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Decision Modelling for Health Economic Evaluation. A Briggs, M Sculpher, K Claxton
E-mail: d.m.caldwell{at}bristol.ac.uk
Decision Modelling for Health Economic Evaluation. A Briggs, M Sculpher, K Claxton: 2006, pp. 256 . $47.50, ISBN13: 9780198526629; ISBN10: 0198526628 Paperback. Oxford: OUP
As part of its technology appraisal programme, the National Institute for Clinical Excellence (NICE) uses economic evaluation to inform decisions about the use of healthcare interventions by the National Health Service. There has been considerable debate about the most appropriate methods to be used for health technology assessment, however, NICE guidelines state that clinical and cost-effectiveness should be considered over an ‘appropriate’ time horizon, be relevant to UK patients and compare all relevant treatments. This usually requires the development of a decision-analytic model. Furthermore, as NICE insist that models adequately reflect the decision uncertainty associated with a technology, decision-models are required to be probabilistic.
‘Decision Modelling for Health Economic Evaluation’ focuses on the role and methods of decision analysis in economic evaluation. It is an advanced, practical guide to the use of probabilistic decision modelling techniques, written by authors at the forefront of developments in this field. It is applied in approach with illustrative case studies and example exercises for key concepts, and has spreadsheet templates and solutions available from the accompanying website. Each chapter has a practical exercise that builds on the preceding one and so, on completion, builds a complete probabilistic decision analysis. The practical exercises assume familiarity with Excel; however, this is a common spreadsheet package and is regularly used by analysts in this field.
The introductory chapter briefly covers the basic principles of economic evaluation. Chapter 2 summarizes the key principles and concepts of decision analytic modelling, and identifies six main stages in the development of a decision model. The main type of decision model used is the cohort model and excellent worked examples are given of the two most common forms used in health economic evaluation, the decision tree and Markov model. The advantages and disadvantages of these simple approaches are well-presented and further developments and extensions to standard Markov models are introduced in Chapter 3.
The key strength of this book is the emphasis it places on the need for decision-analysis to be probabilistic and Chapters 4–7 provide a thorough review of the advantages of probabilistic modelling. Chapter 4 outlines the rationale for making decision models probabilistic and describes the methods for handling uncertainty, with a detailed section on the choice of distributions for model parameters. Chapter 5 shows how to present decision uncertainty and heterogeneity in a model using cost-effectiveness acceptability curves and outlines the advantages over the standard technique of incremental cost-effectiveness ratios.
The decision to adopt a new technology based on current information will be uncertain, and there is always the chance that the ‘wrong’ decision will be made. Chapters 6 and 7 explicitly address decision making under uncertainty. Expected value of information (EVI) methods can be used to quantify the cost of decision uncertainty, and hence quantify the value of additional information as a basis for research prioritization. At present, for all but the most straightforward models EVI is computationally demanding and requires strong modelling skills; a fact that the authors do not fully convey. Methods for calculating the expected value of sample information, in particular, are subject to continuing research and refinement. Nevertheless, EVI methods are an indispensable quantification to the axiom, ‘more research is necessary’.
The material contained in this book was developed alongside a 3-day residential course on ‘Advanced Modelling Methods for Health Economic Evaluation’. By definition, the book assumes prior experience of health economic evaluation and is aimed at advanced students and health technology analysts. Given that NICE recommend the use of probabilistic methods, this book is undoubtedly a welcome handbook for health technology analysts requiring technical details on decision-analytic modelling.
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