Using R for Decision Modeling in Health Technology Assessment [EL005]
Petros Pechlivanoglou, PhD, Eline Krijkamp, PhD
To benefit from this course students should have:
- Basic probability and statistics knowledge AND
- Some experience with R coding (has done NIHES courses using R (CK020, courses of Dr. Rizopoulos using R) or online courses like DataCamp) or significant experience with coding in other programming languages, AND
- Successfully passed an advanced course in decision modeling:
- EL004: Advanced Topics in Decision Making in Medicine
- Harvard T.C. Chan School of Public health courses RDS202, RDS280, RDS286
- Faculty approval of an equivalent course
- Recommended but not compulsory: Using R for Statistics in Medical Research (CK020)
- Clinical Epidemiology
Detailed information about this course:
This course aims to teach how to build decision models in R to students who have an understanding of health decision science.
- The course combines lectures with R coding exercise.
- The course is project-based. You are expected to apply the theory and skills you learn during this course to a decision problem you select yourself.
- The assignments in this course are group work.
Important: this course has a pre-course preparation module with a workload of 4-10 hours, which you must finalize prior to the course.
More detailed information about each session will be provided in the syllabus.
Attendance of all lectures and practicums is compulsory in order to be able to complete the assignments and case example successfully. Each day builds on knowledge and skills from the previous day. Clarification of the material taught is best done in the interactive teaching environment provided during classroom sessions.
See 'how to apply' for the course registration period.
By the end of the course the student will be able to
- Create decision models using R (decision tree/cohort/microsimulation models)
- Understand the advantages and disadvantages of building decision models in R
- Apply good coding practice in R
- Create transparent and readable decision models
- Apply the methods learned in real-life practical examples
Students (Msc/PhD level) or researchers in decision science, health economics, clinical sciences, clinical epidemiology, public health, health technology assessment or value-based healthcare.
Reduction on fees
PLEASE NOTE: This does not apply to the fee of the research master programmes (120 EC points)
No fees are charged for Erasmus MC PhD candidates, provided they have an account in Hora Finita, the Erasmus University PhD registration system. When submitting the application, you can state that someone else pays your tuition fee.
In case of cancellation or no show, the cancellation policy applies based on the full course fee.
25% reduction for all (international) PhD candidates without formal appointment at Erasmus MC
Upon receipt of your application you will receive a request to upload proof of enrollment as a PhD candidate.