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Summer school: Bayesian methods in health economics

Last updated

January 11, 2023

Aims, objectives and intended audience

This summer school aims at providing an introduction to Bayesian analysis and Markov Chain Monte Carlo (MCMC) methods using R and MCMC sampling software (such as OpenBUGS and JAGS), as applied to cost-effectiveness analysis and typical models used in health economic evaluations.

We will also focus on more recent methods for Probabilistic Sensitivity Analysis and research prioritisation, including Value of Information calculations.

The summer school is intended for health economists, statisticians, and decision modellers interested in the practice of Bayesian modelling and will be based on a mixture of lectures and computer practicals. The emphasis will be on examples of applied analysis: software and code to carry out the analyses will be provided and presented/discussed in details.

Important

Participants are encouraged to bring their own laptops for the practicals. We shall assume a basic knowledge of standard methods in health economics and some familiarity with a range of probability distributions, regression analysis, Markov models and random-effects meta-analysis. However, statistical concepts are reviewed in the context of applied health economic evaluations in the lectures.

We shall also assume at least basic familiarity with R, although, once again, we shall have a specific focus on the practicals and explain in details the practice of (Bayesian) modelling for health technology assessment.

Lectures and practicals are based on the following textbooks: Bayesian Methods in Health Economics (BMHE), Bayesian Cost-Effectiveness Analysis with the R package BCEA (BCEA), The BUGS Book (BB) and Evidence Synthesis for Decision Making in Healthcare (ESDM).

The summer school essentially is made of two halves — the first main topic is Bayesian modelling; the second is its application to health-economic evaluation. These two will be intertwined throughout — we will switch back and forth to the description of the methods and their application to data on cost-effectiveness of (mainly, but not exclusively) pharmaceutical interventions.

In any case, the main learning objective of this summer school is for you to be able to perform a Bayesian analysis (specifically on health care data and with the objective of a full economic evaluation). We will be formal in the exposition of the technical concepts, while not fixating with proofs and theorems (but rather trying to provide the rationale and the intuition behind the use of the various methods).

Important

In order to achieve this goal, much of the summer school will concentrate on practical skills, such as programming and doing data analysis in R and Bayesian software such as BUGS. Both pieces of software are publicly available and you can install them on your machine. You probably have used R in your previous work/studies. In any case, you will be given a lot of material to make up for any lack of knowledge. We expect you to go through the exercises and try to improve your computational skills throughout the summer school. As for BUGS, you probably have not seen it before — but again, you will be provided with a wealth of material and so, by the end of the week, you should be able to write, run and debug BUGS code proficiently!

Details

Faculty

The summer school is taught by Gianluca Baio, Anna Heath, Howard Thom and Nathan Green.

🙏 Special thanks go to Mark Strong, Chris Jackson and Nicky Welton, who have all been involved in previous versions of the summer school (and might be again, in future ones…) and have been instrumental in creating some of the material we present here.

Daily schedule

The following is the daily scheduled for the last edition (2022), which was hosted in the main campus at the University of Lausanne — topics and lecturers may slightly vary, but by and large the list of topics and the setup of the summer school will remain very close to this. Click on the banners below to expand the full schedule for each day.

Day 1 — Monday
Start End Topic
10:00 11:00 Lecture 1: Introduction to Bayesian reasoning, computation and BUGS
11:00 11:15 Coffee break
11:15 12:00 Practical 1. Monte Carlo in BUGS
12:00 13:00 Lecture 2: Learning from data using MCMC and BUGS
13:00 14:00 Lunch
14:00 15:15 Practical 2. MCMC in BUGS
15:15 15:30 Coffee break
15:30 16:30 Lecture 3: Introduction to health economic evaluation
16:30 17:00 Practical 3. Introduction to R and cost-effectiveness analysis using BCEA
Day 2 — Tuesday
Start End Topic
09:00 10:00 Lecture 4: Individual level data in health economics
10:00 11:00 Practical 4. Cost-effectiveness analysis with individual-level data
11:00 11:15 Coffee break
11:15 12:15 Lecture 5: Aggregated level data
12:15 13:15 Practical 5. Evidence synthesis and decision models
13:15 14:15 Lunch
14:15 15:15 Lecture 6: Evidence synthesis and network meta-analysis
15:15 15:30 Coffee break
15:30 16:30 Practical 6. Network meta-analysis
Day 3 — Wednesday
Start End Topic
09:00 10:00 Lecture 7: Model error and structural analysis
10:00 10:45 Practical 7. PSA to structural uncertainty
10:45 11:00 Coffee break
11:00 12:00 Lecture 8: Survival analysis in HTA
12:00 13:00 Lunch
13:00 14:00 Practical 8. Survival analysis
14:00 14:15 Coffee break
14:15 15:15 Lecture 9: Markov models
15:15 16:00 Practical 9. Markov models
Day 4 — Thursday
Start End Topic
09:00 10:00 Lecture 10: Missing data in cost-effectiveness modelling
10:00 10:45 Practical 10. Missing data
10:45 11:00 Coffee break
11:00 12:00 Lecture 11: Introduction to Value of Information
12:00 13:00 Lunch
13:00 13:45 Practical 11. Computing the EVPI using nested Monte Carlo simulations
13:45 14:45 Lecture 12: Expected value of partial information
14:45 15:00 Coffee break
15:00 16:00 Practical 12. Computing the EVPPI in BCEA and SAVI
Day 5 — Friday
Start End Topic
09:00 09:45 Lecture 13: Expected value of sample information
09:45 10:15 Lecture 14: Generating data for the analysis of the EVSI
10:15 10:30 Coffee break
10:30 11:15 Practical 13. Generating data for EVSI
11:15 12:00 Lecture 15: Calculating expected value of sample information
12:00 13:00 Lunch
13:00 13:45 Practical 14. Computing the EVSI using Monte Carlo simulations
13:45 14:15 Lecture 16: Regression-based EVSI
14:15 15:00 Practical 15. Computing EVSI using regression

Next edition — Florence, 24-28 July 2023

The next edition of the summer school will be held on 24-28 July 2023 at the Centro Studi CISL, Via della Piazzuola, 71 — 50133 Florence. The registration fee includes accommodation for the duration of the course (from Sunday night to Thursday night included), but specific arrangements for extra days could be made directly with the Centro Studi, pending room availabilities.

Travelling to Florence

Florence is served by the Amerigo Vespucci international airport, but the most convenient way to fly to Tuscany is probably through Pisa’s Galileo Galilei international airport — there are some low cost airlines flying there and the airport is a bit bigger. There is a train connection from Pisa Airport to Pisa Centrale and then to Firenze Santa Maria Novella. There are also coaches going directly to Florence from Pisa airport – some options here. Train tickets can be booked and bought at the Trenitalia website. If you arrive to Florence Airport, the taxi ride to the Centro Studi is about 20 minutes and cost about €20. There is also a recently opened tram connection from Florence airport to Santa Maria Novella train station.

Another relatively convenient airport is Bologna (which is just half an hour away on the fast, but more expensive train, called “Frecciarossa”, still available to book from Trenitalia. There’s a bus connection from Bologna airport to the main train station and then lots of connections from Bologna Centrale to Firenze Santa Maria Novella). Rome is also not too far away. There are two main train companies in Italy: Trenitalia is state-run while Italo is a private company. Only Trenitalia serves Florence from Pisa, while it is possible to travel to Florence from other cities with both.

How to get there

Once you arrive to Florence main station, you could get to Centro Studi either by taxi (there is a taxi queue in front of the station), or by bus. You will have to buy your bus ticket before you board the bus — you can buy a 90-minute ticket for €1.50, but other options are available). From Santa Maria Novella, you can reach Centro Studi with the following:

  • Bus no 7 to Fiesole (approximately one every 20 minutes) and get off at Ospedale di Camerata. Via Della Piazzola (where Centro Studi is) runs just parallel to Ospedale di Camerata about 300 meters away.

Click on the “More options” link in the map below to get full navigation instructions (via Google Maps).

This is the official route suggested on Centro Studi’s website. If you are driving, there is parking space in the Centro Studi, but it is probably best to arrange directly with them.

Registration

Participation fees

There are three types of partecipation fees:

  1. Students (£900)
  2. Public sector (£1,800)
  3. Private sector (£2,200)

These course fees include tuition, full board accommodation, as well as access to the course material (slides, R/BUGS code/scripts for the practicals, relevant papers, etc). By the way, here’s a couple of pics of the Centro Studi, in case you need a little push…

Registration process

Registration is now open and available through UCL Online Store. The deadline for registration is set to 15 June 2023 and there are 20 places available – so hurry!


         
© Gianluca Baio 2022-2024