WORK PACKAGE 7

Methodological tools using multi-criteria value methods for HTA decision-making

Objectives
  • To create an analytical framework explaining the determinants of HTA coverage recommendations from a comparative perspective

  • To generate a predictive model, and test it empirically, analysing the relative importance of different value dimensions across countries implementing HTA and across different therapeutic areas

  • To develop (a) a multidimensional value framework and (b) preference elicitation techniques to inform HTA decision-making processes relating to the evaluation of new medicines;

  • To test the multi-dimensional value framework in practice and assess its suitability for evaluating new medicines with HTA agencies and relevant stakeholders

  • To research the practical implementation of MCDA evaluations, by adapting these in alignment with responsibilities and remits currently in place across different HTA bodies 

Methodology
  • Use an analytical framework to identify critical determinants in the evidence submitted to HTA bodies; develop a parsimonious model that captures these determinants in a systematic way and conduct econometric analysis to identify their relevance and importance in shaping HTA recommendations

  • Structure sound multi-criteria evaluation models using participatory Delphi processes to collect views from relevant stakeholders; develop methods within a socio-technical framework for the evaluation of new medicines based on a common and transparent value metric, using both qualitative (e.g. MACBETH) and quantitative (e.g. swing weighting) approaches

  • Test empirically the proposed value framework in collaboration with three HTA bodies using a rotational design, and develop an actionable model for the evaluation of medicines across different disease areas

  • Explore multi-criteria portfolio decision models to inform resource allocation and value based pricing indices obtained through multi-variate regression analyses using MCDA scores as tools to assist HTA bodies

Outputs
  • Deliverable D7.1: An analytical framework outlining the determinants of HTA recommendations across settings based on primary data collection and primary analysis of secondary data (available January 2023)
     

  • Deliverable D7.2: Advancing knowledge and MCDA tools to assist HTA agencies in evaluating medicines on a common basis (available January 2023)
     

  • Deliverable D7.3: Testing the IMPACT-HTA Value Framework in collaboration with HTA agencies: Case studies on Non-Small Cell Lung Cancer and Spinal Muscular Atrophy (available January 2023)
     

  • Dataset on determinants of HTA recommendations (available January 2023) 
     

  • Dataset from the 1st Delphi process to HTA stakeholders, organized into 6 parallel panels (2 rounds), about “This aspect should be considered in the evaluation of new medicines on a common basis” (2019) (available January 2023)
     

  • Dataset from the 2nd Delphi process to HTA stakeholders, organized in a single panel (2 rounds), question “This aspect should be considered in the evaluation of new medicines on a common basis” (2019) (available January 2023)
     

  • Dataset from the Web-Delphi process to INAMI stakeholders (three rounds), about “The relevance of the following value aspects for the evaluation of new medicines in this disease context is” (2020) (available January 2023)
     

  • Dataset from the Web-Delphi process to TLV stakeholders (three rounds), about “The relevance of the following value aspects for the evaluation of new medicines in this disease context is” (2020) (available January 2023)

Publications

  • Oliveira MD, et al. (2019). Multi-criteria decision analysis for health technology assessment: addressing methodological challenges to improve the state of the art. The European Journal of Health Economics 20(6): 891–918. [https://link.springer.com/article/10.1007%2Fs10198-019-01052-3] 10.1007/s10198-019-01052-3

Leads

Instituto Superior Técnico

Engineering and Management Department

 

London School of Economics and Political Science

LSE Health

Research team

Monica Oliveira (PI)

Panos Kanavos (PI)

Carlos Bana

Aris Angelis

Erica Visintin

Klára Dimitrovová

Ana Vieira

Teresa Rodrigues

Liliana Freitas