Event Details
Overview
Over the past years, areas of clinical development and patient access / health technology assessment (HTA) have moved closer together. However, both areas differ in how they approach their respective research or policy questions. While the estimand framework is being the focus in clinical development, HTA is being viewed through the lens of the PICO framework. This might lead to mutual misunderstandings and ultimately prevent timely patient access due to evidence gaps. Additionally, the Target Trial Emulation framework has become popular in the Real-World Data setting to translate research questions into a meaningful design, and share some commonalities with the estimand and PICO frameworks.
For randomized controlled trials (RCTs) in the regulatory setting, the estimand framework has been introduced and stimulated by the publication of the International Council of Harmonisation (ICH) E9 (R1) Addendum [1]. It is recognized by regulatory agencies such as EMA and the Food and Drug Administration (FDA). According to ICH guidance, an estimand is defined on the basis of five attributes: (1) treatment(s); (2) target population; (3) clinical outcome of interest; (4) population-level summary effect measure; and (5) strategy for intercurrent (post-randomization) events.
In HTA, the PICO framework is typically used to translate policy questions into research questions. PICOs consist of five components: (1) population; (2) intervention; (3) comparator(s); and (4) outcome. During the reimbursement process, the manufacturer typically submits an evidence dossier that addresses the research questions(s) included in the scope. Best-practice guidelines recommend specifying relevant PICO question(s) in the HTA scoping process [2]. In evidence synthesis, PICO questions are often formulated prior to the analysis in order to guide the data extraction required for systematic literature reviews [3].
The Target Trial Emulation (TTE), developed by Hernan et al. (2016) [3], is a framework proposed for Real-World Data studies to minimize common biases due to selection or confounding. TTE is a two-step process, whereby
a protocol for an hypothetical RCT that would answer the question of interest is developed;
This protocol is applied to the Real-World Data so that it mimics the data that would have been gathered for the RCT [5].
In this framework, eligibility criteria, treatment strategies, assignment procedures, follow-up period, outcome, causal contrasts of interest and analysis plan should be specified. Gomes, Latimer, Soares et al., in [4], discusses opportunities and challenges of using TTE with Real-World-Data in the HTA context.
In this webinar series, we will compare and contrast all three frameworks and discuss how they may complement each other, also in light of the new EU HTA regulation.
This webinar brings together Antonio Remiro-Azocar, Robert Hemmings and Nicholas Latimer, who are renowned experts in the respective fields. They will provide an introduction to these three concepts, illustrated with examples, thus enabling a solid understanding of these frameworks as well as their communalities and differences. A Q&A session will provide opportunities for the audience to engage with the speakers and clarify any questions.
[1] https://pubmed.ncbi.nlm.nih.gov/26908545/
[3] https://pubmed.ncbi.nlm.nih.gov/17573961/
[4] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4832051/
[5] https://link.springer.com/article/10.1007/s40273-022-01141-x
Who is this event intended for? Regulatory and HTA statisticians. Anybody interested in the new EU HTA regulation.
What is the benefit of attending? Learn about the multiple frameworks used to define a research question and the context in which they are used. Discuss how they may complement each other in light of the new EU HTA regulation.
Timings
13:00-14:30 GMT | 14:00-15:30
