As has been the case at previous ISPOR conferences, several sessions centred on the complex HEOR challenges in rare diseases stemming from issues such as limited data availability. This year, perhaps surprisingly, there appeared to be a smaller number of sessions focussing on rare diseases. However, many of the challenges inherent to rare populations were discussed during other ‘non-rare specific’ sessions. Since most rare diseases are severe, and indeed life-threatening in many cases, the discussions on new methods to account for disease severity in cost-effectiveness analyses were particularly relevant. These methods include NICE’s severity modifier and the GRACE framework (see Analytical Methods & Technical Topics). As many gene therapies are being developed for rare diseases, debates at the conference around the different approaches for gene therapy commercialisation, learning from real-life examples, were also of interest (see Revisions to Medicaid Best Price Rule).
One session of particular relevance to rare diseases was on structured expert elicitation (SEE). SEE is a formal exercise used to generate data from a group of experts, often when data is otherwise lacking or is inappropriate, which is frequently the case in rare diseases. It is a technique we have used at Costello Medical for the purposes of HTA, to generate additional evidence and thereby reduce uncertainty for healthcare decision-makers. Indeed, as of June 2021, one study found SEE methodology to have been used in 40 HTA submissions, indicating its acceptance as an evidence generation method by HTA bodies.1 Whilst the acceptance of SEE is good news for rare disease manufacturers who are often faced with a lack of more traditional data types, it is associated with several challenges. These include risk of bias stemming from the choice of experts and the time-consuming nature of the exercise. However, there has been little guidance from healthcare decision-makers about how SEE techniques should be used to inform their decision-making. Helpfully, the Centre for Health Economics at the University of York has led the development of several resources and tools to facilitate SEE exercises (Figure 1). Ultimately, it is likely that manufacturers will still need to invest substantial resources into deciding the best SEE methodology to use. However, as SEE exercises become more standardised and HTA bodies issue further guidance on their preferred methodology, they should become easier to conduct in the future.
Discussion at the conference on the use of appropriate patient-reported outcome (PRO) instruments was also of interest, since it is now widely accepted that disease-specific tools are often more appropriate for measuring PROs in rare diseases, compared with generic tools such as the EQ-5D. The choice of PRO instrument is particularly important because the generated data can influence regulatory and reimbursement decisions. Several initiatives and guidance documents now exist to aid stakeholders in the choice of PRO instrument. The Patient-Reported Outcomes Tools: Engaging Users and Stakeholders (PROTEUS) consortium (funded by a not-for-profit organisation and the pharmaceutical industry) promote rigorous PRO methods by partnering with stakeholders to disseminate and implement guidance documents on PRO methods. Another example is the guidance released by the FDA’s Oncology Centre of Excellence to improve the collection of PROs in oncology, including for rare cancers. Given the increasing use of disease-specific PRO tools, these resources should help stakeholders decide, early on, the most appropriate PRO instrument(s) to be used. For manufacturers looking to use PRO instruments to support their case, stakeholder engagement will be crucial to ensure all relevant regulatory and reimbursement bodies are in agreement about the choice of PRO instrument, especially where guidance is limited and continues to evolve, to ensure manufacturers are not caught out when justifying their choice of instrument at submission.
Kate Hanman, Head of Rare Diseases – Gene Therapies Lead (LinkedIn)