There rarely exists a one-size-fits-all solution. It is true socially, politically and it is certainly true in healthcare especially when deciding which drug to use to treat a condition in a given patient.
For years, randomized controlled trials (RCTs) have been considered the “gold standard” for drug development and approval. These studies are generally conducted within an environment that is very controlled, as they must be, in order to determine the efficacy and safety of a drug. While these designs are very effective in determining if a product works or is safe, as many have posited, questions central to improving health outcomes and guiding policy decisions post-approval typically can only be answered by assessing a therapy’s effectiveness in larger, more heterogeneous populations and, in the case of interventions for chronic conditions, over longer periods of time. This leads to an evidence chasm that can delay effective treatments for patients as decision-makers struggle to interpret and translate existing evidence.
Importantly, regulator sentiment toward utilizing real-world evidence within the drug development process has changed dramatically over the last decade as real-world data have been more available and methods for effectively and reliably utilizing these data to provide deeper insight into risks and benefits for individual patients and specific populations have matured. Regulators have made the most progress in leveraging these real-world data resources in the area of safety. More recently, they have turned their attention to effectiveness in an attempt to address this disconnect with post-approval evidentiary requirements.
All real-world designs have value when effectively fit to purpose. However, many see pragmatic trials (PTs) as powerful solutions that can be leveraged both post- and pre-approval. They are designed to extend scientific evaluation beyond the limits of the RCT research setting and into the realm of real-world medical practice. While non-interventional observational studies and registries generate useful information on real-world drug effects, they lack the randomization that can reduce bias.