ISPOR 2022: Plenary Session 3 – Innovative Methods for Integrating Data Across Outcomes and Borders



Today we sat into the final plenary session of ISPOR Europe 2022 which was a discussion on innovative methods for integrating data across outcomes and boarders. The discussions were focused on how real-world data can be used for decision making at both a regulatory and HTA level and how we can optimise the methods for data collection to make it most beneficial across the board. This is of increasing importance as the amount of real-world data continues to expand in volume, granularity, and heterogeneity.

The Talking Points

Though it was stressed that randomised control trials remain the gold standard of evidence for understanding the safety and efficacy of any new product, the panel discussed the ideal methods to expand the evidence base for a product via real world evidence. This type of data can be valuable when randomised control trials cannot be performed, they do not cover a sufficiently long-time horizon or if they have used surrogate endpoints. However, for this evidence to be valuable and usable for decision makers there must be processes for integrating data from numerous countries and sources as well as implementing views of what makes good evidence from multiple stakeholders.

We heard about this issue from a regulatory viewpoint, by Peter Arlett of the EMA, where it was the opinion that a research question should be used to guide the evidence requirements but that we must embrace the whole spectrum of evidence methods. For real world evidence there should be a high level of transparency so that trust can be built to enable good data sharing platforms and that the patient voice must be used to guide the types of data being collected. The DARWIN EU programme was brought up again in this plenary session (see our overview of ISPOR plenary session 2 for further information), as a means to proficiently collect real world data in a way that can be scaled up to be analysed across countries to support in decision making at both the regulatory and the HTA level.

From Shahid Hanif of the GetReal Institute we heard about the European Health Data Scape that is being developed to get health data to be more effectively used across Europe. It aim to do this by using both primary data, where patients have access to their own data in different countries if the move or are just visiting, and secondary data which aims to facilitate innovation and enable better policy making. With secondary data the aim is to have a catalogue of different data sources brought together that could be used to support decision making by helping with epidemiology assessments, looking at predictions and regional effects and linking treatments to outcomes. The European Health Data Scape is calling for strong involvement into it’s development from a wide range of stakeholders- including a need for citizen and patient representation to build trust (a theme also brought up in Peter Atlett’s talk).

The final talk was from Beate Jahn from UMIT who explained the public health trade-offs between incremental benefits costs in health economics. Using the models shown to the audience, the importance of having as much data as possible to build the comprehensive incremental cost harm ratio was explained as it gave out the best predictions of effectiveness. This type of model differs from an aggregated framework, such as when calculating QALYs, as they are disaggregated and can capture a higher granularity (including things such as short-term effects like anxiety over treatment) and therefore better inform decision making. Many of the trade-offs incorporated into these models would typically not be collected in a randomised control trial.

3 Takeaways

  1. A plethora of data must be used to inform on both regulatory and HTA decision making including both randomised control trials and real-world evidence
  2. Collaboration and commitment are required to from multiple stake holders to ensure that evidence is collected in a way that it can be valuable across many causes
  3. When data is collected the research question must be considered so that the relevant benefits and trade-offs can be collected for the stake holders

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