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The evaluation of methods to optimise management of chronic health conditions requires accurate estimates of the effect of these conditions on healthcare cost and quality of life. Current methods to produce these estimates tend to rely on observational methods that are prone to bias. Randomised controlled trials provide higher quality evidence, but are limited in their ability to provide information beyond the end of trial follow-up, and are often challenged by issues of feasibility, cost, and ethics. New ways of generating evidence of this nature are now much needed.

Mendelian Randomization allows inferences to be made about the causal effects of health conditions by treating common genetic variants as instrumental variables. These variants are associated with a health condition of interest, but, provided certain requirements are met, are not related to confounding variables that would affect observational analyses of the effects of health conditions, and are only related to these effects via the health condition of interest. Datasets with linked genotypic, cost and quality of life information are now becoming available and will facilitate this type of analysis.

Already well established in genetic epidemiology, possible uses for Mendelian Randomization in health economics are numerous but remain largely unexploited. I will discuss how the conceptual and analytic advances offered by Mendelian Randomization, and the opportunities offered by the rapid development of digital and biological resources, could contribute improved evidence and methods to economic evaluation and health economics more generally.

Bio: 

Dr Padraig Dixon is a Senior Research Associate in Health Economics at the School of Social and Community Medicine, University of Bristol. He hold degrees in Economics from Trinity College Dublin (BA) and Nuffield College, Oxford (MPhil and DPhil), and in Health Economics from the University of York (MSc). His research interests are in economic evaluation, healthcare policy and causal inference.

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