Presenters: dr. F. Thielen; dr. B. Wijnen Dates: 21 september 2026 28 september 2026 12 oktober 2026 26 oktober 2026 PhD students are eligible for a reduced tariff. Requirements: Laptop with R and R-studio pre-installed Right to install R-packages on laptop Dataset with costs and effects (optional) This four-session (full days) programme provides hands-on training in conducting trial-based economic evaluations using R, with a focus on the practical challenges of mental health data. Participants will work through the full analytical workflow—from raw data to reporting results—combining theory with applied exercises and opportunities to use their own datasets.
Participants are preferably expected to bring their own datasets, which they will analyse over the duration of the course (although example datasets are available). Session 1 – Foundations and Data Preparation The programme starts with the theoretical foundations of trial-based economic evaluation, including perspectives, costing approaches, and outcome measurement. Participants will receive an overview of commonly used health economic questionnaires and required datasets. The session introduces working with R for HTA and covers essential steps in data cleaning and merging to create analysis-ready datasets. Session 2 – Outcomes, Costs, and Missing Data This session focuses on analysing key health economic measures, including EQ-5D for health-related quality of life and iMCQ, iPCQ, and TIC-P for healthcare utilisation and productivity losses. Participants will learn how to structure and prepare data for analysis and explore practical approaches to handling missing data in trial-based studies. Session 3 – Modelling Incremental Effects Participants will learn how to specify imputation models and estimate incremental costs and effects using regression-based approaches. The session covers combining bootstrapping with regression, incorporating baseline adjustments, and understanding uncertainty around estimates. Session 4 – Interpretation, Reporting, and Dissemination The final session addresses sensitivity analyses and reporting standards, including cost-effectiveness planes, CEACs, cotton-candy plots, incremental net monetary benefit (iNMB), and ICERs. Participants will also learn how to present and disseminate results using Quarto. The programme concludes with participants presenting their own work and receiving structured feedback.
Day-by-day program Day 1 – Foundations & Data Preparation Theoretical basis of trial-based economic evaluation Overview of required datasets and commonly used questionnaires (e.g. EQ-5D, iMCQ, iPCQ, TIC-P) Introduction to working with R for HTA Common challenges in data cleaning, restructuring, and merging Working with the Dutch guidelines (and the Tattoheene R-package) Day 2 – Outcomes, Costs & Missing Data Analysis of EQ-5D and derivation of QALYs Analysis of iMCQ, iPCQ, and TIC-P cost data Preparing datasets for economic analysis Practical approaches to handling missing data Day 3 – Estimating Incremental Effects Specifying imputation models Regression-based estimation of incremental costs and effects Combining bootstrapping with regression Incorporating baseline adjustments Quantifying uncertainty Day 4 – Interpretation, Reporting & Dissemination Sensitivity analyses Reporting results: CE planes, CEACs, cotton-candy plots, ICERs, iNMB Presenting and disseminating analyses using Quarto Participants present their own work and receive structured feedback
This course is aimed primarily at PhD students and other scientifically oriented staff (e.g., postdoctoral researchers, research assistants, or health economists) who are interested in conducting trial-based economic evaluations. Participants are expected to have an interest in health economic methods and applied research. Some prior experience with R is desirable, as R will be used throughout the course for hands-on analyses; however, important R skills will be covered at the start of the course, making it accessible to participants with limited prior exposure.
