Testing preference elicitation methods in clinical case studies

Our researchers will test different methods for preference elicitation in clinical case studies. We will evaluate what patients think is relevant about their disease and its impact. We will look at which treatment options they prefer and their willingness to accept trade-offs between benefits and risks of their treatment. These methods will be evaluated at different decision points in the drug development process.

We are running clinical patient preference case studies in three disease areas, involving both patient partners and clinical research partners in the PREFER project: lung cancer, rheumatoid arthritis and neuromuscular disorders. PhD students working in the project and partners from the pharmaceutical industry provide an additional eight more patient preference studies covering preferences for different kinds of treatment for haemophilia, rheumatoid arthritis, diabetes, multiple myeloma, chronic obstructive pulmonary disease, chronic pain and myocardial infarction. We are also using eye-tracking techniques to see how patients respond to a preference study using a methodology called discrete choice experiments (DCE).

Want to learn more? have a look at our case study catalogue. And if you want to know more about how we use different terms in PREFER, have a look at our glossary. We are also making an effort to summarise case study results in plain language. 

The PREFER project is running three large clinical patient preference studies in three disease areas: Lung cancer, neuromuscular disorders and rheumatoid arthritis.

PREFER partners are contributing data from patient preference studies they have conducted outside the framework of the project. In addition, our PhD students are running their own preference studies as part of their thesis work. Alongside, some of the pharmaceutical companies involved in the project are doing their own studies to explore PREFER research questions. Together, these eight contributions from our partners will contribute to robust and evidence-based recommendations!