An Inclusive Civil Society Dialogue for Successful Implementation of the EU HTA Regulation: Call to Action to Ensure Appropriate Involvement of Stakeholders and Collaborators.

OBJECTIVES: Stakeholder involvement has long been considered a success factor for a joint European health technology assessment (HTA) process, and its relevance is now anchored in the EU HTA Regulation's (EU HTAR) legislative wording. Therefore, we aimed to explore the roles, challenges, and most important activities to increase the level of involvement per stakeholder group. METHODS: At the 2022 Fall Convention of the European Access Academy (EAA), working groups addressed the involvement of patients, clinicians, regulators, health technology developers (HTD), and national HTA bodies and payers within the EU HTA process. Each working group revisited the pre-convention survey results, determined key role characteristics for each stakeholder, and agreed on the most important activities to fulfill the role profile. Finally, the activities suggested per group were prioritized by plenary group. RESULTS: The prioritized actions for patients included training and capacity building, the establishment of a patient involvement committee, and the establishment of a patient unit at the EC secretariat. For clinicians, it included alignment on evidence assessment from a clinical vs. HTA point of view, capacity building, and standardization of processes. The most important actions for regulators are to develop joint regulatory-HTA guidance documents, align processes and interfaces under the regulation, and share discussions on post-licensing evidence generation. HTDs prioritized scientific advice capacity and the review of the scoping process, and further development of the scope of the assessment report fact checks. The top three actions for national HTA bodies and payers included clarification on the early HTD dialogue process, political support and commitment, and clarification on financial support. CONCLUSIONS: Addressing the activities identified as the most important for stakeholders/collaborators in the EU HTA process (e.g., in the implementation of the EU HTA Stakeholder Network and of the guidance documents developed by the EUnetHTA 21 consortium) will be key to starting an "inclusive civil society dialogue", as suggested by the European Commission's Pharmaceutical Strategy.

The role of stakeholder involvement in the evolving EU HTA process: Insights generated through the European Access Academy's multi-stakeholder pre-convention questionnaire.

Involvement of all relevant stakeholders will be of utmost importance for the success of the developing EU HTA harmonization process. A multi-step procedure was applied to develop a survey across stakeholders/collaborators within the EU HTA framework to assess their current level of involvement, determine their suggested future role, identify challenges to contribution, and highlight efficient ways to fulfilling their role. The 'key' stakeholder groups identified and covered by this research included: patients', clinicians', regulatory, and Health Technology Developer representatives. The survey was circulated to a wide expert audience including all relevant stakeholder groups in order to determine self-perception by the 'key' stakeholders regarding involvement in the HTA process (self-rating), and in a second, slightly modified version of the questionnaire, to determine the perception of 'key' stakeholder involvement by HTA bodies, payers, and policymakers (external rating). Predefined analyses were conducted on the submitted responses. Fifty-four responses were received (patients 9; clinicians: 8; regulators: 4; HTDs 14; HTA bodies: 7; Payers: 5; policymakers 3; others 4). The mean self-perceived involvement score was consistently lower for each of the 'key' stakeholder groups than the respective external ratings. Based on the qualitative insights generated in the survey, a RACI Chart (Responsible/Accountable/Consulted/Informed) was developed for each of the stakeholder groups to determine their roles and involvement in the current EU HTA process. Our findings suggest extensive effort and a distinct research agenda are required to ensure adequate involvement of the key stakeholder groups in the evolving EU HTA process.

Pharmaceutical net price transparency across european markets: Insights from a multi-agent simulation model.

With pharmaceutical health policy striving for fair and sustainable pricing under increasing budgetary pressures, public stakeholders are more and more willing to be involved in transparent access decision-making related to novel medicines, considered by them to be a societal good. Full net price transparency (NPT) is believed by many to promote price competition and to increase equity by making pharmaceutical products accessible to all. Using agent-based simulations, we find that a full NPT system implemented across EU countries would not be viable. This while, acting as rational economic agents, a group of middle- and lower-income countries would not be willing to give up their confidential agreements with the pharmaceutical industry. Even partial NPT would delay access predominantly in middle- to lower-income countries. Hence, we conclude that implementing net price transparency across Europe would be challenging to reach from a political perspective. Especially in lower-income countries there would remain a plea to be left free to negotiate confidential discounts with drug manufacturers. This while, counterintuitively, in those countries NPT will be seen to be unjust while violating Ramsey pricing and distributive justice principles.

Barriers and Opportunities for Implementation of Outcome-Based Spread Payments for High-Cost, One-Shot Curative Therapies.

Background: The challenging market access of high-cost one-time curative therapies has inspired the development of alternative reimbursement structures, such as outcome-based spread payments, to mitigate their unaffordability and answer remaining uncertainties. This study aimed to provide a broad overview of barriers and possible opportunities for the practical implementation of outcome-based spread payments for the reimbursement of one-shot therapies in European healthcare systems. Methods: A systematic literature review was performed investigating published literature and publicly available documents to identify barriers and implementation opportunities for both spreading payments and for implementing outcome-based agreements. Data was analyzed via qualitative content analysis by extracting data with a reporting template. Results: A total of 1,503 publications were screened and 174 were included. Main identified barriers for the implementation of spread payments are reaching an agreement on financial terms while considering 12-months budget cycles and the possible violation of corresponding international accounting rules. Furthermore, outcome correction of payments is currently hindered by the need for additional data collection, the lack of clear governance structures and the resulting administrative burden and cost. The use of spread payments adjusted by population- or individual-level data collected within automated registries and overseen by a governance committee and external advisory board may alleviate several barriers and may support the reimbursement of highly innovative therapies. Conclusion: High-cost advanced therapy medicinal products pose a substantial affordability challenge on healthcare systems worldwide. Outcome-based spread payments may mitigate the initial budget impact and alleviate existing uncertainties; however, their effective implementation still faces several barriers and will be facilitated by realizing the required organizational changes.

A Systematic Review of the Value Assessment Frameworks Used within Health Technology Assessment of Omics Technologies and Their Actual Adoption from HTA Agencies.

BACKGROUND: Omics technologies, enabling the measurements of genes (genomics), mRNA (transcriptomics), proteins (proteomics) and metabolites (metabolomics), are valuable tools for personalized decision-making. We aimed to identify the existing value assessment frameworks used by health technology assessment (HTA) doers for the evaluation of omics technologies through a systematic review. METHODS: PubMed, Scopus, Embase and Web of Science databases were searched to retrieve potential eligible articles published until 31 May 2020 in English. Additionally, through a desk research in HTA agencies' repositories, we retrieved the published reports on the practical use of these frameworks. RESULTS: Twenty-three articles were included in the systematic review. Twenty-two frameworks, which addressed genetic and/or genomic technologies, were described. Most of them derived from the ACCE framework and evaluated the domains of analytical validity, clinical validity and clinical utility. We retrieved forty-five reports, which mainly addressed the commercial transcriptomic prognostics and next generation sequencing, and evaluated clinical effectiveness, economic aspects, and description and technical characteristics. CONCLUSIONS: A value assessment framework for the HTA evaluation of omics technologies is not standardized and accepted, yet. Our work reports that the most evaluated domains are analytical validity, clinical validity and clinical utility and economic aspects.

Evaluation of precision medicine assessment reports of the Belgian healthcare payer to inform reimbursement decisions.

INTRODUCTION: Precision medicines rely on companion diagnostics to identify patient subgroups eligible for receiving the pharmaceutical product. Until recently, the Belgian public health payer, RIZIV-INAMI, assessed precision medicines and companion diagnostics separately for reimbursement decisions. As both components are considered co-dependent technologies, their assessment should be conducted jointly from a health technology assessment (HTA) perspective. As of July 2019, a novel procedure was implemented accommodating for this joint assessment practice. The aim of this research was to formulate recommendations to improve the assessment in the novel procedure. METHODS: This study evaluated the precision medicine assessment reports of RIZIV-INAMI of the last 5 years under the former assessment procedure. The HTA framework for co-dependent technologies developed by Merlin et al. for the Australian healthcare system was used as a reference standard in this evaluation. Criteria were scored as either present or not present. RESULTS: Thirteen assessment reports were evaluated. Varying scores between reports were obtained for the domain establishing the co-dependent relationship between diagnostic and pharmaceutical. Domains evaluating the clinical utility of the biomarker and the cost-effectiveness performed poorly, whereas the budget impact and the transfer of trial data to the local setting performed well. RECOMMENDATIONS: Based on these results we recommend three amendments for the novel procedure. (i) The implementation of the linked evidence approach when direct evidence of clinical utility is not present, (ii) incorporation of a bias assessment tool, and (iii) further specify guidelines for submission and assessment to decrease the variability of reported evidence between assessment reports.

Shedding Light on Reimbursement Policies of Companion Diagnostics in European Countries.

OBJECTIVES: Ensuring access to precision medicine has been an issue because in some European countries, desynchronized reimbursement decision-making occurs between the medicine and the companion diagnostic (CDx). This has resulted in cases in which precision medicine is reimbursed but not the CDx. In overcoming this issue, an alignment of the decision-making process for reimbursement between the 2 entities should be considered. As pharmaceutical reimbursement procedures are meticulously covered in the literature, we set out to systematically map in vitro diagnostic (IVD) reimbursement procedures and identify policies for aligning these procedures with the pharmaceutical reimbursement procedures. METHODS: We selected 8 European countries for this analysis. For each country, we characterized the national benefit basket entailing the IVD medical acts in outpatient care, evaluated the procedure for inclusion, and identified alternative reimbursement practices for CDx. Targeted searches, using publicly accessible sources, were conducted to identify relevant reimbursement policies and laws. RESULTS: We systematically describe the reimbursement process in 8 European countries. Alternative procedures for CDx reimbursement were identified in Belgium and Germany. Alternative policies attributed to the practice of precision medicine were identified in England and Italy. In France, some CDx are included in the "coverage with evidence" development program. Specifically, the health technology assessment agencies of France and England commented on the assessment of companion diagnostics and their clinical utility. CONCLUSION: CDx reimbursement procedures have recently been implemented in some countries. This was seemingly done primarily to ensure access to the precision medicine and only secondary to the value they would provide.

Nearest Neighbour Propensity Score Matching and Bootstrapping for Estimating Binary Patient Response in Oncology: A Monte Carlo Simulation.

Nearest Neighbour (NN) propensity score (PS) matching methods are commonly used in pharmacoepidemiology to estimate treatment response using observational data. Unfortunately, there is limited evidence on the optimal approach for accurately estimating binary treatment response and, more so, to estimate its variance. Bootstrapping, although commonly used to accurately estimate variance, is rarely used together with PS matching. In this Monte Carlo simulation-based study, we examined the performance of bootstrapping used in conjunction with PS matching, as opposed to different NN matching techniques, on a simulated dataset exhibiting varying levels of real world complexity. Thus, an experimental design was set up that independently varied the proportion of patients treated, the proportion of outcomes censored and the amount of PS matches used. Simulation results were externally validated on a real observational dataset obtained from the Belgian Cancer Registry. We found all investigated PS methods to be stable and concordant, with k-NN matching to be optimally dealing with the censoring problem, typically present in chronic cancer-related datasets, whilst being the least computationally expensive. In contrast, bootstrapping used in conjunction with PS matching, being the most computationally expensive, only showed superior results in small patient populations with long-term largely unobserved treatment effects.

Real-World Evidence Gathering in Oncology: The Need for a Biomedical Big Data Insight-Providing Federated Network.

Moving toward new adaptive pathways for the development and access to innovative medicines implies that real-world data (RWD) collected throughout the medicinal product life cycle is becoming increasingly important. Big data analytics on RWD can obtain new and powerful insights into medicines' effectiveness. However, the healthcare ecosystem still faces many sector-specific challenges that hamper the use of big data analytics delivering real world evidence (RWE). We distinguish between exploratory (ExTE) and hypotheses-evaluating (HETE) studies testing treatment effectiveness in the real world. From our experience and in the context of the four V's of data management, we show that to get meaningful results data Variety and Veracity are needed regardless of the type of study conducted. More so, for ExTE studies high data Volume is needed while for HETE studies high Velocity becomes essential. Next, we highlight what are needed within the biomedical big data ecosystem, being: (a) international data reusability; (b) real-time RWD processing information systems; and (c) longitudinal RWD. Finally, in an effort to manage the four V's whilst respecting patient privacy laws we argue for the development of an underlying federated RWD infrastructure on a common data model, capable of bringing the centrally-conducted big data analysis to the de-centrally kept biomedical data.

Patient-Level Effectiveness Prediction Modeling for Glioblastoma Using Classification Trees.

OBJECTIVES: Little research has been done in pharmacoepidemiology on the use of machine learning for exploring medicinal treatment effectiveness in oncology. Therefore, the aim of this study was to explore the added value of machine learning methods to investigate individual treatment responses for glioblastoma patients treated with temozolomide. METHODS: Based on a retrospective observational registry covering 3090 patients with glioblastoma treated with temozolomide, we proposed the use of a two-step iterative exploratory learning process consisting of an initialization phase and a machine learning phase. For initialization, we defined a binary response variable as the target label using one-by-one nearest neighbor propensity score matching. Secondly, a classification tree algorithm was trained and validated for dividing individual patients into treatment response and non-response groups. Theorizing about treatment response was then done by evaluating the tree performance. RESULTS: The classification tree model has an area under the curve (AUC) classification performance of 67% corresponding to a sensitivity of 0.69 and a specificity of 0.51. This result in predicting patient-level response was slightly better than the logistic regression model featuring an AUC of 64% (0.63 sensitivity and 0.54 specificity). The tree confirms confounding by age and discovers further age-related stratification with chemotherapy-treatment dependency, both not revealed in preceding clinical studies. The model lacked genetic information confounding treatment response. CONCLUSIONS: A classification tree was found to be suitable for understanding patient-level effectiveness for this glioblastoma-temozolomide case because of its high interpretability and capability to deal with covariate interdependencies, essential in a real-world environment. Possible improvements in the model's classification can be achieved by including genetic information and collecting primary data on treatment response. The model can be valuable in clinical practice for predicting personal treatment pathways.