In this digital age, all stakeholders seek information about the healthcare products they approve, prescribe, use or pay for. Real World Evidence (RWE) and Health Economics and Outcomes Research (HEOR) are essential to generate the required information which in turn will help address the challenges of access and pricing. There’s a strong realization that it’s optimal to incorporate outcomes and pharmaco-economic data reflective of the benefits to the patients in the real world into the clinical development stage.
A primary function of HEOR is to link clinical trials to the real world and help project the value of interventions. Thus knowledge and experience in planning and conduct of clinical trials is synergistic to expertise in HEOR. With lines between clinical development and post-approval research fast blurring, including the kind of designs used, the type of analysis done, the type of skill set required is also overlapping to a great extent. Clinical studies increasingly capture outcomes data (PRO data increasingly required by regulators) and post-approval real world studies increasingly require safety and efficacy/effectiveness to be measured in the real world. Of course, there are certain types of designs and analyses pertinent to each stage which require niche expertise and experience, but there’s also a lot common between the two stages.
Value can be a broad term, with different connotations at different stages of the product life-cycle. It’s first focused on efficacy, then on effectiveness and then on using available healthcare resources optimally and efficiently throughout the life-cycle. The strategy to demonstrate value across stages has to be systematically incorporated into the clinical development plan. The thought process often has to begin during the pre-clinical stage to understand the epidemiology, disease patterns, target population etc. A good CDP will have aspects of RWE, HEOR and MA built into each phase of clinical trials.
While it has been realized for some time now that HEOR is an integral part of clinical R&D & it impacts clinical trial design, the big data era is expected to take this integration of HEOR into clinical R&D to a different level. This is being referred to as Precision HEOR. Big data analytics is increasingly used to identify disease biomarkers for drug discovery for development of personalized medicine. Incorporating HEOR at this early stage will help differentiated market access strategies corresponding to different patient populations, and this in turn will also feed information back that will help further drug discovery and development.
While outsourcing RWE, HEOR and MA related activities, especially analytics and writing which tends to be more amenable to outsourcing, there’s a distinct benefit in using a group that has strong experience in providing these services for clinical trials, along with an understanding of how the orientation and the objectives of HEOR studies are different, and an exposure to real world data (e.g., claims data, EHR). In the future world of precision HEOR, it will be even more important to have a blend of strong analytical and writing skills in traditional clinical development, along with experience in RWE and HEOR. A group that has a combination of these skill sets can most effectively implement precision HEOR strategy. In the sponsor organization, this can be achieved through close collaboration between clinical development and HEOR teams, which tend to work in silos even today, so that cross-pollination of knowledge will occur. With respect to a service provider, when HEOR analytics and writing is outsourced, it’s important to work with a team that collectively has strong experience across development and RWE/HEOR.