At the end of the course, you will be able to:
- Describe the medicine development chain and perspectives of various stakeholders on this chain
- Explain the factors that may affect the effectiveness of a new medicine in real life
- Explain existing designs for efficacy and effectiveness studies and how to evaluate those
- Explain the relationship between the study design choices of a relative effectiveness study and the value of information for marketing authorization at HTA
- Describe the operational challenges (including ethical, regulatory and legal aspects) of relative effectiveness studies
- Describe the existing current methods in evidence synthesis and the methods that can be applied for predictive medicine effectiveness modeling (incorporating real-world evidence)
- Understand how to integrate thinking about real-world evidence into medicine development and decision-making
- Communicate policy options around relative effectiveness, real-world evidence and various study designs
LU1 - The Medicine Development landscape
In LU1 the following subjects are covered:
- The medicine development process;
- Changes in the medicine development chain (and the environment thereof) that triggered an interest in real-world evidence;
- Roles of stakeholders in and societal forces on evidence requirements.
Decisions about development, authorisation and access are made under conditions of uncertainty and based on limited evidence. This is problematic because treatment choices for patients become suboptimal and guideline development is hindered. In this Learning Unit it will be discussed how real-world evidence could contribute to addressing this challenge.
After completing this Learning Unit you will be able to understand the key components of the medicine development process and the roles and perspectives of various stakeholders in this chain.
LU2 – Real-World Evidence Generation
Why real-world evidence is needed has been introduced in Learning Unit 1: a gap exists between the evidence observed in randomized controlled trials looking for the true biological effect (efficacy) of a drug and the effect observed (later) in real life (effectiveness). In this Learning Unit we will look in more detail into how we define real-world evidence. We will also dive deeper into the factors that explain the possible difference between efficacy and effectiveness: the drivers of effectiveness.
We will talk about the methods to capture real-world evidence through observational studies or pragmatic clinical trials, or a combination of both.
The last part of this Learning Unit will focus on the conduct of pragmatic trials, in which (unanticipated) operational challenges are often encountered due to the limited experience with these types of trials (unanticipated). These challenges may have important implications on the feasibility of the trial and the interpretation of the results. Examples of such operational challenges will be given, and the interplay between the design choices, their implications and operational challenges will be discussed.
We will finish the Learning Unit challenging you to design some elements of a pragmatic trial yourself.
After completing this learning unit you will be able to:
- Name which main study designs exist to generate real world evidence and explain key characteristics
- Explain the importance of knowing which are the drivers of effectiveness in real life
- Explain which are the main study design choices for a trial influencing whether you answer a relative effectiveness or an efficacy question
- Recognize the operational challenges involved in pragmatic trial
- Explain the interplay between design choices for a pragmatic trial, operational feasibility and the implications on the interpretation of the trial
LU3 - Evidence Synthesis including Real-World Data and Predicting Effectiveness
Syntheses of evidence from individual source studies, by means of systematic reviews, meta-analyses, and multiple treatment comparisons and prediction models are important tools to generate effectiveness estimates of medicines. These tools are used by pharmaceutical companies, HTA, reimbursement and regulatory agencies, healthcare professionals, guideline developers, and patients to inform decisions related to the generalisability of efficacy results from randomised controlled trials (RCTs).
In this Learning Unit you will be collaborating with your fellow participants. Please keep this in mind when creating your own schedule for this week.
After completing this Learning Unit you will be able to:
- understand the basic concepts and methods in evidence synthesis and predicting effectiveness;
- critically assess existing applications of evidence synthesis and predict effectiveness; and
- understand the advantages and challenges of incorporating real-world data into evidence synthesis.
LU4 - Decision-Making and Weighing Evidence
In this Learning Unit you will get familiar with the final tools to analyse real-life data and apply the knowledge you have gained so far.
After completing this Learning Unit you will be able to:
- describe the state of the art in decision-making for medicine authorisation purposes;
- summarise the available clinical trial data and their associated uncertainties in a format suitable for benefit-risk assessment;
- explain what multi-criteria decision analysis (MCDA) is and how it can be used to make the value judgments underlying the benefit-risk assessment explicit; and
- explain what patient preference studies are and how they can inform medicine development and regulatory decision-making.
LU5 – Developing Relative Effectiveness strategies for Decision-Makers
The overall aim of this Learning Unit is to focus on the evidence requirements of (regulatory and reimbursement) decision makers, and to apply what we have learned in the previous Learning Units to a new medicine, for which evidence of efficacy, safety and effectiveness will be presented to these decision-makers.
After completing this Learning Unit, you will be able to:
- Describe the role, perspectives and requirements of key decision makers in medicine development;
- Describe alternative medicine development programmes and their rationale, in particular those making (more) use of real-world data (RWD);
- Understand trade-offs required to meet the needs of different decision makers in medicine development: Pharma R&D, regulatory agencies, reimbursement agencies.
In the first part of this Learning Unit (LU5.1-5.5) you will learn about:
- The changing regulatory and reimbursement environment
- Challenges that may be faced in demonstrating relative effectiveness
- Tools available from the GetReal project that can help assess requirements and design studies and analyses
In the second part of this Learning Unit (LU5.6-5.8) you will apply what you have learned in a team exercise to consider how real-world data can be used to help improve an evidence plan for a (simulated) medicine being developed by a pharmaceutical company.
The Learning Unit will conclude with a teleconference between your team and faculty members, at which your proposals for using RWD to support this medicine will be discussed.