This 3-day course (taught over 3 individual days, spaced over a 2 week period) will be online with roughly 50% lectures that will explain theory and demonstrate coding, and 50% practicals that give the participants exercises to implement what they have been shown. Lectures and practicals will be delivered live.

By the end of the course participants should be able to:

1. program in the R statistical language;
2. build a decision tree model in R;
3. build a Markov model in R;
4. build a semi-Markov model in R;
5. incorporate uncertainty in model inputs in an economic model; and
6. conduct a value of information analysis.

This course is intended for anyone undertaking model based cost-effectiveness analyses. We welcome attendees from academia, government, or industry.

This course will cover:

Day 1 [14 July 2025]

1. introduction to R using health economic examples;
2. decision trees (deterministic and probabilistic); and
3. advanced topics in R (program flow, input/output, functions).

Day 2 [16 July 2025]

1. decision trees (building your own model in R from scratch);
2. basic Markov models; and
3. advanced Markov models (more states, more treatments, modularised code).

Day 3 [21 July 2025]

1. semi-Markov models with application to oncology; and
2. value of information analysis.

The co-leads Dr Howard Thom and Dr Mary Ward are experts on model development and value of information analysis in R. Dr Thom has developed dozens of models in R, including decision tree, Markov, and semi-Markov models. Professor Nicky Welton provides further expertise on value of information analysis.

To make sure the course is suitable for you and you will benefit from attending, please ensure you meet the following prerequisites before booking:

Before booking this course, please make sure you read the information provided above about the target audience and prerequisites. It is important that you have access to the relevant IT resources needed for the course and meet the knowledge prerequisites to ensure you can get the most from the course.

Bookings are taken via our online booking system, for which you must register an account. To check if you are eligible for free or discounted courses please see our fees and voucher packs page. All bookings are subject to our terms & conditions, which can be read in full here.

For help and support with booking a course refer to our booking information page, FAQs or feel free to contact us directly. For available payment options please see: How to pay your short course fees.

Participants are granted access to our virtual learning platform (Blackboard) 1 to 2 weeks in advance of the course. This allows time for any pre-course work to be completed and to familiarise with the platform.

To gain the most from the course, we recommend that you attend in full and participate in all interactive components. We endeavour to record all live lecture sessions and upload these to the online learning environment within 24 hours. This allows course participants to review these sessions at leisure and revisit them multiple times. Please note that we do not record breakout sessions.

All course participants retain access to the online learning materials and recordings for 3 months after the course. 

University of Bristol staff and postgraduate students who do not wish to attend the full course may instead register for access to the 'Materials & Recordings' version of this course: Further information and bookings.

100% of attendees recommend this course*.
*Attendee feedback from 2024.

Here is a sample of feedback from the last run of the course:

"A really well structured course with loads of great resources." - Course feedback, July 2024

"The walk through nature of the exercises with clear explanation of the coding arguments was very helpful. Having my own versions of the scripts to annotate and use in real time was great." - Course feedback, July 2024

"This course was excellently done. Fast pace. Following the code was done well. Very well organised." - Course feedback, July 2024

"The course demystified the use of R in economic modelling. I think that I am now in a way better position to follow R scripts and be able to use them in my day-to-day role within an EAG." - Course feedback, July 2024

"Talking through the logic around the coding of decision models in R was great for understanding and being given essentially templates for taking our own learning forward was great. I also found the session on packages really helpful for improving project and coding hygiene." - Course feedback, July 2024