Preface

This book originates from the work that we have done, at different times and in different capacities, in the area of statistical modelling for health economic evaluation. In our view, this is a very interesting and exciting area for statisticians: despite the strong connotation derived by its name, health economic evaluation is just as much (if not more!) about statistics than it is about healthcare or economics. Statistical modelling is a fundamental part of any such evaluation and as models and the data that are used to populate them become bigger, more complex and representative of a complicated underlying reality, so do the skills required by a modeller. Broadly speaking, the objective of publicly funded healthcare systems (such as the UK’s) is to maximise health gains across the general population, given finite monetary resources and a limited budget. Bodies such as the National Institute for Health and Care Excellence (NICE) provide guidance on decision-making on the basis of health economic evaluation. This covers a suite of analytical approaches (usually termed “cost-effectiveness analysis”) for combining costs and consequences of intervention(s) compared to a control, the purpose of which is to aid decision-making associated with resource allocation. To this aim, much of the recent research has been oriented towards building the health economic evaluation on sound and advanced statistical decision-theoretic foundations. Historically, cost-effectiveness analysis has been based on modelling often performed in specialised commercial packages (such as TreeAge) or even more frequently spreadsheet calculators (almost invariably MicrosoftExcel). The “party-line” for why this is the case is that these are “easy to use, familiar, readily available and easy to share with stakeholders and clients”. Possibly, in addition to these, another crucial factor for the wide popularity of these tools is the fact that often modellers are not statisticians by training (and thus less familiar with general-purpose statistical packages such as SAS, Stata or R). Even more interestingly, it is often the case that cost-effectiveness models are based on existing templates (usually developed as Excel spreadsheets, for example for a specific country or drug) and then “adapted” to the situation at hand.