The Spanish Health Economics Association (AES) has announced the pre-congress workshop entitled “Methods for addressing missing data in health economic evaluation”, which will precede the 39th Health Economics Conference.
Missing data are omnipresent in the evaluation of health economics. The main concern that arises with these is that they tend to be systematically different from those with complete data. As a result, the cost-effectiveness inferences made are often misleading. These limitations affect both economic evaluations based on a single study and those that synthesise data from various sources into decision-making models. While the appropriate methods for addressing missing data are available in most software packages, their acceptance in the evaluation of health economics has been limited.
Taught by experts in missing data methodology, this course offers a detailed description of the introductory methods for addressing missing data in economic evaluation. The course will introduce the statistical concepts and underlying assumptions of each method and will provide extensive guidance on applying the methods in practice.
Participants will take part in hands-on sessions that illustrate how to implement each technique with easy-to-use software (R and JAGS).
At the end of the course, participants will be able to: (1) Recognise the key statistical concepts, the underlying assumptions and the advantages of different statistical methods for dealing with missing data in cost-effectiveness analysis. (2) Carry out a descriptive analysis of incomplete cost-effectiveness data. (3) Apply multiple imputation methods to address missing data in economic evaluation studies. (4) Carry out sensitivity analyses to assess whether cost-effectiveness inferences stand up to assumptions of alternative missing data.
Participants who wish to have hands-on experience must bring their personal laptops with the appropriate software installed (R and JAGS).
| 10:00 - 10:30 h | Preliminary - Introduction to (Bayesian) modelling in HTA
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| 10:30 - 11:00 h | Introduction to missing data
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| 11:00 - 12:00 h | Missing data in HTA
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| 12:00 - 13:30 h | Lunch |
| 13:30 - 14:30 h | Examples
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| 14:30 - 16:30 h | Practicals
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*The working language will be English.
Javier Mar
Alto Deba Integrated Health Organisation, Mondragón.
Prof Gianluca Baio,
Professor of Statistics and Health Economics; Dept of Statistical Science; Faculty of Maths & Physical Sciences, University College London, London, UK
Gianluca graduated in Statistics and Economics from the University of Florence (Italy). 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 Sciences at University College London (UK).
Gianluca's main interests are in Bayesian statistical modelling for cost effectiveness analysis and decision-making problems in the health systems and causal inference using the decision-theoretic approach. Current applied works include the economic evaluation of health interventions such as the treatment of osteoporosis in elderly women, and of cardiovascular disease secondary prevention with statins. He is also participating in several research projects funded by research councils and two EU-funded projects on pharmacological surveillance in Europe.
Andrea Gabrio,
Research Associate; Primary Care & Population Health; Institute of Epidemiology & Health: Faculty of Pop Health Sciences; University College London, London, UK
Andrea graduated in Economics from the University of Pavia (Italy) and then completed an MSc in Statistcs and Econometrics from the University of Essex (UK). Successively, he completed a PhD programme in Statistics at University College London (UK), during which he also worked as a consultant in the field of economic evaluations for the company MAPIgroup and spent a brief period at the University of Florida (US) as a visiting research student.
Andrea's main research interest is the application of Bayesian statistics in health economics. Specifically, he focuses on Bayesian methods to handle missing data in health economic evaluations and to assess the impact of their uncertainty on the output of the decision-making process.
Registration rules: