Using R for Decision Modeling in Health Technology Assessment
[ EL005 ]

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.

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

Assignment(s), Compulsory attendance

T.B.A.

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)