This 3-day course 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

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

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

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.

96% of attendees recommend this course*.
*Attendee feedback from 2022-2023.

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

“Overall, the course was a great learning experience, providing with a strong foundation of using R in economic model”.
Course feedback, May 2023

“Lots of very good coding and modelling skills”.
Course feedback, May 2023

“The course was very interesting, relevant and useful. The tutors were all very knowledgeable, clear and approachable. I think the course ran over a good length of time to allow us to go into a suitable level of detail”.
Course feedback, May 2023

“I enjoyed the course a lot - the lectures were pitched right (for me at least) and the practical sessions were interesting in terms of showing how the models could be run in R”. 
Course feedback, May 2023

“I want to congratulations all the lecturers/tutors of the course. Agenda, materials and course structure were well designed and consistent”.
Course feedback, May 2023

“Lectures were really good and intense. Not too basic which was nice”.
Course feedback, May 2023

“Clear explanations on the whole, and great to have all the models/code to work through, plus access to the recordings”.
Course feedback, May 2023

“The course files were useful and the combination of practicals and teaching”.
Course feedback, May 2023

“I thought the teachers were all excellent and clear”.
Course feedback, May 2023