Title: Bayesian approaches for addressing missing data in Cost-Effectiveness Analysis (alongside Randomised Controlled Trials)

Abstract: Health economics has become an increasingly important discipline in medical research. Bodies such as NICE provide guidance based on health economic evaluation. Much of recent research has focused on using advanced statistical decision theoretic approaches in health economic evaluation studies where the interest is in the analysis of a multivariate outcome comprising of clinical benefit and associated costs. Missing data on one or both outcomes is common in these studies. The complex structure of the relationships between the outcomes makes handling missing data more challenging in these studies. In this talk the issues related to missing data in health economic evaluation studies will be discussed and novel statistical models developed to handle missing data using a full Bayesian approach will be described, using as motivating example a pilot study conducted at UCL.

Bio: Gianluca Baio is a professor of Statistics and Health Economics in the Department of Statistical Science at University College 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, hierarchical/multilevel models and causal inference using the decision-theoretic approach. 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.