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Abstract: Classification and Regression Trees (CART) are ‘big data’ techniques that are particularly valuable for analysing non-linear relationships and interactions. We use CART to analyse the decision-making processes that determine funding outcomes for health interventions. Comparison between our CART models and previously published results demonstrates concurrence with standard regression techniques while providing additional insights regarding the role of the funding environment and the structure of decision-maker preferences. In particular, our analysis identified silos and non-linearities, where cost-effectiveness thresholds varied markedly depending on the type or objective of the health intervention. This presentation will provide an introduction to the CART methodology, highlight its strengths in uncovering a structure that might otherwise have been missed using standard regression techniques, and comment on some of the limitations of CART in practice.

Biography: Chris Schilling is an experienced health economist and PhD Candidate at the University of Melbourne. His research priorities include microsimulation modelling to inform policy decisions, classification and regression tree (CART) methods, and the economics of surgery. Chris is an Associate Lecturer for the Centre of Health Policy at the University of Melbourne, holds an honorary position at the Department of Surgery, St. Vincent’s Hospital Melbourne, and is an Associate at the New Zealand Institute of Economic Research.

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