The course 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 including Value of Information calculations. As such, it 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.

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.

DescriptionInstructorsTarget audienceCourse formatProgrammeFeesApplicationScholarshipWatch the video!
When?
20 – 24 June, 2022
Where?
UNIL Campus
For whom?
International PhD students or postdocs in Economics, health economics, public health, epidemiology, health informatics, biostatistics, political sciences, decision science. Limited places for professionals.
How much?
SSPH+ Students: CHF 300.- / Students: CHF 500.- / Professionals: CHF 1500.-
Credits?
Non credited course, Official Certificate of Attendance delivered
Language?
English

Instructors:  Anna Heath (SickKids hospital, Toronto), Nathan Green (UCL), Gianluca Baio (UCL)

Dr Anna Heath is a Scientist in the Child Health and Evaluative Sciences Program at SickKids Research Institute, an Assistant Professor in the Division of Biostatistics, University of Toronto and an Honorary Research Associate in the Department of Statistical Science, University College London, UK. She holds a PhD in Statistical Science at University College London. Since 2018, she has worked at the Hospital for Sick Children, Toronto on novel methodology to improve the conception design and analysis of clinical research within a Bayesian framework. She is particularly interested in applying methods from decision theory, the science of determining the best course of action when the consequence of each action is uncertain, to clinical trial design. In practice, this interest requires a broad range of interrelated research topics including health economic decision modelling, Bayesian expert elicitation, utility theory, meta-analysis, statistical computation methods, clinical trial design and Bayesian statistical methods.
 
 

Dr Nathan Green studied undergraduate mathematics and statistics and obtained a PhD in applied probability from the University of Liverpool. He worked for the Ministry of Defence for several years and moved back into academia and the field of public health in 2010. He has focused on infectious disease modelling, previously at PHE (now UKHSA) and Imperial College London, before joining the department of Statistical Science at UCL in 2020. He is Vice-Chair of the Medical Section of the Royal Statistical Society and former British Science Association Media Fellow. His research interests include Bayesian statistical modelling for cost-effectiveness analysis and decision-making problems in the health systems, multilevel models and causal inference using the decision-theoretic approach.
 
 

Prof. Gianluca Baio a Professor of Statistics and Health Economics in the Department of Statistical Science at University College London. I graduated in Statistics and Economics from the University of Florence (Italy). I then completed a PhD programme in Applied Statistics again at the University of Florence, after a period at the Program on the Pharmaceutical Industry at the MIT Sloan School of Management, Cambridge (USA). I then worked as a Research Fellow and then Lecturer in the Department of Statistical Sciences at University College London (UK). My main interests are in Bayesian statistical modelling for cost effectiveness analysis and decision-making problems in the health systems, hierarchical/multilevel models and causal inference using the decision-theoretic approach. I lead the Statistics for Health Economic Evaluation research group within the department of Statistical Science. Our activity revolves around the development and application of Bayesian statistical methodology for health economic evaluation, e.g. cost-effectiveness or cost-utility analysis. We work in close collaboration with academics both within UCL and at other institutions and our activities include a series of seminars aimed at statisticians, health economists and clinicians working in economic evaluations. I collaborate with the UK National Institute for Health and Care Excellence (NICE) as a Scientific Advisor on Health Technology Appraisal projects. I have served as Secretary (2014-2016) and then Programme Chair (2016-2018) in the Section on Biostatistics and Pharmaceutical Statistics of the International Society for Bayesian Analysis. I have served as an Associate Editor for the Journal of the Royal Statistical Society (Series A) for the period 2015-2021 and have been a member of the Editorial Board of Significance, the magazine of both the Royal Statistical Society and the American Statistical Association in the period 2012-2021. I also am a member of the Scientific Committee of the Bayes Workshop and a member of the technical sub-group tasked by the NHS Medical Director to review the statistical and methodological aspects related to the use of the Summary Hospital-level Mortality Indicator. I have been the 18th Armitage Lecturer in November 2021.

PhD students or postdocs in health economics, economics, public health, biostatistics, or equivalent. The programme has a limited number of places for professionals outside academia.

The course will consist of lectures and computer labs.

Lecture topics
Introduction to health economic evaluations
Introduction to Bayesian inference
Introduction to Markov Chain Monte Carlo in BUGS
Cost-effectiveness analysis with individual-level data
Aggregated-level data and hierarchical models
Evidence synthesis and network meta-analysis
Model error and structural uncertainty
Markov models
Survival analysis
Missing data in cost-effectiveness modelling
Introduction to the value of information
Expected value of partial information (1) – algebraic tricks
Expected value of partial information (2) – generalised additive models & GP regression
Expected value of partial information (3) – GP regression in INLA/SPDE
Expected value of sample information (1) – conjugated analysis
Expected value of sample information (2) – efficient nested simulation and moment matching
Expected value of sample information (3) – regression- and sufficient statistics-based methods

Software & useful information
OpenBUGS (free software for Bayesian analysis)
R (free general statistical software)
JAGS (alternative software for Bayesian analysis) – probably the easiest option for Linux or Mac users
Stan (yet another software for Bayesian analysis) – this is based on a different method for MCMC (called Hamiltonian Monte Carlo)
R2OpenBUGS (R library to interface R and OpenBUGS) or R2jags (does the same for R and JAGS) or rstan (does the same for R and Stan)
BCEA (R library to perform Bayesian Cost Effectiveness Analysis). The stable (CRAN) current version is 2.5. The GitHub version is 2.6.
Wine (a “compatibility layer” that allows to run Windows applications from Linux or Mac)
Instructions to install OpenBUGS using Wine (for Mac users)

You find here the tentative programme, regularly updated.

SSPH+ PhD students: CHF 300.-
PhD students and postdocs: CHF 500.-
Professionals: CHF 1500.- 

  • Tuition fees
  • Lunches
  • Social events

Please note that accommodation is not covered by the fees.

Your application for the summer school must include your CV, which can be uploaded in the “Submitted documents” section on the registration platform.

Application deadline: May 15th, 2022

The Solidarity Scholarships aim at supporting students who would not be able to cover summer schools’ costs in full. The selected candidates will see their tuition fees as well as their travel, accommodation and meals costs covered. Depending on each candidate’s situation, a financial support may be added to partially cover other expenses during the stay. The deadline for scholarship application is March 6th, 2022.

To be eligible, candidates must:

  • Be currently enrolled in a higher education institution located in an ODA recipient country;
  • Have successfully applied to the school they wish to attend;
  • Be able to attend the entire school;
  • Require external financial support to be able to attend the school;
  • Have obtained excellent academic results;
  • Be covered by health insurance for the duration of the school.

Candidates who wish to apply to the Solidarity Scholarship must first sign up for one of the summer schools. Once accepted, candidates will receive the link to the online form to apply for the scholarship. Along with personal and academic information, the following documents will be requested (to be submitted in French or English):

  • Full curriculum vitae;
  • Letter of motivation, with connection between the summer schools and your academic goals and information regarding the necessity of a financial support;
  • Proof of residency (such as the copy of a lease, an official invoice with your address indicated or other official documents) ;
  • Copy of university degree certificate with final grade(s);
  • Proof of current enrolment in a higher education institution;
  • English certificate (if needed);
  • Letter of support from an academic.

N.B. The following video refers to the previous edition of the summer school “Health Economics for Public Health Decision-making”. The academic content of the current edition of the summer school will differ from the previous edition.

Registration is closed