Gianluca leads the Statistics for Health Economic Evaluation research group within the department of Statistical Science and he is the co-director of UCL MSc Programme in Health Economics and Decision Science. Gianluca's 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. He 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) he then worked as a Research Fellow and then Temporary Lecturer in the Department of Statistical Science at University College London (UK). Gianluca graduated in Statistics and Economics from the University of Florence (Italy). Gianluca Baio is a professor of Statistics and Health Economics in the Department of Statistical Science at University College London (UK). University College London, London, United Kingdom This course is designed for those with some familiarity with modelling techniques, such as the concepts of discrete time cohort Markov models and probabilistic sensitivity analysis, but familiarity with R coding is not required.Īttendees will require a laptop with RStudio (v1.1.0 or higher) and R (v4.2.1 or higher) downloaded and installed. Participants will be provided with materials, including model examples in R and information on where to go for further learning. All sessions will interchange between descriptive lectures and hands-on exercises.
Additional useful packages for modelling using R will also be discussed. The faculty will lead participants through practical examples of health economic modelling including using R for Markov models from deterministic analysis through to probabilistic sensitivity analysis and EVPI. The faculty are expert speakers who have diverse experience in academia, national Health Technology Assessment agencies (NICE, NCPE), and industry. This course explores the use of R for health economic modelling in the context of health economics and outcomes research (HEOR) and faculty will guide the participants through practical examples of HEOR. This highly practical course will outline the computational and transparency advantages of using R, for those used to health economic modelling using Microsoft Excel.
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