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Edited by Andrew Briggs, Mark Sculpher and Karl Claxton. Oxford, Oxford University Press, 2006, £24.95, pp 237. ISBN 10: 0-19-852662-8, ISBN 13: 978-0-19-852662-9, 37 figures and 33 tables.
Economists are great model builders. Studying human behaviour has taught them to rely on assumptions (prior beliefs) and to use sophisticated mathematics and statistical techniques to reach outcomes (posteriors). In clinical trials the researcher is often restricted by instrumental precision (priors) and applies statistical techniques to reach scientific breakthroughs (posteriors). However, in a world of finite resources, it is inevitable that there has to be a limit on the number of trials that can be carried out. Thus is health economics born.
Decision Modelling for Health Economic Evaluation is a series of workshops created for practitioners who need to present a decision framework to policy‐makers. Each of the eight chapters deals with a major issue in health decision analysis. Topics are dealt with in a manner that meets the anticipation of the impatient reader. An important advantage of this book is the exercises at the end of chapter. The authors present a “step‐by‐step” guide to the exercises in tutorial fashion using Excel. The exercises include case studies.1,2 An associated web site (www.herc.ox.ac.uk/pubs/books/decision/supportingmaterials) provides worksheets and their solutions. Practitioners will appreciate the use of case studies and Excel macros, although the examples given are mostly from the UK experience. As there is no limitation on the application of methods, interested readers may use their own case studies. Boxed examples and summaries also help identify key points. However, readers are expected to be familiar with the basic ideas and elementary texts in health economics.3
The authors implicitly assume that their views of health economic policy are equally relevant to both private and public health care systems as technical examinations of decision models for further clinical trials do not depend on health systems. However, a more critical evaluation of health policy objectives may not be so neutral. The authors' focus on decision‐making under uncertain conditions is contrasted with cost–benefit analysis, as evaluation of health effects is often based on results. Policy‐makers who are accountable to an electorate tend to favour evaluation based on results, with both socioeconomic and political factors influencing health policy decisions. This is an issue not considered directly relevant to the main theme of this book, which is practical and technical in nature.
Models are, by their nature, abstractions of reality. The issue of increasing sample information or performing further simulations to reduce parameter uncertainty is dealt with mainly in Chapter 6, Exercise 6.6, on the calculation of expected value of perfect information (EVPI). EVPI attempts to appraise the cost‐effectiveness of obtaining additional information on parameters as an aid to decision‐making. The simulations attempt to answer questions regarding the benefit of further tests, the worth of further research and the value of findings.
As well as including many cross‐references to related studies, this text is a rich source book that merits repeated reading.