (e.g. claims databases, electronic medical records, patients and disease registries) during routine clinical practice². The evidence generated by these data, defined as real-world evidence (RWE), has become increasingly important in the regulatory field, to generate evidence in both the pre- and post-marketing setting as well as in the healthcare, clinical and scientific fields. Accordingly, regulatory agencies are delivering guidelines about how to integrate RWE in the process of approval of new medications.

In recent years, several large-scale distributed database networks have been set-up, for which RWD fits the definition of big data. As an example, Sentinel and, more recently, DARWIN-EU®, were launched by the Food and Drug Administration and the European Medicines Agency, respectively, with the main aim of improving the evaluation of the use, safety and effectiveness of medicinal products by combining a large number of healthcare databases. These networks will be increasingly used for all signal management processes, mostly strengthening and validation; however, several initiatives aimed at exploring the role of RWD to complement Spontaneous Reporting System (SRS) in the context of signal detection are also ongoing.

Beyond identification and confirmation of previously unknown safety signals, in addition, RWD could have the highest impact on addressing other PV related questions concerning as an example medication errors, irrational use, and counterfeit medicines.

Traditionally, debate about methodological aspects of Big Data and RWE with respect to post-marketing surveillance of medicines mostly pertain to the International Society for Pharmacoepidemiology (ISPE), while ISoP is mainly an SRS-based community. Having this SIG may help in better understanding which role RWE and Big Data can play in the context of pharmacovigilance and how they can complement SRS. Furthermore, this SIG may help educate ISoP community on topics that will increasingly affect pharmacovigilance routine activities.

Aim

The overall aim of the Real-World Evidence and Big Data SIG is to provide an opportunity for ISoP members to share, provide and exchange information on relevant issues concerning RWE/Big data in pharmacovigilance, especially innovative approaches and new methods for collecting, identifying and analyzing large safety-related data, as well as to support the liaison between pharmacovigilance and pharmacoepidemiology.

Objectives

The key objectives are:

1. To organize introductory pre-conference course on pharmacoepidemiology as well as symposia on RWE/big data in pharmacovigilance at ISoP annual conferences, especially providing updates on the activities of the above-mentioned large scale regulatory-driven networks;

2. To develop methodological papers on how SRS and RWE should complement with each other and more in general on the role of RWE/big data in all the stages of signal management process, from signal validation to signal strengthening and detection. A mapping exercise of all the ongoing initiatives concerning signal management using Big Data could be a more immediate goal of this SIG;

3. To facilitate liaison and establish a more solid collaboration between ISoP and International Society for Pharmacoepidemiology (ISPE) as well as with EMA’s European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP), the Institute for Safe Medication Practices and other key international programs (e.g., ECAMET) in order to promote joint activities;

4. To support other ISoPs’ SIG such as Medication Error, Risk Minimization measures for Asian countries and others, regarding educational and research initiatives concerning RWE and Big data.

References
1. https://www.ema.europa.eu/en/about-us/how-we-work/big-data
2. Cave, A., Kurz, X. and Arlett, P. (2019), Real-World Data for Regulatory Decision Making: Challenges and Possible Solutions for Europe. Clin. Pharmacol. Ther., 106: 36-39. https://doi.org/10.1002/cpt.1426