The main objectives of this module are:

 

After completion of the course, students are able to

1. define the basic principles and good practices of decision modelling in health care
2. define the different types of decision models and reasons why a certain model is appropriat
3. conceptualize and explain a decision model
4. make proper assumptions regarding data and the model structure
5. define how the influence of those assumptions can be tested (scenario analyses and sensitivity analyses)
6. perform a probabilistic sensitivity analysis
7. perform different forms of scenario analysis
8. explain the basic principles of a Value of Information (VOI) analysis
9. explain the basic principles of a headroom analysis
10. explain the basic principles of a Budget Impact Analysis (BIA)
11. apply model validation in the context of economic evaluations

The module 

Decision analytic modelling is widely used as a framework for structuring clinical and economic outcomes of healthcare innovations for decision makers. This allows these decision makers, which can be for example patients, clinicians or policy makers, to prioritize those innovations that provide most value for money. In this course students will learn to conceptualize, build, analyze, interpret and validate such decision models. The decisions that are informed by a decision model are always surrounded by uncertainty. As part of the course students will learn how to incorporate parameter within their evaluations by using for example scenarios or probabilistic sensitivity analysis. Students will also learn how to evaluate the potential value of new technologies in a very early stage of their development, when hardly any evidence is available on the effectiveness of this technology. In orde to quantify the expected cost of the uncertainties in a model, the basic concepts of Value of Information (VOI) analysis, including the concept of Expected Value of Perfect Information (EVPI), will be discussed. Whether decision models are used in the decision making process depends on two critical factors: trust and confidence. Two main methods for achieving this will be discussed in the course: transparency (people can see how the model is built) and validation (how well the model reproduces reality).
Finally, as part of the evaluation of health care innovations, decision makers are increasingly demanding budget impact analysis (BIA) to evaluate the expected changes of the health care budget by adopting the new innovation. Students therefore will learn in this course some basic principles how decision modelling can be used to perform a BIA.
At the end of this course students are able to use different advanced modelling techniques for (economic) evaluations of health care innovations. The course is therefore particular useful for students who wish to extent their basic modeling techniques and want to learn how to use more advanced methods in economic evaluations. This will prepare the students for working in the field o health economics, for example within pharmaceutical companies, consultancy firms or HTA bodies. The course is assessed through one group assignment (50%), and an individual exam (50%). The group assignment tests the practical skills of the students. The exam is based on the core text books of Drummond et al., 2015, 4th edition and Briggs et al., 2006, 1st edition.