Real4Reg now offers an online self-paced training programme that introduces participants to the basics of using and analysing real-world data (RWD) and common data models (CDMs) in the context regulatory and health technology assessment (HTA) decision-making. Some good practices and practical applications are presented.
In 2024, Real4Reg conducted a survey regarding RWD and real-world evidence (RWE) use in regulatory affairs and HTA which results were later shared in the publication “Key Stakeholder’s Knowledge, Opinions, and Interests on Real-World Evidence in the Regulatory Process – Results of an EU-Wide Survey”. One of the main findings of this scientific article was that many stakeholders lacked education on RWD use and analyses for regulatory and HTA decision-making. Therefore, Real4Reg developed a training program useful for a wide range of stakeholders, including regulatory/HTA specialists, industry and academia representatives, payers, and patients.
Training Programme: Key Information
Total duration: Approximately 7 hours
Format: Online, self-paced, asynchronous
Number of Modules: 4
Modules recommended for Patients: Modules 1 and 3
Certificate: Get a certificate of completion for every module
Training Modules – Online, Self-Paced, and Asynchronous
The training program presents a modular structure and each participant can select the ones that they would like to take, according to personal preferences/individual background. The survey results helped in the design of the training structure and in the definition of the main goals, leading to four different modules, divided in eight sub-modules. Since the modules build on each other, the knowledge is acquired progressively.
The first module provides introductory information about RWE use in regulatory decision-making, while the second one focuses on the basics of CDMs. Module 3 provides good practice examples and in module 4 the participants will be able to apply the algorithms on a dedicated dataset.
In this training course, the participants will learn the basics of CDMs and how RWD can support regulatory decision-making, ensuring a better understanding by all stakeholders and a higher acceptance of these new practices by regulatory and HTA bodies.
Overview
Here is an overview of the contents, duration, prior knowledge required to understand the topics, and the learning objectives of each module:
Module 1 – Introduction to Real-World Evidence in Regulatory Decision-Making
Module 2 – Common Data Models With a Focus on OMOP
Module 2A – Motivation, Principles, and Characteristics
Module 2B – Hands-on Data Experience
Module 3 – Good Practice Examples of Real-World Data Analyses
Module 3A – Description of Study Populations
Module 3B – Creation of an External Control Arm and Synthetic Data
Module 3C – Descriptive Drug Utilisation Studies
Module 3D – Safety and Effectiveness Studies (Available in October 2026)
Module 4 – Tools for Real-World Data Analysis in Action (Available in December 2026)
The modules can be interrupted whenever the participants want and the progress made will be saved, allowing to later continue with the training program from the same point.
Module 1 – Introduction to Real-World Evidence in Regulatory Decision-Making
Recommended for Patients
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Duration: 50 minutes
Prior Knowledge Required: None
This is an introductory module that will give you the foundations about the use of RWD in regulatory and HTA decision-making. You will learn about key concepts, processes, terms related to, value, and limitations of the use of RWD in the assessment of treatments’ safety and effectiveness. In addition, emerging technologies that were used in Real4Reg, like artificial intelligence (AI) and machine learning (ML), will be introduced. The module’s main goal is to explain how RWD might support and strengthen these practices.
Learning Objectives:
- Describe regulatory and HTA decision-making processes and diverse needs;
- Understand RWD/RWE definitions and recognise the worth and shortcomings;
- Explain the concept of data quality;
- Describe the fundamentals of AI and ML in RWD analysis.
Module 2 – Common Data Models With a Focus on OMOP
This module provides an overview of CDMs. The Observational Medical Outcomes Partnership (OMOP) CDM used by Real4Reg serves as an example to explore the strengths, barriers, and potential ways to overcome problems that arise from the health data mapping of each country to a CDM, as they emerged through the project’s analyses. To summit it up, it presents the Real4Reg’s “lessons learnt” that might be helpful for other researchers.
Module 2A – Motivation, Principles, and Characteristics
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Duration: 30 minutes
Prior Knowledge Required: Familiarity with the terms – RWD, RWE, and clinical research
In this submodule, key aspects of CDMs will be explored, including their usefulness as data harmonisation tools, main principles, and advantages and disadvantages of different CDMs. OMOP serves as an example to look into these topics.
Learning Objectives:
- Explain the need and fundamentals of data harmonisation through CDMs;
- Understand the advantages and disadvantages of various CDMs;
- Understand the basic principles and characteristics of OMOP.
Module 2B – Hands-on Data Experience
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Duration: 20 minutes
Prior Knowledge Required: Familiarity with the concept of clinical classifications/vocabularies (e.g. ICD, SNOMED), vocabulary mapping, and basic informatics (e.g. RAM, secure processing environment)
This submodule focuses on the experience that Real4Reg had with the mapping of country-specific health data to OMOP, characterising concrete challenges and solutions found to overcome them. In addition, it also includes numerous considerations regarding the data processing step that ensure that OMOP’s standards are met.
Learning Objective:
- Explain barriers and ways to overcome the problems in the “OMOPing” process.
Module 3 – Good Practice Examples of Real-World Data Analyses
Recommended for Patients
This is a practical module that gives concrete examples of RWD analyses in areas that are relevant to regulatory and HTA specialists, increasing awareness about the potential. It has four submodules and presents some AI techniques that can be incorporated by regulatory and HTA decision-making. The good practice examples presented cover both the pre-authorisation and post-authorisation phases.
Module 3A – Description of Study Populations
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Duration: 30 minutes
Prior Knowledge Required: Familiarity with the terms – RWD, RWE, epidemiologic studies, incidence, prevalence, and tables/diagrams interpretation
This submodule focuses on presenting a good practice example for analyses in pre-authorisation phase, namely regarding the description of study populations with amyotrophic lateral sclerosis and breast cancer, as investigated in Real4Reg. The main goal is to understand the capacity of RWD sources like healthcare registers or claims data to provide high-quality and accessible information.
Learning objectives:
- Describe rationale and objectives of the good practice example presented;
- Examine descriptive results;
- Understand heterogeneity, value, challenges, learnings, and next steps of RWD use in pre-authorisation phase.
Module 3B – Creation of an External Control Arm and Synthetic Data
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Duration: 30 minutes
Prior Knowledge Required: Familiarity with the terms – RWD, RWE, and randomised controlled trial, Bayes’s rule, Bayesian networks, probabilistic graphical models, generative adversarial networks, diffusion models, variational autoencoder, supervised machine learning, area under the curve, distribution matching, and Pearson correlation
In this submodule, another good practice example regarding the pre-authorisation phase is presented. This time, the incorporation of AI and ML techniques to produce synthetic data and external control arms is explored. These practices might help in the clarification of questions that clinical trials usually cannot address, due to various reasons.
Learning Objectives:
- Understand the need and assess the credibility of external control arms and synthetic data in the pre-authorisation phase;
- Understand the generation and construction process of external control arms and synthetic data, respectively.
Module 3C – Descriptive Drug Utilisation Studies
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Duration: 40 minutes
Prior Knowledge Required: Familiarity with the terms – post-authorisation, RWD, RWE, incidence, and prevalence
This submodule aims to present a good practice example in regards to drug utilisation studies, which have a crucial role in the post-authorisation phase. The Real4Reg’s research topics – fluoroquinolones and SGLT2 inhibitors – are used to assess parameters like time trends of drug use, user characteristics or consequences of regulatory warnings.
Learning Objectives:
• Describe and explain time trends in drug use, user characteristics, and impact of regulatory warnings;
• Understand the impacts of data sources’ heterogeneity in drug utilisation studies;
• Explain how descriptive studies can be he helpful for observational studies regarding medicine’s safety and effectiveness.
Module 3D – Safety and Effectiveness Studies (Available in October 2026)
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Duration: 45 minutes
Prior Knowledge Required: Familiarity with the terms – post-authorisation, RWD, RWE, incidence, prevalence, randomised control trial, and cohort study
In this submodule, another good practice example that can be applied to the post-authorisation phase is presented. Here the basics of analyses regarding drug’s safety, effectiveness, and repurposing are explored. Target trial emulation and causal ML are presented as tools that might be applied to predict the risk of adverse drug reactions, both on an individual and population levels.
Learning Objectives:
- Comprehend the fundamentals of target trial emulation;
- Assess the possibility of predicting the risk of adverse drug reactions through target trial emulation and AI/ML methods;
- Judge the validity of the methodologies presented.
Module 4 – Tools for Real-World Data Analysis in Action (Available in December 2026)
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Duration: 3 hours (includes downloading the material, installation of Phyton and Visual Studio)
Prior Knowledge Required: Familiarity with the terms – AI, external control arm, tables and diagrams’ interpretation, programming language, randomised control trial, RWD, RWE, and single arm trial. In addition, completion of Module 3 is required to know and understand the algorithms that will be employed here.
In this module it will be possible to apply the algorithms, workflows, and methodologies presented in the previous modules and used in Real4Reg, to solve a question with scientific relevance. It will require the installation of Phyton and Visual Studio. This module will facilitate their modification to the participant’s needs, since there will be a deeper understanding of how they work.
Learning Objective:
- Find solutions for regulatory questions through innovative algorithms used in OMOP CDM.
Receive a certificate for each completed module
At the end of each module, the participants will be asked to complete a quiz with 5 questions each and if they correctly answer to at least four of them, they will receive a certificate of completion.
