Around the Globe: Europe
Fully Integrated Data and Evidence Generation: A Novel, Comprehensive Approach to the Uncertainties of Patient Access in Europe
M. Soriano Gabarro
Epidemiology – Global Health – Policy Independent Researcher
Isabelle Stoeckert
Independent Regulatory Science Expert
W

ith the January 2025 implementation of the Health Technology Regulation, the European Union (EU) has introduced the Joint Clinical Assessment (JCA) as a parallel process to the Marketing Authorization review process. This leads to a much earlier evaluation of clinical comparative evidence regarding the relative effects of a new health technology—whether a medicinal product or medical device—and the earlier identification of uncertainties that may impact subsequent national or regional access decisions. Health technology developers need to overcome these emerging challenges.

Many developers have begun to incorporate the views of internal and external stakeholders into their evidence generation strategies and are integrating real-world data (RWD) sources and observational research methods and designs for the generation of real-world evidence (RWE) to complement their clinical development plans and studies.

Developers are also adapting to new health authority standards and requirements concerning health data quality and methodological rigor, striving to optimize use of available data sources and study designs to accelerate clinical development, regulatory approval, and patient access.

Moving forward, it will be essential to also integrate healthcare data sources and evidence into unified evidence generation plans and the adoption of integrated approaches to analyze such data. There is one example of a data integration and evidence generation plan for a specific disease/product area at one specific point in time during the product’s lifecycle. This article will explore other approaches for achieving such integration.

Comparative evidence at the time of Marketing Authorization Application/Joint Clinical Assessment (JCA) remains limited.

Choosing whether to invest in pharmaceutical innovation in Europe has become a pivotal decision for companies developing health technologies worldwide. While EU legislators signal support for innovation, current United States (US) pricing policies tend to discourage clinical development and early product launches in the EU. The introduction of the EU Joint Clinical Assessment (JCA) requires developers to plan and present comparative clinical evidence on health outcomes at the time of Marketing Authorization Application (MAA). Fulfilling all expectations and requirements of EU Regulators, national Health Technology Assessment bodies (HTAb), and reimbursement systems through the conduct of traditional randomized controlled trials (RCT) increases costs and lengthens the development, marketing authorization, and introduction of health technologies and, in many cases, raises expected return on investment (ROI) concerns to investors. Additionally, at times such RCTs may not be feasible, may not target the most relevant populations, or may not be ethical.

EU Health Technology Assessment (HTA) bodies will evaluate the certainty of comparative clinical evidence before products reach any EU market. This early assessment is likely to reveal more uncertainties than what would be identified at a later stage, when additional evidence from real-world exposure will have been accumulated.

Recent experience with products approved early under Priority Medicines Status (PRIME)/conditional marketing authorization (CMA) as reported by Cerreta et al. (publication in progress) indicate higher uncertainties for healthcare decision makers (as compared to standard approvals) at time of approval. This relates to the extent to which clinical trial evidence can support access to the approved indications, which comparators are relevant, whether the trial duration is sufficient, and the value of available health-related quality-of-life data or other relevant patient experience data (PED).

To overcome challenges in comparative clinical evidence and increase the certainty and availability of comparative clinical evidence to inform stakeholder decisions early in, very early integration of all available data and evidence into a comprehensive Integrated Evidence Generation plan will be of key relevance and value to health technology developers and healthcare decision-making bodies.

Many developers consider integrated evidence planning primarily as a means of generating RWE beyond RCTs or as a way to generate evidence that fills the gaps left by those RCTs.

However, this perspective overlooks the potential to connect data and evidence early in the process, which optimizes opportunities for health technology development by complementing RCTs and by addressing knowledge gaps throughout the development lifecycle.

Additionally, developers often have internal structures that hinder comprehensive data integration, evidence generation planning, and study execution. Planning for activities such as generating evidence from different data sources and populations on standard of care, complementing single-arm trials with RWD, or generating RWE for an external control arm within a RCT must be done early on by a multidisciplinary team of experts designing the RCTs relevant for the health technology under development. However, expertise and objectives are frequently siloed with teams of experts based on different, not interconnected, structures. Processes for the design and conduct of different types of clinical studies are also often governed by separate committees or functions within a developer’s organization.

Key Elements of a Comprehensive Integrated Data and Evidence Generation Strategy and Plan

A comprehensive Integrated Data and Evidence Generation Plan for a specific health technology should systematically incorporate the perspectives and needs of different stakeholders such as patients, clinicians, regulatory bodies, payers, HTA bodies, and other policymakers. It should also identify and address all evidence gaps; leverage, map, and select multiple high-quality, fit-for-use health data sources; and use advanced research methodologies and innovative technologies to generate robust, reliable evidence supporting health technology development throughout its entire lifecycle (Figure 1).

Closely aligned functions within the developer company should contribute to the early strategic conceptualization and planning of the Integrated Data and Evidence Generation plan, including the assessment of evidence gaps, the identification, mapping, and selection of the most appropriate health data sources through a comprehensive data plan, and the deployment of advanced methods and innovative technologies, as well as the execution of required studies (Figure 2).

A diagram showing a central blue circle labeled "Data and Evidence" with four blue arrows pointing inward, labeled with text about data sources, data quality, advanced methods, and stakeholder needs.
Figure 1: Alignment among clinical development, medical affairs, epidemiology, statistics, data sciences, patient access, and regulatory experts on a comprehensive Integrated Data and Evidence Generation Strategy and Plan.
A table titled "Integrated Data and Evidence Generation Plan" with columns for "Section" and "Key Elements." It lists 12 numbered steps outlining components of the plan, from asset overview to evidence dissemination.
Figure 2: Key elements of a comprehensive Integrated Data and Evidence Generation Strategy and Plan.

Novel Concepts with Potential to Increase Certainty of Evidence

With the increasing availability of fit-for-use health data sources complemented by advanced methodological approaches and improved computational competences—including emerging applications of AI in clinical research—it is now possible to leverage multiple and diverse health data sources with sophisticated statistical methods and innovative analytical tools. These advances enable more robust and reliable evidence generation for the development and access of new health technologies. New techniques for collecting, curating, analyzing, and integrating health data from multiple sources are also enhancing extrapolation and modeling capabilities. Table 1 below outlines key methodological and technological developments pertinent for the utilization and harmonization of health data in clinical research. The adoption of these approaches depends on strong expertise, partnerships, and collaboration. It also depends on establishing trust among healthcare decision makers. Facilitating stakeholder dialogue is essential to fostering confidence in these methodological and technological developments, thereby supporting efforts to increase the certainty of evidence at the time of MAA and JCA submissions.

Complex clinical trials
Nonconventional, high-efficiency studies designed with adaptive elements, multiple arms, or advanced, innovative methods that evaluate multiple drugs, populations, or diseases simultaneously under a shared framework. Includes platform, adaptive, and basket trials. Primarily used in oncology. References: EMA guideline on clinical trials for small populations, Q&A for complex clinical trials.
Bayesian approaches
Bayesian approaches make use of prior knowledge in planning/conducting new experiments; i.e., by adapting the design based on experience gained over time. Depending on context, the methods are fit for use and can complement the frequentist tools determined by p values in analyzing evidence for healthcare decision making. References: ICH E20 Guideline on Adaptive Designs, EMA Concept Paper Jan 2026, EMA workshop 17 June 2025.
Estimand framework
An estimand is a precise description of the treatment effect reflecting the clinical question posed by a given clinical trial objective and considers how intercurrent events are reflected in the clinical question of interest. Hence, it is particularly useful for time-to-event observations. Allows multiple views on same data set. References: ICH E9 (R1) Addendum, P. Arlett et al. 2025.
External control arms
Used as a comparator against an experimental treatment group in a single-arm clinical trial. It includes historic and concurrent external controls using data sources such as RWD, registries, and synthetic data. Critical for small or selected populations; i.e., in personalized medicines settings and for gene technology advances. References: EMA draft concept paper July 2025, EMA Workshop Nov 2025.
Target Trial Emulation framework
Methodological framework that applies the key features of RCT design to the analysis of observational data to strengthen causal inference. TTE may be used constructively to benchmark RWE against established trial results. Can be complemented with other options to link RCT and RWD (i.e., tokenization). Reference: GetReal Institute TTE Report Dec 2025.
Real-World Evidence (RWE)/Observational study designs
Observational study designs and studies using RWD that complement RCTs by capturing real-world outcomes, patient variability, and long-term effects of interventions observed as part of routine clinical care. Advances in study design and analytics have significantly improved RWE’s ability to infer causality in areas of clinical research. Acceptance of use in healthcare decision making is increasing. Reference: Jansen et al 2025.

RWE and observational studies are increasingly using federated health data networks with harmonized data sources that leverage large, diverse, multi-institutional data sets while maintaining stringent patient privacy and complying with data regulations. They allow for analysis of data across countries and healthcare systems, increasing study robustness and generalizability.

Patient Experience Data (PED)
Information regarding patients’ experiences, perspectives, needs, and priorities, reported directly by patients or their caregivers without interpretation by clinicians. It includes qualitative experience as well as Patient Preference Studies and Patient Reported Outcomes. There is an evolving role for PED in EU healthcare decision making. PED is particularly useful in this context to inform on patient relevance of outcomes as well as on meaningful thresholds of such outcomes. The EU HTA Regulation mandates consideration of the patient perspective in EU JCAs. Reference: ICH E22 Guideline on Patient Preference Studies, EMA reflection paper on Patient Experience Data 2025.
Acceptance of Novel Biomarker, Outcomes, Digital Health Technologies (DHTs)
The pathways for endorsement of such tools across stakeholders remain scattered and involve many uncertainties. HTA bodies have different views on value/clinical relevance, often defined by their individual methodologies, and expect demonstration of clinical/patient relevance. The pathway foreseen by EMA’s Qualification of New Methodologies is currently being reshaped.
Appropriate levels of evidence for small populations (Orphan, Pediatrics, Feasibility)
Feasibility of RCTs is hampered by many factors, including recruitment challenges as well as ethical and resource considerations (e.g., pediatric indications, biomarker-directed therapies, or rare diseases). The perspectives on whether an RCT is possible differ widely between stakeholders, and the adaptation of evidentiary values in cases when RCTs are not feasible remains critical. Developing a common understanding on the feasibility of RCTs and appropriate levels of evidence for small populations and trials with recruitment challenges is essential.
Modeling, simulations, and synthetic data
Integrates nonclinical data, clinical data, prior information, and knowledge (e.g., drug and disease characteristics). Allows for optimization of study designs and evaluation of treatment effects under multiple scenarios before trial execution; supports evaluation of long-term effects; predicts healthcare interventions in pandemia. Reference: ICH M15 MIDD.
Table 1: Methodological and technological developments pertinent to the utilization and harmonization of health data and with the potential to enhance the certainty of evidence.
Complex clinical trials
Nonconventional, high-efficiency studies designed with adaptive elements, multiple arms, or advanced, innovative methods that evaluate multiple drugs, populations, or diseases simultaneously under a shared framework. Includes platform, adaptive, and basket trials. Primarily used in oncology. References: EMA guideline on clinical trials for small populations, Q&A for complex clinical trials.
Bayesian approaches
Bayesian approaches make use of prior knowledge in planning/conducting new experiments; i.e., by adapting the design based on experience gained over time. Depending on context, the methods are fit for use and can complement the frequentist tools determined by p values in analyzing evidence for healthcare decision making. References: ICH E20 Guideline on Adaptive Designs, EMA Concept Paper Jan 2026, EMA workshop 17 June 2025.
Estimand framework
An estimand is a precise description of the treatment effect reflecting the clinical question posed by a given clinical trial objective and considers how intercurrent events are reflected in the clinical question of interest. Hence, it is particularly useful for time-to-event observations. Allows multiple views on same data set. References: ICH E9 (R1) Addendum, P. Arlett et al. 2025.
External control arms
Used as a comparator against an experimental treatment group in a single-arm clinical trial. It includes historic and concurrent external controls using data sources such as RWD, registries, and synthetic data. Critical for small or selected populations; i.e., in personalized medicines settings and for gene technology advances. References: EMA draft concept paper July 2025, EMA Workshop Nov 2025.
Target Trial Emulation framework
Methodological framework that applies the key features of RCT design to the analysis of observational data to strengthen causal inference. TTE may be used constructively to benchmark RWE against established trial results. Can be complemented with other options to link RCT and RWD (i.e., tokenization). Reference: GetReal Institute TTE Report Dec 2025.
Real-World Evidence (RWE)/Observational study designs
Observational study designs and studies using RWD that complement RCTs by capturing real-world outcomes, patient variability, and long-term effects of interventions observed as part of routine clinical care. Advances in study design and analytics have significantly improved RWE’s ability to infer causality in areas of clinical research. Acceptance of use in healthcare decision making is increasing. Reference: Jansen et al 2025.

RWE and observational studies are increasingly using federated health data networks with harmonized data sources that leverage large, diverse, multi-institutional data sets while maintaining stringent patient privacy and complying with data regulations. They allow for analysis of data across countries and healthcare systems, increasing study robustness and generalizability.

Patient Experience Data (PED)
Information regarding patients’ experiences, perspectives, needs, and priorities, reported directly by patients or their caregivers without interpretation by clinicians. It includes qualitative experience as well as Patient Preference Studies and Patient Reported Outcomes. There is an evolving role for PED in EU healthcare decision making. PED is particularly useful in this context to inform on patient relevance of outcomes as well as on meaningful thresholds of such outcomes. The EU HTA Regulation mandates consideration of the patient perspective in EU JCAs. Reference: ICH E22 Guideline on Patient Preference Studies, EMA reflection paper on Patient Experience Data 2025.
Acceptance of Novel Biomarker, Outcomes, Digital Health Technologies (DHTs)
The pathways for endorsement of such tools across stakeholders remain scattered and involve many uncertainties. HTA bodies have different views on value/clinical relevance, often defined by their individual methodologies, and expect demonstration of clinical/patient relevance. The pathway foreseen by EMA’s Qualification of New Methodologies is currently being reshaped.
Appropriate levels of evidence for small populations (Orphan, Pediatrics, Feasibility)
Feasibility of RCTs is hampered by many factors, including recruitment challenges as well as ethical and resource considerations (e.g., pediatric indications, biomarker-directed therapies, or rare diseases). The perspectives on whether an RCT is possible differ widely between stakeholders, and the adaptation of evidentiary values in cases when RCTs are not feasible remains critical. Developing a common understanding on the feasibility of RCTs and appropriate levels of evidence for small populations and trials with recruitment challenges is essential.
Modeling, simulations, and synthetic data
Integrates nonclinical data, clinical data, prior information, and knowledge (e.g., drug and disease characteristics). Allows for optimization of study designs and evaluation of treatment effects under multiple scenarios before trial execution; supports evaluation of long-term effects; predicts healthcare interventions in pandemia. Reference: ICH M15 MIDD.
Table 1: Methodological and technological developments pertinent to the utilization and harmonization of health data and with the potential to enhance the certainty of evidence.

How we respond now will shape future patient access to health technology innovations in the EU.

Enhancing evidentiary standards in light of scientific advancements requires a renewed approach on how we access, use, combine, and analyze fit-for-use health data and on how we generate robust, reliable evidence to accelerate clinical development and regulatory approval and maximize patient access to new health technologies within the EU.

Trust from healthcare decision makers in the suitability of fit-for-use health care data and methodological and technological innovations is fundamental.

A dialogue on evidentiary standards between EU regulators and EU HTA bodies was initiated in 2025. Article 6 of the HTR implementing act on cooperation of HTA bodies and EMA mandates opportunities for exchanging views on scientific or technical matters across products relevant to JCA and joint scientific consultation. Such discussions—informed by expert insights—contribute to clarifying acceptance criteria for developers and promote broader adoption. They address minimum requirements for acceptance, and whether these standards are relevant for particular diseases or therapeutic areas.

To meet the expectations of healthcare decision-making bodies, developers should formulate comprehensive Integrated Data and Evidence Generation strategies and plans that are tailored to individual health technologies. The active involvement and leadership of the essential functions—including experts in methodological and technological domains such as statisticians, epidemiologists, and data scientists—as well as policy experts are vital to success. Developers must incorporate these elements to establish robust, reliable comparative evidence needs at an early stage and maximize opportunities for patient access to health technologies in Europe.

Note: These considerations are the sole view of the authors and are partially based on a panel discussion with HTA bodies and regulators at the DIA Europe 2026 session Addressing Evidence Needs for Regulatory and HTA Decision Making – How to Ensure that Clinical Development Leads to Patient Access in Europe. The authors thank all contributors for sharing their thoughts.
To learn more, plan to attend DIA Europe 2027 next March in Basel, Switzerland.
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