It is widely acknowledged that real-world data (RWD) has the potential to unlock value for researchers and organizations seeking to bring more effective, affordable and accessible health solutions to patients in need. However, as is often the case with new paradigms, the evolving structures and provenance of RWD can make its analysis quite complex and challenging.
More than 80 percent of pharmaceutical companies surveyed in a recent market analysis indicated that they were entering into strategic partnerships to access new sources of RWD. This surge in sources, along with the potential for inconsistencies and variability in what are thought to be the best methodologies to apply to RWD, highlights an equally growing need to establish trust and transparency in analytical methods, insights, and conclusions drawn from studies that use RWD.
Key thought leaders from groups like The International Society for Pharmacoeconomics and Outcomes Research (ISPOR) have stated that the “growing interest ([n RWD] has created an urgency to develop processes that promote trust in the evidence-generation process and to enable decision-makers to evaluate the quality of the methods used in real-world studies.”
Trust in real-world evidence (RWE) usually relates to concepts like pre-specification of an analytic plan but additionally, that trust should extend to detailed discussions of RWD source characteristics and data quality. Going further, we can also build trust in RWE with transparency into analytic method implementation for a particular study, as well as the ability to reproduce that study.