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Real World Evidence (RWE) 201…THE END – A Landscape Analysis of Regional RWE Frameworks – The European Health Data Space and DARWIN-EU

RWE 201 – THE END – A Landscape Analysis of Regional RWE Frameworks – The European Health Data Space and DARWIN-EU

In our real world evidence (RWE) 201 series we have been exploring the regional regulatory and data access frameworks that have been implemented to support access to clinical experience data (real world data | RWD), which is crucial if we are to use this RWD to generate RWE.
The European Union (EU) is making significant strides in advancing healthcare through a series of interconnected initiatives. The EU4Health Program aims to bolster health systems, focusing on crisis preparedness and disease prevention. The European Health Data Space is an effort to ensure secure access and exchange of health data across EU countries, enhancing healthcare quality and research. Complementing this, the Data Governance Act seeks to foster trust and facilitate data sharing for societal benefits. The European Medicines Agency (EMA)’s 2025 Vision for Real World Evidence (RWE) and the EMA RWE Framework to Support Regulatory Decision Making are pivotal in integrating real-world data (RWD) and RWE into regulatory processes, improving drug development and monitoring. The EU’s Action Plan for RWD & RWE further emphasizes the use of real-world healthcare data in policy and decision-making. Finally, the incorporation of RWD/RWE into the new EU Medicines Regulations marks a significant shift towards evidence-based, data-driven approaches in the pharmaceutical sector, aiming to enhance patient outcomes and healthcare efficiency across the EU.
The following posts provide more details and visual summaries:
•  EU – EU4Health Program:
•  EU – The European Health Data Space:
•  EU – The Data Governance Act:
•  EU – EMA’s 2025 Vision for RWE:
•  EU – EMA RWE Framework to Support Regulatory Decision Making:
•  EU – EU’s Action Plan for Real-World Data (RWD) & RWE:
•  EU – RWD/RWE is Embedded into the New EU Medicines Regulations:

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Real World Evidence (RWE) 201…THE END – A Landscape Analysis of Regional RWE Frameworks – The European Health Data Space and DARWIN-EU2023-12-08T14:58:47+00:00

Real World Evidence (RWE) 201 – EU – EMA Big Data Steering Group Updates Its Workplan to Accelerate Transformation to Data-Driven Medicines Regulation

RWE 201 – EU – EMA Big Data Steering Group Updates Its Workplan to Accelerate Transformation to Data-Driven Medicines Regulation

Updated EMA Big Data Steering Group Workplan:

The Heads of Medicines Agencies (HMA) and European Medicines Agency (EMA) Big Data Steering Group workplan has been enhanced including work on national regulatory use cases for real-world evidence (RWE), intensified work on artificial intelligence (AI) and public consultation on patient experience data (PED).

The updated Big Data Steering Group (BDSG) workplan continues to evolve to integrate use of big data and data analytics in medicines regulation. he updated workplan contains the following key additions:

[1] Real-world evidence (RWE): DARWIN EU® will address use cases from national regulators and learnings from RWE pilots will be gathered and published.  Work on RWE guidance, at EU and international level, will be informed by public consultations and collaboration with international regulators under the umbrella of ICH will continue.

[2] Real World Data (RWD) quality considerations will be published following a public consultation.

[3] Engagement with patients’ organisations will intensify through a public consultation on Patient Experience Data (PED), dialogue on training needs, workshop on patient registries, a call to populate the metadata and RWD source catalogues with PED, and exploration of use cases to analyse PED to establish their role in regulatory decision-making process.

[4] Analysis of additional data types will be explored with the development of use cases for genomics data, the launch of a ‘proof of concept’ on non-clinical raw data analysis and discussion on Chemistry, Manufacturing and Controls (CMC) data analysis.

[5] Experimentation of advanced analytics, including AI, will continue and the first AI knowledge mining tool for core regulatory processes will be released to the EU regulatory network.

[6] The future European Medicines Regulatory Network data strategy will be developed to prepare for publication in 2025.

A full overview of the timeline can be found here:

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Real World Evidence (RWE) 201 – EU – EMA Big Data Steering Group Updates Its Workplan to Accelerate Transformation to Data-Driven Medicines Regulation2023-12-08T14:14:24+00:00

EU – RWD/RWE is Embedded into the New EU Medicines Regulations

RWE 201 – EU – RWD/RWE is Embedded into the New EU Medicines Regulations


Coming Soon…New EU Medicines Regulations:

In 2023, the European Commission undertook an ambitious overhaul of its pharmaceutical regulations. This revision addresses foundational pharmaceutical legislation, specifically Regulation 726/2004, Directive 2001/83/EC, and rules for medicines tailored for children and rare diseases, namely Regulation 1901/2006 and Regulation 141/2000/EC.

Primary Aims:

– Ensure all EU patients access safe, effective, and affordable medicines promptly and fairly.

– Bolster medicine supply security across the EU.

– Propagate an innovation-centric environment for medicine R&D in Europe.

– Pivot towards environmentally sustainable medicines.

– Confront antimicrobial resistance and environmental pharmaceutical contamination through a holistic One Health approach.

Key Points of the Revision:

  1. Individual Patient Data: Regulators can now request structured individual patient data from clinical studies, promoting data-driven benefit-risk assessments for medicines throughout their life cycle (Recital 63 of MP-R).
  2. Transparency of Public Support: There’s now a mandate to disclose any direct financial backing received from public authorities for medicine R&D, fostering accountability and transparency (Recital 131 of MP-D).
  3. Patient Representation: The CHMP (Committee for Medicinal Products for Human Use) and PRAC (Pharmacovigilance Risk Assessment Committee) now include patient representatives, enriching patient voices in decisions.
  4. Real World Data (RWD): The revision endorses the use of health data, particularly RWD, for regulatory decision-making. Through systems like the DARWIN and European Health Data Space infrastructure, the agency can harness supercomputing, AI, and big data without jeopardizing privacy (Recital 60 of MP-R).
  5. Regulatory Sandbox: This introduces a controlled setting wherein innovative regulatory solutions can be tested, cultivated, and authenticated under scrutiny (Articles 2(12), 113-155 of MP-R).
  6. Compassionate Use Programs: Provisions for these programs, which offer early medicinal product access, have been strengthened. It’s vital to collect data from these programs to evaluate the benefit-risk ratio of medicines (Recital 57 & Article 26(4) of MP-R).
  7. Comparative Effectiveness: By repurposing medicines and leveraging comparative trial data, patients will have expedited access to novel treatments. Such data assists authorities in ascertaining a medicine’s cost-effectiveness.
  8. Relative Effectiveness: The EU has devised a scientific, evidence-based methodology to gauge the relative effectiveness of medicinal products. This focuses on a medicine’s added value against other health technologies but doesn’t extend to its marketing authorization (Recital 130 of MP-R).

In essence, this legislative revamp by the European Commission fosters a more patient-centric, transparent, and data-driven approach in the EU pharmaceutical landscape.

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EU – RWD/RWE is Embedded into the New EU Medicines Regulations2023-11-05T12:28:42+00:00

EU – EU’s Action Plan for Real-World Data (RWD) & RWE

RWE 201 – EU – EU’s Action Plan for Real-World Data (RWD) & RWE


The European Union has embarked on an ambitious journey to weave Real World Evidence (RWE) into the fabric of its healthcare system. The strategy, underscored by a comprehensive multi-year plan, focuses on harnessing the power of real-world data (RWD) through collaboration, standardization, and innovation. The infrastructure for this integration outlines seven pivotal components: Terminology, Findability, Access, Quality, Advanced Analytics, Use, and Use Cases.


RWE 2020 – The Foundation: The EU emphasizes on the importance of RWD Collection and RWE Generation. Key pillars of this foundational year include:

– Enhancing collaboration nationally and across borders.

– Creating a robust framework with effective methodologies.

– Recognizing the role of RWE in supplementing clinical trials.

– Aiming to support decisions by regulators, HTA, and payers.

– Advocating support for healthcare providers.



– Focuses on access to RWD and the establishment of the EU4Health Program.

– Introduction of new legislations: Artificial Intelligence Regulation, Health Technology Assessment Regulation, and laws on RWE Value and RWD Access.

– Notably, a step towards centralized data governance with the European Health Data Space Regulation.



– Expands on the RWE Use Cases with guidelines and draft laws.

– Emphasis on medicinal product regulations.

– The Data Governance Act aims to support data access and streamline data utilization.



– Proliferation of RWE use cases continues.

– Medicinal Products Regulation is in the limelight alongside explorations into single-arm trials by EMA.

– The year marks a notable focus on AI with the EMA’s guidance on Artificial Intelligence.2024:

– The direction is set towards advanced RWD analytics and a broader scope of RWE Use Cases.

– Introduces a Catalogue of NIS using RWD Sources and a focus on RWD findability with source catalogues = The replacement for the EU PAS Register


This roadmap not only outlines the EU’s commitment to innovative healthcare solutions but also sets a precedent for global health systems to leverage real-world data and evidence for enhanced patient care.

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EU – EU’s Action Plan for Real-World Data (RWD) & RWE2023-11-05T12:24:00+00:00

EU – EMA’s 2025 Vision for RWE

RWE 201 – EU – EMA’s 2025 Vision for RWE


EMA 2025 Vision:

EMA RWE Framework Report:

The European Medicines Agency (EMA) isn’t simply relying on RWE submissions from companies. They’ve proactively embarked on developing the DARWIN EU framework, providing access to real-world data (RWD), enabling them to validate study designs, confirm disease prevalence for paediatric waivers, orphan drug designations, and more.

By 2025, EMA aims to:

  1. Fully integrate the use of RWE.
  2. Establish its value across all regulatory domains.

RWE and RWD are not novelties in the EU’s medicinal regulation. While their roles in safety monitoring and disease epidemiology are well-established, their evidentiary worth, especially for efficacy demonstration, is under continued assessment.

Key Developments:

[1] DARWIN EU: Launched in 2022, funded by the EU4Health program, it’s an EU-wide RWD network. It facilitates high-quality studies pivotal for regulatory decisions…by and for the EMA.

[2] RWE Framework Report (June 2023): The EMA detailed how RWE aids in:

– Designing and gauging the feasibility of upcoming studies.

– Ensuring representativeness and validity of completed research.

– Gauging disease prevalence and incidence.

– Understanding clinical management practices.

– Monitoring drug utilization in real-world scenarios.

– Evaluating the efficacy and safety of medical measures.

– Measuring the effectiveness of risk minimisation measure.

Significance: The COVID-19 pandemic highlighted the importance of RWE, as regulators utilized RWD to monitor the safety and efficacy of treatments. This emphasizes the regulator’s role, not just as a gatekeeper but as an adept analyst of RWE. The European Medicine Regulators Network (EMRN) recognizes this, investing in enhancing the skills of its workforce through recruitment, dedicated training, and promoting best practices.

In summary, RWE (generated from RWD) provides the EMA with a practical perspective on medical interventions in real-life scenarios, becoming indispensable in its regulatory role.

Given the EMA’s proactive stance on integrating RWE into their regulatory processes, how is your organization adapting its drug development strategies to ensure you’re not only keeping pace but also leveraging the full potential of real-world evidence?

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EU – EMA’s 2025 Vision for RWE2023-11-05T12:19:04+00:00

EU – The Data Governance Act

RWE 201 – EU – The Data Governance Act


Data Governance Act:

The EU Data Governance Act (DGA) aims to establish a legal framework for data sharing across sectors. It promotes the sharing of data, including personal data, in a secure and standardized manner. This is relevant for health data, as it facilitates the sharing of RWD among healthcare providers, researchers, and institutions.

[1] Data Intermediaries: The DGA introduces the concept of “data intermediaries” and “data intermediary service providers” (DISPs), which are entities that facilitate data sharing between data providers (e.g., healthcare organizations) and data users (e.g., researchers). These intermediaries may play a role in managing access to health data within the EHDS, ensuring compliance with data protection regulations.

Examples of DISPs: Salus Coop, Midata, Data Trusts Initiative, UK Biobank, INSIGHT, The Pistoia Alliance

[2] Data Governance Mechanisms: The Act promotes the development of data governance mechanisms (e.g., mandatory registration of DISPs by Sept 2025), including codes of conduct, interoperability standards and certification mechanisms, to ensure that data sharing activities adhere to data protection and privacy regulations. This is crucial in the context of health data, where sensitive information is involved.

[3] Cross-Border Data Sharing: The Act encourages cross-border data sharing within the European Union, which is particularly relevant for research purposes within the EHDS, as it enables access to a more diverse and comprehensive set of health data from different member states.

[4] Data Portability and Data Altruism: The Act recognizes the importance of data portability and data altruism, allowing individuals to have more control over their data and choose to share it for research or public interest purposes, which supports the availability of RWD for research in the health sector.

[5] Security and Privacy: The DGA places a strong emphasis on data security and privacy (e.g., secure processing environments), requiring data sharing activities to comply with EU data protection regulations, such as the General Data Protection Regulation (GDPR). This is crucial when dealing with health data to ensure the protection of patients’ sensitive information.

In summary, the EU Data Governance Act, provides a legal framework for secure and standardized data sharing that is essential for supporting access to Real-World Data and research activities within the European Health Data Space. It helps to ensure that data sharing is conducted in compliance with data protection regulations while promoting transparency, security, and cross-border collaboration in health research.

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EU – The Data Governance Act2023-10-14T11:38:23+00:00

EU 2021 – Setting the Scene for the European Health Data Space

RWE 201 – EU 2021 – Setting the Scene for the European Health Data Space



In the evolving healthcare framework of the European Union (EU), the prominence of Real-World Data (RWD) and Real-World Evidence (RWE) is unmistakable. These tools play a critical role in shaping decisions about treatment affordability and efficacy. The early EU initiatives focused on fostering collaborations, enhancing data precision, and championing a healthcare environment that is transparent and sustainable for all its member states.

[1] Stakeholder Engagement: Collaboration with regulators, HTA bodies, healthcare experts, and patients boosts the validity and utility of data, promoting both affordability and better decision-making processes.

[2] Value of Randomised Evidence: While controlled randomised trials are a cornerstone, the insights from electronic health records and registry-based trials capture a more genuine reflection of patient experiences and outcomes.

[3] RWE Bridging Knowledge Gaps: With initiatives like EHDS and DARWIN EU, RWE promises to supplement existing regulatory knowledge, enhancing aspects like digital health integration, ensuring data privacy, and promoting system interoperability.

[4] Tackling Affordability Issues: In light of spiralling healthcare costs, strategies like the International Horizon Scanning Initiative and the Pharmaceutical Strategy underline the essence of collaborative drug pricing and cost transparency.

[5] Advantages of Cross-border HTA Collaborations: Initiatives like EUnetHTA facilitated joint evaluations of medicines and devices, fostering synergy amongst EU nations.

[6] Enhanced RWE Collection: The EU’s ambition to streamline RWE collection via digital strides within the EHDS platform aims to augment clinical trial findings and thus positively influence regulatory decisions.

[7] Focus on Pricing & Reimbursement: The NCAPR accentuates the need for pricing that champions affordability and promotes a competitive market, particularly emphasizing the role of generics and biosimilars.

[8] Catalysing Innovation: The drive to incentivize innovation ensures a steady influx of effective, accessible medical solutions, maintaining an equilibrium between ground-breaking advancements and affordability.

In summary, the EU’s dedication to leveraging RWD and RWE showcases its resolve to merge innovation with cost-effectiveness in healthcare. Through fostering robust collaborations, championing data-centric initiatives, and prioritizing transparency in medical pricing, the EU is poised to navigate the delicate balance between advancing medical technologies and ensuring accessible, patient-focused care informed by real-world patient experiences.

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EU 2021 – Setting the Scene for the European Health Data Space2023-10-14T09:39:13+00:00

Quality Considerations when Using RWD from Registries to Support Regulatory Decisions in the EU


EMA has published a comprehensive guideline, which provides recommendations on key methodological aspects that are specific to the use of patient registries when planning to conduct registry-based studies to support regulatory decision making on medicinal products within the European Union (EU).

In October 2021, the EMA published its “Guideline on Registry-Based Studies” [Link] [1]. 

According to the EMA:

In this article we will explore:

    • The differences between a registry and a registry-based study
    • Use of registry-based studies for evidence generation
    • Considerations when planning a registry-based study – Feasibility analysis
    • Legal obligations and regulatory requirements for registry-based studies
    • Good Registry Practice (GRP) – Quality considerations for patient registries
    • Examples of agreed key performance indicators (KPIs) of data quality
    • Data sharing outside the context of registry-based studies – Contractual considerations
    • Checklist for evaluating the suitability of registries for registry-based studies


Patient Registries as an Important Data Source for Registry-Based Studies

Patient registries may have several purposes, such as to monitor the clinical status, quality of life, comorbidities and treatments of patients over time or to monitor and improve overall quality of care. They are a source of data on the presence or occurrence of a particular disease or health-related individual characteristic(s), such as a set of signs or symptoms, or a specific condition, such as pregnancy, breast-feeding, a birth defect or a molecular or genomic feature. They are therefore an important source of data for registry-based studies on healthcare practices, utilisation of medicines and medical devices, and outcomes of treatments. They may, in particular, represent an important source of data on rare diseases and patients treated with advanced therapy medicinal products (ATMP), including gene therapy (as per Section 2 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Differences Between a Registry-Based Study and a Patient Registry

(Source: Section 3.1 of the EMA – Guideline on Registry-Based Studies, October 2021 [1])

Use of Registry-Based Studies for Evidence Generation

The acceptability of registry-based studies as a source of evidence for regulatory purposes depends on several factors related to the specific regulatory assessment procedure for the concerned medicinal product, the characteristics of the concerned registry (see Annex) and the objectives, design and analytical plan of the proposed study. Early consultation with national competent authorities (NCAs), where applicable, and with EMA (e.g., the procedure for Scientific Advice and Protocol Assistance) is recommended when a registry-based study is proposed to be used and study protocols should be published (as per Section 3.2 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Examples where registry-based studies have been used for evidence include (as per Section 3.2 of the EMA – Guideline on Registry-Based Studies, October 2021) [1]:

    • To complement the evidence generated in the pre-authorisation phase
        • Examples of such evidence may include information on standards or real-world practice of care for the disease, incidence, prevalence and determinants of disease outcomes in clinical practice, or the characteristics of the registry population.
        • Studies based on patient registries may also contextualise the results of uncontrolled trials, and patient registries have been used to support registry-based randomised controlled trials (RRCTs) for patient recruitment (e.g., to identify patients meeting inclusion/exclusion criteria), randomisation allocation, sample size calculation, endpoints identification, data collection and study follow-up. Open questions remain regarding the validity and relevance of RRCTs. It is therefore recommended to obtain Scientific Advice from EMA and, where applicable, from the concerned NCAs, health technology assessment (HTA) bodies and health insurance schemes as payers on the acceptability of the chosen approach for evidence generation in case deviations from a traditional randomised clinical trial (RCT) design are considered.
    • To provide evidence in the post-authorisation phase
        • Patient registries can be the basis for recruitment and randomisation for RCTs and non-interventional studies, post-authorisation efficacy studies (PAES) and post-authorisation safety studies (PASS) performed after marketing authorisation. 
        • Patient registries may allow linkage of patient records with other data sources such as biobank data, census data, or demographic data.
        • In the context of medicinal products with efficacy previously demonstrated in RCTs, registry-based studies may help, for example, to assess the effectiveness of adapted dosing schemes applied in clinical practice and understand effectiveness and safety of products in a broader clinical disease-related context and a more heterogenous patient population.
        • Products intended for rare diseases are often studied in uncontrolled trials and the size of the safety and efficacy datasets at time of marketing authorisation application is small. In these cases, follow-up for efficacy and safety may be needed, and PAES and PASS are often imposed for post-authorisation evidence generation. These are frequently and preferentially performed on the basis of existing patient registries.
    • To evaluate the effects of medicinal products used during pregnancy and breast feeding
        • Pregnancy registries include pregnant women exposed or not to different treatments and followed up to collect information on outcomes of pregnancy and in the offspring for a given medicinal product. Despite the challenges of such studies related to the completeness of information on pregnancy outcomes, the ascertainment of the exposure window/ trimester, teratology information services or electronic healthcare records where mother-child linkage is possible, pregnancy registries may also provide valuable data on the benefit-risk balance of medicinal products in breastfeeding.

Considerations When Planning a Registry-Based Study – Feasibility Analysis

MAAs/MAHs proposing a registry-based study should provide adequate information regarding the availability of data, the quality management procedures applied and the need and feasibility of introducing any study-specific additional data collection and quality control measures (as per Section 3.3 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

A feasibility analysis should be considered by the MAA/MAH or research organisation initiating the study prior to writing the study protocol, to guide its development and facilitate the discussion with NCAs, EMA, HTA bodies and other parties. The feasibility analysis should be performed in collaboration with registry holders and include the following information, as applicable (as per Section 3.3 of the EMA – Guideline on Registry-Based Studies, October 2021) [1]:

    • General Description – General description of the registry(ies) or network of registries; the Checklist for evaluating the suitability of registries for registry-based studies can be used to prepare this description; the epidemiology of the disease, this is more precise, medicines use and standards of care applied in the country or registry setting should be described if relevant for the specific study.
    • Availability of Core Data Elements – Analysis of the availability in the registry of the core data elements needed for the planned study period (as availability of data elements may vary over time), including relevant confounding and effect-modifying variables, whether they are mapped to any standard terminologies (e.g., MedDRA, OMOP common data model), the frequency of their recording and the capacity to collect any additional data elements or introduce additional data collection methods if necessary .
    • Quality and Completeness of the Data Elements – Analysis of the quality, completeness and timeliness of the available data elements needed for the study, including information on missing data and possible data imputations, risk of duplicate data for the same patient, results of any verification or validation performed (e.g., through an audit), analysis of the differences between several registries available in the network and their possible impact on data integration, description of the methods applied for data linkage as applicable, and possible interoperability measures that can be adopted.
    • Adverse Event Reporting Processes – Description of processes in place for the identification of adverse events and prompt reporting of suspected adverse reactions occurring in the course of treatments, and capacity to introduce additional processes for their collection and reporting if needed.
    • Study Size and Patient Recruitment – Study size estimation and analysis of the time needed to complete patient recruitment for the clinical study by providing available data on the number of centres involved in the registry(ies), numbers of registered patients and active patients, number of new patients enrolled per month/year, number of patients exposed to the medicinal product(s) of interest, duration of follow-up, missing data and losses to follow-up, need and possibility to obtain informed consent.
    • Bias – Evaluation of any potential information bias, selection bias due to the inclusion/exclusion criteria of centres (e.g., primary, secondary or tertiary care) and patients, potential time-related bias between and within registry(ies), and potential bias due to loss to follow-up.
    • Confounding – Evaluation of any potential confounding that may arise, especially if some data elements cannot be collected or measured.
    • Analytical Issues – Analytical issues that may arise based on the data characteristics and the study design.
    • Data Privacy – Any data privacy issues, possible limitations in relation to informed consent and governance related issues such as data access, data sharing and funding source.
    • Suitability of the Registry – Overall evaluation of the suitability of the registry for the specific study, taking into account any missing information on the above-mentioned aspects.

The final report of the feasibility analysis may be submitted either separately or as part of the proposed protocol for a registry-based study. In order to inform the feasibility of other studies in the same registry and reduce duplication of work, the feasibility analysis should be published with the study protocol in the EU PAS Register in agreement with the registry holder. Any confidential information may be redacted if needed (as per Section 3.3 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Joint Registry-Based studies

For regulatory studies addressing a class of products where several MAHs have the same obligation to perform a study, MAHs are encouraged to design a joint registry-based study or to join an already existing study on the same topic (as per Section 3.3 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Study Protocol

The study protocol should describe how the registry infrastructure and population will be used to address the research question of interest, how the study will be conducted and how the validity (both internal and external) of the results will be ensured (as per Section 3.4 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Protocols for non-interventional studies should follow the guidance on the format and content of the protocol for PASS or the Scientific Guidance on PAES. They should apply the best methodological standards, including if applicable those described by the ENCePP Guide on Methodological Standards in Pharmacoepidemiology. The ENCePP Checklist for Study Protocols identifies important points to be addressed when designing a non-interventional study and writing the study protocol (as per Section 3.4 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Where the registry-based study entails secondary use of data, the study protocol should specify the events of interest that are already collected in the registry and discuss the risks of bias and unmeasured confounding. Dedicated and complete search strategies, coding lists or adjudication should be used to accurately define the outcomes of interest (as per Section 3.4 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

The protocol should specify agreements made with the registry holder on the additional variables that can be collected, with timelines for data availability (as per Section 3.4 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

If a registry-based study is to be conducted across multiple registries, a common study protocol should be developed based on core data elements available in the registry and a common design, even if some aspects of the study may vary according to the characteristics of each registry and not all outcomes may be assessed in all registries. Nevertheless, the protocol should also describe differences between registries, assess the resulting heterogeneity of data and critically discuss its potential impact on study results. The protocol or statistical analysis plan (SAP) should propose sensitivity analyses addressing this heterogeneity (as per Section 3.4 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Where several registries are suitable for a study but not all of them are intended to be involved, the study protocol should provide the justification of the choice, i.e., inclusion and exclusion criteria, and discuss the potential impact of selection and interpretability of datasets and findings (as per Section 3.4 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Choice of Study Population – Procedures for Primary Data Collection

The registry population serves as the source population for the registry-based study. The choice of the study population should be driven by the study objectives and may represent the totality of the registry population or only a subset with pre-defined characteristics. For example, when studying a medicine of interest, the potential study population may include various groups of patients: newly diagnosed patients entering the registry and receiving a first prescription of the medicine of interest, and registry patients already diagnosed with the disease and who are switched from another treatment, receive the medicine of interest as add-on therapy or have received the medicine of interest only in the past. In such situations, it is useful to collect the data needed to describe all patients receiving the medicine of interest and assess the heterogeneity between subsets of these patients (as per Section 3.5.1 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

In case of study-specific primary data collection within an existing registry, it is critical that procedures are in place to support complete data collection on all eligible patients enrolled in the registry (as per Section 3.5.1 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Additional study-specific primary data collection may add complexity to the registry-based study. The data collection method applied should clearly be described in the study protocol as it has implications with regards to potential sources of bias and confounding, adequate retrieval of missing data and safety reporting requirements (as per Section 3.6 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Additional study-specific data collection may also affect the ongoing registries’ data collection and maintenance and require audit and validation (as per Section 3.6 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Informed Consent

Informed consent serves as an ethical standard and procedural obligation. It provides the fundamental condition under which a person can be included into a study. It is not conceived as a legal basis but should be seen as a safeguard for data processing

compliance. Therefore, it is important to distinguish between the requirement for consent for a subject to participate in a study and the requirements for a lawful processing of personal data under the GDPR (as per Section 3.5.2 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

In the context of a registry-based study, the ethical and procedural obligations require that informed consent be obtained from patients to participate in the study in addition to the consent already given for participating in the registry, as applicable. It should clearly outline areas such as an explanation of the purposes of the study, the expected duration, intended use of their data and cover all data to be accessed and processed as specified in the study protocol (including but not limited to the access for monitoring, auditing or inspections by competent authorities). It should also provide information about what will happen to the results of the study (as per Section 3.5.2 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Data Protection

The conduct of registry-based studies needs to respect the following applicable Union data protection rules at each step of the processing of personal data, including the option for data sharing/pooling between registries and other stakeholders like competent authorities and MAAs/MAHs:

    • The General Data Protection Regulation (EU) 2016/679 (GDPR), which applies to processing carried out by organisations and bodies operating within the EU, and
    • Regulation (EU) 2018/1725 (EUDPR), which applies to Union institutions, bodies, offices and agencies

(as per Section 3.5.3 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

When conducting registry-based studies, the legal basis of the personal data processing needs to be established. Specific considerations may be required in case of processing of special categories of personal data such as sensitive (health) information. It should be noted that Member States are allowed to maintain or introduce further conditions, including limitations with regard to the processing of genetic data, biometric data or data concerning health (as per Section 3.5.3 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

According to the principle of accountability, it is the obligation of the data controller (e.g. a registry holder, MAA/MAH, investigator) to implement appropriate technical and organisational measures to ensure and be able to demonstrate that the personal data are processed in accordance with data protection requirements (as per Section 3.5.3 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Data Quality Management

Data quality management for a registry-based study depends on various factors, including the planned use of the study results and whether the study makes use of primary data collection or secondary use of registry data. While data quality management of the registry is the responsibility of the registry holder, it is the MAA/MAH’s responsibility to manage the data quality of the registry-based study and interpret the results based on findings on data quality. Specific details on level of data verification and actions to be taken if there are relevant findings, including possible internal or external audits, should be described in a specific data management plan. This plan should be discussed and agreed upon by the MAA/MAH and the registry holder (as per Section 3.7 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Methods and specific measures should be guided by the feasibility analysis and be selected with a view to minimise risk of invalid study results (as per Section 3.7 of the EMA – Guideline on Registry-Based Studies, October 2021) [1]:

    • The validity of any data cleaning, extraction and transformation processes should be verified and monitored. This may be specifically relevant in studies using a network of registries where the transformation is performed locally. A risk-based approach requires the identification of data that are critical for data protection and the reliability of the study results.
    • Quality checks of the data used in the study should be performed to alert on erroneous, missing or out-of-range values and logical inconsistencies, and trigger prompt data verification and remedial measures if needed.
    • In studies with primary data collection, the various factors (e.g. limited human or material resources or inadequate training) influencing quality should be identified and addressed to preserve the integrity of the study. Possible measures include random source data verification, onsite review of processes and computerised systems used for data collection and management. The collected information per time interval for the main outcome parameters can be compared to the amount expected.

The European Commission’s risk-proportionate approaches in clinical trials, the EMA Reflection Paper on risk-based quality management in clinical trials, the GVP Module III on pharmacovigilance inspections and national regulations should be consulted on these aspects (as per Section 3.7 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Data Analysis

The analytical approach to the outcomes of interest should be pre-specified in the registry-based study protocol and the SAP as applicable. Changes to the pre-specified statistical analysis should be reflected by an amendment to the study protocol and/or by an amendment to the SAP. All changes should be presented in the study report (as per Section 3.8 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

For non-interventional studies, the ENCePP Guide on Methodological Standards in Pharmacoepidemiology presents methods to address bias and adjust for confounding (as per Section 3.8 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Depending on the objectives of the registry-based study, the data analysis may need to include an evaluation of the representativeness of the study population in relation to the source population, as it may influence the external validity of the registry-based study. In case of primary data collection, a comparison of available data between eligible registry patients who are recruited, who decline recruitment or who withdraw from the study and between patients randomised and not randomised in the study, should be performed. If possible, this should be supplemented by a comparison of the study population with a similar population identified from scientific literature data, available electronic healthcare databases, other registries deemed suitable for the study but not used for data collection as justified in the study protocol, or other population-based data sources (as per Section 3.8 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Missing data may lead to bias and confounding, and their handling should be carefully described in the study protocol and the SAP. A thorough justification should be provided for the assumptions about their distribution, causes and timing. The ENCePP Guide on Methodological Standards in Pharmacoepidemiology provides guidance on how to handle missing data (as per Section 3.8 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

In the absence of randomised treatment allocation in registry-based non-interventional studies, some common analytical issues should be addressed (as per Section 3.8 of the EMA – Guideline on Registry-Based Studies, October 2021) [1]:

    • The characteristics of patient groups prescribed different treatments are likely to differ. Treatment decisions may be influenced by various factors that may also be associated with the risk of occurrence of the outcome of interest, such as disease severity or the monitoring practice of patients. While methods for addressing this underlying problem have been proposed, these do not provide a unique solution and several sensitivity analyses using different approaches should be performed. In addition, ascertainment of marginal treatment effects over time and factors underlying treatment trajectories may require complete collection of information over the course of the study.
    • Registries and registry-based studies may involve different time points for patient inclusion and follow-up, initiation of treatments of interest and ascertainment of events and other variables. The probability of occurrence of events of interest may also be time-dependent. These time points are important to consider as they affect the comparability between treatment groups. Graphical representation of the analysis plan should be used to help understand the various time components of the study and the registry. When investigating a treatment effect, immortal time bias can occur when the follow-up period for the study starts before initiation of the treatment under study and the period between start of follow-up and start of treatment is misclassified as exposed.
    • Selection bias, information bias and time-related bias may also occur in comparisons to historical control groups. The clinical context may have changed with regard to e.g., treatment options, diagnosis, medical practice in choice of treatments according to severity of disease, patient care, secular trends in the occurrence of important events, completeness of data collection or other uncollected or unknown factors. These sources of bias should be identified and the impact on the validity of the results assessed.
    • A comparative non-exposed control group may be selected from outside the registry, for example from another registry or electronic healthcare records in a country/region where the medicine has not yet been marketed. In this situation, one should ensure that underlying differences between the two populations influencing the risk of outcome occurrence are adequately measured and accounted for in the analysis. Since it may not be possible to identify all underlying differences between populations and completeness of data collection may differ, such comparisons need to be interpreted cautiously.
    • Registries offer the opportunity to compare patients prescribed a treatment of interest with patients who are untreated or who have received a different treatment(s) over a long period of time. Inclusion of prevalent medicine users (i.e., patients already treated for some time before study follow-up begins) can introduce two types of bias. Firstly, prevalent medicine users are “survivors” of the early period of treatment, which can introduce substantial (selection) bias if the risk for adverse reactions varies with time (e.g., if treatments carry a risk of hypersensitivity reactions or affect cardiovascular risk). Secondly, covariates influencing medicine prescription at study entry (e.g., disease severity) may be affected by previous medicine use, or patients may differ regarding health-related behaviours (e.g. healthy user effect). A new user design reduces these biases by restricting the analysis to incident medicine users, i.e., patients who enter the study cohort only at the start of the first course of the treatment(s) of interest during the study period. The disadvantages of a new-user design may be a lower sample size and a lower number of patients with long-term exposure, which may then require to extend the duration of the study.
    • In the context of the new user design, use of an active comparator may reduce confounding by indication or disease severity as a comparison is made between patients with the same indication initiating different treatments. With newly marketed medicines, however, an active comparator with ideal comparability of patients’ characteristics may often be unavailable because newly marketed medicines are often strictly prescribed according to patients’ prognostic characteristics and reimbursement considerations, which leads to channelling bias.

Data Reporting

National and EU obligations and reporting requirements for non-interventional studies should be followed (as per Section 3.9 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

The methods used in the study should be published with sufficient details, while protecting patient privacy, to allow for replication using the same registry database or using a database derived from another registry collecting similar data. Relevant guidelines on reporting of results from non-interventional studies are presented in the Good Pharmacovigilance Practices Module VIII and the ENCePP Guide on Methodological Standards in Pharmacoepidemiology (as per Section 3.9 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Post-authorisation registry-based non-interventional studies should be registered in the EU PAS Register with the study protocol, the SAP if applicable and the final study report. The final report must contain all study results derived from the analyses prespecified in the study protocol and SAP, whether favourable or unfavourable. The analytical code as well as any prior feasibility analyses are ideally also made available. A summary in lay language of the main results and conclusions of the final study report should be prepared and distributed to the registry participants in collaboration with the registry holder (as per Section 3.9 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

For non-interventional studies, the principles of scientific independence and transparency for reporting study results described in the ENCePP Code of Conduct and the ADVANCE Code of Conduct for vaccines should be followed. The responsibility for preparing the final study report lies at the appropriate level of study governance, e.g., medical/scientific advisory board, principal investigator and local registry investigators in studies based on multiple registries. For studies funded by a MAA/MAH and requested by a regulatory authority, all parties involved should be responsible for ensuring that the study meets the regulatory requirements of the competent authority and the MAA/MAH should be

able to comment on the study results and their interpretation as well as on the format of the report.  Requests by the MAA/MAH that interpretation of the results or their presentation be changed should be based on sound scientific reasons or documented regulatory requirements (as per Section 3.9 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Following the submission of the final study report, the competent authority may request additional information and clarifications from the MAA/MAH or may initiate an inspection. Therefore, if a research contract is signed between the MAA/MAH and the registry holder, the contract should include a requirement for the registry holder to address the scientific aspects of the request, with the possibility for the MAA/MAH to provide comments, as well as a requirement to allow a possible regulatory inspection of the registry-based study (as per Section 3.9 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Legal Obligations and Regulatory Requirements for Registry-Based Studies

The following table summarises the legal basis and regulatory requirements applicable to MAAs/MAHs for different activities related to registry-based studies (as per Section 4 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].


Good Registry Practice (GRP) – Considerations for Patient Registries


Quality Management – Framework for Quality Management

Uncertainties about the quality of the data collected in registries may undermine the confidence in the validity and reliability of the evidence generated from registry data in registry-based studies. The Commission Implementing Regulation (EU) No 520/2012 and GVP Module I provide a quality framework for MAHs, competent authorities of Member States and the EMA. Measurable quality requirements can be achieved by (as per Section A.4.1 of the Annex of the EMA – Guideline on Registry-Based Studies, October 2021) [1]:

    • Quality planning: establishing structures (including validated computerised systems) and planning integrated and consistent processes
    • Quality assurance and control: monitoring and evaluating how effectively the structures and processes have been established and how effectively the processes are being carried out
    • Quality improvement: correcting and improving the structures and processes where necessary.

These quality management activities (“plan, do, check, act”) should be done in a continuous manner throughout the lifetime of the registry and be regularly assessed. They should be made available to patients, health care professionals and potential users of the registry data to provide confidence that quality management is adequately performed. Responsibilities should be clearly defined to enable sustainability of the quality management system (as per Section A.4.1 of the Annex of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Data security should be part of quality management. Use of an existing patient registry for a new purpose, such as a registry-based study, may require availability of predefined data elements for specific users (e.g. users who perform data entry, management, quality control, extraction or analysis) but not necessarily all registry data. Specific measures (e.g., fire walls, log-in codes or access rights) may therefore need to be in place or introduced in the registry system when needed for some categories of users. Traceability (i.e., the possibility to trace changes made to patient data in the registry and who made these changes) should be part of the data security measures (as per Section A.4.1 of the Annex of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Quality Management – Requirements for Data Quality

In this context, data quality includes four main components (as per Section A.4.2 of the Annex of the EMA – Guideline on Registry-Based Studies, October 2021) [1]:

    • Consistency: the formats and definitions of the variables are consistent over time, across all centres within a registry and across all registries within a network of registries
    • Completeness: patient enrolment is maximised, patient attrition is minimised and complete information on a core data set is recorded for all eligible patients with minimisation of missing data
    • Accuracy: the data available in the registry is a correct representation of patient information available to the health care professional, e.g., data available in medical charts or laboratory test results; where the registry data are a compilation or duplication of electronic medical records at the point of care, accuracy should rely on a check of the extraction and uploading procedure
    • Timeliness: there is a timely recording and reporting of data and data updates, based on their intended use in compliance with an agreed procedure.

Requirements of data quality may be difficult to achieve concomitantly in all centres within a registry or within all registries of a network of registries; implementation of the same data elements, terminologies, data entry procedures and data control software may not be feasible simultaneously in all centres. Intermediate solutions may be adopted focussing on a core data set and mapping procedures. Centres may progressively implement components of data quality and be included in the registry or network of registries once they have achieved an adequate level of data quality as agreed between the concerned parties according to the data needs (as per Section A.4.2 of the Annex of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Quality Management – Key Performance Indicators of Data Quality

Registries should use performance indicators to assess and drive improvement of data quality. Such indicators should be measurable and associated with remedial measures if acceptable levels of quality are not found. Their definition depends on the disease, governance, infrastructure, local health system and processes in place within the registry or network of registries. They should therefore be defined in a multi-disciplinary approach with all concerned parties. Examples of agreed key performance indicators of data quality are presented in the reports of the EMA workshops on cystic fibrosis registries, multiple sclerosis registries and CAR T-cell Therapy Registries (as per Section A.4.3 of the Annex of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Examples of Agreed Key Performance Indicators of Data Quality

Table References: EBMT and CIBMTR Registries: ;   Haemophilia Registries: ; Multiple Sclerosis Registries: ; Cystic Fibrosis Registries:

Quality Management – Data Quality Management Activities

Quality management can be supported by the activities described below. These activities should take into account appropriate technical and organisational measures to be implemented to ensure a sufficient level of security when personal data and more specifically health data is processed. Such measures should at least consist of pseudonymisation, encryption, non-disclosure agreements, strict access role distribution, access role restrictions as well as access logs. National provisions, which may stipulate specific technical requirements or other safeguards such as adherence to professional secrecy rules should be also taken into account (as per Section A.4.4 of the Annex of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Given the variety in the organisation and infrastructure of registries, these recommendations should be adapted to each situation (as per Section A.4.4 of the Annex of the EMA – Guideline on Registry-Based Studies, October 2021) [1]:

    • KPIs and SOPs – Data quality management activities should be documented, communicated, maintained and updated as necessary, and all relevant source documents should be kept, managed and made available for auditing purposes in a timely manner, including:
        • Standard Operating Procedures (SOPs), steps of data quality management from data planning to reporting, with data management responsibilities
        • Key Performance Indicators (KPIs) of data quality, planned and performed data checks (manual or automated) and cleaning processes including query management and on-site monitoring.
    • Support Tools – Should be developed and provided, e.g., data collection and reporting software, support function (helpdesk), training material and training sessions. A centralised remote electronic quality control could be set-up to limit on-site visits to be done according to a predefined risk approach.
    • Appropriately Qualified and Trained Staff – Appropriate qualification and training of data managers and other persons involved in the data collection process should be ensured, with knowledge about the disease, exposures and outcomes captured in the registry.
    • Routine Data Quality Checks – In case of a local data extraction process or manual data entry, routine data quality checks should be performed to alert on erroneous, missing or out-of-range values and logical inconsistencies, and trigger prompt data verification and remedial measure if needed. The validity of any data cleaning, extraction and transformation processes should be documented, especially if it involves mapping of data to a common terminology.
    • Internal or External Audits – Internal or external audits with on-site review of processes and data audits should be performed according to a risk-based approach; remote quality control measures, targeted visits and targeted source data verification should be triggered by pre-defined thresholds of data quality measures.
    • Data Verification – The minimum amount of data verification required may depend on the amount of data collected and should ideally take into account critical aspects of data collection where differences may occur, e.g., between individual centres or between persons within individual centres.
    • External Comparisons of Aggregated Registry Data – Aggregated registry data should ideally be compared to literature data or data from external data sources such as electronic health records or insurance claims databases as regards the distribution of categories of important variables such as age, gender, factors associated with disease occurrence or severity, or drug exposure.
    • Feedback on Data Quality Issues – Feedback on findings on data quality issues should be given systematically to data providers so that escalation and remedial action can be taken at the level of the data source.
    • Corrective and Preventative Activities (CAPAs) – When considering implementation of corrective and preventive activities, additional workload for data collection and data entry should be addressed, as a cumbersome data entry process may increase the amount of missing data and decrease data quality.


Registries generally operate under governance principles influenced by their purpose, operating procedures, legal environment or funding sources (55). Different parties may potentially also have divergent priorities, such as scientific independence, fulfilment of regulatory commitments, transparency or intellectual property rights. Clear governance principles supporting effective collaborations between all parties for regulatory use of registries, including data sharing between stakeholders, are therefore useful (as per Section A.5 of the Annex of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Registry holders should consider the following aspects to ensure transparency, best use and

sustainability of their registry(as per Section A.5 of the Annex of the EMA – Guideline on Registry-Based Studies, October 2021) [1]:

    • To publish documentation of key registry characteristics, such as purpose of the registry, inclusion and exclusion criteria for participating centres and enrolment of patients, core and optional data sets collected (with timelines and frequency of data uploads), quality management process and experience of previous collaborations; the registry should be registered in the ENCePP Resources Databases.
    • To establish a governance structure for the management of the registry and registry-based studies, with a steering committee, ethics committee and scientific advisory board.
    • To establish a single contact point within the registry or network of registries for requesting information on available data and data access conditions.
    • To publish a policy for collaborations with external organisations, including information on the scope and decision-making process for participating in collaborations, policy for data sharing and data analysis (explaining possible options for data transfer and analysis based on data privacy rules in place), possible involvement of a third-party, publication policy, and principles for private and public funding.
    • To provide a supportive scientific and technical function for collaborations, which may include support for the development of the study protocol, interoperability between registries, amendments to the scope, schedule or methods of data collection or extraction, data management and analysis; the support provided may vary according to the approach of collaboration for using multiple data sources (see the ENCePP Guide on Methodological Standards in Pharmacoepidemiology), resources available in the registry and the contractual agreements proposed.
    • To develop a template for research contracts between the registry and external organisations, in line with those recommended by the ENCePP Code of Conduct or the ADVANCE Code of Conduct.

Data Sharing Outside the Context of Registry-Based Studies – Contractual Considerations

There may be situations where registry data could be shared outside the context of formal registry-based studies in the format of counts, aggregated data or statistical reports with NCAs, EMA, MAAs/MAHs, HTA bodies, payer organisations or other parties for clinical development planning or the evaluation or monitoring of medicinal products. These data may concern, for example (as per Section A.6 of the Annex of the EMA – Guideline on Registry-Based Studies, October 2021) [1]:

    • Disease epidemiology in terms of prevalence, incidence, outcomes, prognostic factors, potential confounding variables for defined outcomes
    • Size and distribution of the population with a specific disease, condition or exposure for a planned clinical trial or non-interventional study according to demographics, co-morbidities or medication use
    • Drug utilisation, with number of prescriptions for specific medicinal products (or other indicator of intensity of exposure), indications, dose, route of administration, schedule, duration of use, co-medications or use in specific population groups such as extent of paediatric use
    • Medical device utilisation, with number, types, indications and dates for specific implanted products
    • Surgical procedures with numbers, types, indications, dates and any other relevant details
    • Safety information on medicinal products, for example summary tables of adverse events recorded for specific medicinal products, aggregated data or anonymised line listings of patients presenting AESIs, or outcomes of exposed pregnancies
    • Utilisation of health care resources such as number of visits, hospitalisations, or laboratory tests performed.

This information may require capacity for sound analysis within the registry or, if allowed by the registry governance and patient consent, transfer of an anonymised dataset with selected variables to the requester or a third-party performing the analysis on behalf of the registry or the requester. Data sharing may require a contractual agreement between the registry or network of registries and the other concerned parties (as per Section A.6 of the Annex of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Checklist for Evaluating the Suitability of Registries for Registry-Based Studies

(Source: Appendix 1 of the EMA – Guideline on Registry-Based Studies, October 2021; List adapted from the REQuEST tool published by EUnetHTA)





Patient Registry (synonym: registry)

Organised system that collects uniform data (clinical and other) to identify specified outcomes for a population defined by a particular disease, condition or exposure. The term ‘patient’ highlights the focus of the registry on health information. It is broadly defined and may include patients with a certain disease, pregnant or lactating women or individuals presenting with another condition such as a birth defect or a molecular or genomic feature (as per the Glossary of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Disease Registry

Patient registry whose members are defined by a particular disease or disease-related patient characteristic regardless of exposure to any medicinal product, other treatment or particular health service (as per the Glossary of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Product Registry

The term product registry is sometimes used to indicate a system of data collection by marketing authorisation applicants and holders (MAAs/MAHs) targeting patients exposed to a specific medicinal product or substance. From a regulatory perspective, recruitment and follow-up of these patients with the aim to evaluate the use, safety, effectiveness or another outcome of this exposure typically falls outside of normal routine follow-up of patients and therefore corresponds to a clinical trial or non-interventional study in the targeted population. It is therefore preferable to avoid using the term “product registry” in this situation and directly refer to the appropriate terminology instead (clinical trial or non-interventional study) (as per Section 2 of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Registry-Based Study

Investigation of a research question using the data collection infrastructure or patient population of one or several patient registries. A registry-based study is either a clinical trial or a non-interventional study. A registry-based study may apply primary data collection in addition to secondary use of the existing data in the registry (as per the Glossary of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Registry Database (synonym: register)

Database derived from one or several registries (as per the Glossary of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Primary Data Collection

Collection of data directly from patients, caregivers, healthcare

professionals or other persons involved in patient care (as per the Glossary of the EMA – Guideline on Registry-Based Studies, October 2021) [1].

Secondary Use of Data

Use of existing data for a different purpose than the one for which it was originally collected (as per the Glossary of the EMA – Guideline on Registry-Based Studies, October 2021) [1].


1. EMA – Guideline on Registry-Based Studies (October 2021)


2. Draft FDA Guidance – Real-World Data: Assessing Registries to Support Regulatory Decision-Making for Drug and Biological Products Guidance for Industry (November 2021)


3. EUnetHTA – REQueST Tool and its vision paper (September 2019)


Quality Considerations when Using RWD from Registries to Support Regulatory Decisions in the EU2022-08-07T16:37:58+00:00
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