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Economists have traditionally relied on retrospective analysis of actual consumer behaviour to understand the factors affecting the decisions of economic agents. This type of information is termed revealed preference (RP) data. Suppose we want to understand health plan choice. Typically, researchers will try to find a data set where consumers were offered and selected a number of different health plans. Then, they will examine the effect of the health plan attributes - price, services covered and quality ratings - on choice. The advantage of RP data is that it is based on actual decisions; thus, there is no need to assume that consumers will respond to simulated product markets as they do to actual market situations. This characteristic gives RP data high reliability and face validity. The disadvantage with this approach, in this case, is that the price of the insurance plan is highly correlated with the services covered and the actuarial value of the plan.What is needed is an estimate of the effect of prices for a wide range of benefit bundles. [ABSTRACT FROM AUTHOR]

When using discrete choice experiments (DCEs) it is crucial to ensure that subjects are answering in a "rational" (internally consistent) way. One approach to testing rationality is to test the axiom of non-satiation, i.e. more is preferred to less.Within DCEs satisfaction of this axiom has been explored by including choices where one of the options has no worse levels for any of the attributes and better levels for at least one (See Chapters 4, 5 and 7 in this book and Ryan et al., 2001). Johnson and Mathews (2001) carried out different tests of choice consistency, including dominance, monotonicity across different pairs and so-called stability (repetition of identical choices within the same questionnaire). Transitivity of preferences has also been explored (San Miguel, 2000). [ABSTRACT FROM AUTHOR]

Over the last 15 years, discrete choice experiments (DCEs) have proved a very useful technique both within the framework of an economic evaluation, and for modelling behaviour and preferences in a variety of contexts within health economics. This book has presented important issues in the design and analysis of DCEs (Part 1), demonstrated their application in a number of different settings in the health and health care arena (Part 2) and looked at some emerging methodological issues (Part 3). [ABSTRACT FROM AUTHOR]

The aim of this chapter is to show how discrete choice experiments (DCEs) can be applied to examine the preferences of health care providers. Health care providers comprise health care organisations and health professionals employed within them. Health professionals make decisions about their supply of labour in addition to clinical decisions. A key policy issue across many developed and developing countries is shortages of health professionals, particularly doctors and nurses. Although a number of policies are being introduced and have been suggested to reduce these shortages, there is little empirical evidence on what factors influence the labour market behaviour of doctors and nurses. Evidence on the relative impact of pay and remuneration on job choices and labour supply has shown that pecuniary factors do matter, but that their effect may be moderate. This raises the issue of what non-pecuniary job characteristics might be altered by policy to increase recruitment and retention. Existing "revealed preference" administrative and survey data do not collect good information on these non-pecuniary job characteristics. DCEs are a method that can be used to inform this issue (Scott, 2001). This chapter focuses on the labour market decisions of hospital consultants in the UK. [ABSTRACT FROM AUTHOR]

The price proxy plays a distinct role in discrete choice experiments (DCEs) since the inclusion of a price proxy makes it possible to indirectly obtain willingness to pay (WTP) estimates for the good in its entirety or for a change in an attribute level. Marginal rates of substitution (MRS) between the price proxy and other programme attributes constitute these "part-worth" values. [ABSTRACT FROM AUTHOR]

With the rapid growth in the development of new health care technologies, including both drugs and devices, health care decision makers worldwide are facing the challenge of making technology coverage decisions. Decisions have to be made concerning which technologies to fund, and these decisions have to be made in both public and private insurance settings. In order to ensure that health care resources are used in the most appropriate manner health care decision makers need to adopt robust processes for setting priorities. Recent developments in the UK, such as the launch of the National Institute for Health and Clinical Excellence (NICE), have encouraged a more open debate about the principles and issues concerned in health care resource allocation decisions (Entwistle et al., 1996; New, 1996; Rawlins, 1999). However, the appropriate criteria that should be used in setting priorities in a publicly funded health care system are far from clear. From a health economics perspective, one criterion that might be considered as part of the decision-making process when setting health care priorities is the maximisation of quality-adjusted life years (QALYs). A QALY-maximisation approach would then involve the targeting of resources towards health care interventions and services that were expected to deliver the largest gain in QALYs, for every dollar (or pound, euro, etc.) spent. [ABSTRACT FROM AUTHOR]

The aim of this chapter is to show how discrete choice experiments (DCEs) can be applied to examine the preferences of women reinvited for breast screening and to use the results in developing breast screening participation enhancement strategies. [ABSTRACT FROM AUTHOR]

Until the 1990s benefit assessment in health economics was dominated by an assumption that health was the only important outcome from health care. This is evidenced by the large amount of research devoted to valuing health outcomes using quality-adjusted life years (QALYs) (Williams, 1985; Dolan, 1997). The 1990s saw a challenge to this assumption, arguing that concentration on health outcome fails to allow for the possibility that individuals derive benefit from other sources - nonhealth outcomes and process attributes - (Ryan and Shackley, 1995; Ryan and Hughes, 1997; Diener et al., 1998; Donaldson et al., 1998;Ryan, 1999; Donaldson and Shackley, 2003). Non-health outcomes refer to sources of benefit such as the provision of information, reassurance, autonomy and dignity in the provision of care. Process attributes include such aspects of care as waiting time, time in consultation, location of treatment and continuity of care and staff attitudes. While it should be recognised that some of these will have direct effects on health outcomes, they also have what can be regarded as pure non-health benefits. The debate about going beyond health outcomes led to the question of how such attributes can be valued. It became clear that QALYs would not be appropriate for valuing non-health outcome and process attributes. For example, it would not be realistic to ask individuals how many years at the end of their life they would be willing to give up to have waiting time reduced by 3 months; as would be required using the time-trade-off method. [ABSTRACT FROM AUTHOR]

This chapter walks the reader through the stages of a discrete choice experiment (DCE) application drawing on concepts introduced in Chapters 1 and 2. A case study eliciting women's preferences for prenatal screening is used to illustrate the points (Ryan et al., 2005). It should be noted that this data was collected several years ago, and therefore adopted old methods of experimental design. The sample size is also small.We have chosen it because it demonstrates nicely the many potential uses of a DCE, it addressed a policy-relevant question at the time, and it represents one of the few studies in health economics where the scientists (geneticists) worked with the evaluators (economists) and implementers (obstetricians) to look at development, evaluation and implementation of prenatal screening programmes. [ABSTRACT FROM AUTHOR]

As noted in Chapter 1, the application of discrete choice experiments (DCEs) in health economics has seen an increase over the last few years.While the number of studies using DCEs is growing, there has been relatively limited consideration of experimental design theory and methods. Details of the development of the designed experiment are rarely discussed. Many studies have used small fractional factorial designs (FFDs), generated with commercial design software packages, e.g. orthogonal main effects plans (OMEPs), sometimes manipulated in ad hoc ways (e.g. randomly pairing up scenarios or taking one scenario from the design and combining it with every other scenario). Such approaches can result in designs with unknown statistical design properties, in particular with unknown correlations between parameter estimates. [ABSTRACT FROM AUTHOR]

Since their introduction in health economics in the early 1990s, research in the area of health care benefits valuation has seen an increased interest in the use of discrete choice experiments (DCEs). This is shown by the explosion of literature applying this technique to direct evaluation of different policy-relevant attributes of health care interventions as well as to look at other issues such as understanding labour supply characteristics, time preferences or uptake or demand forecasting (see Ryan and Gerard, 2003; Fiebig et al., 2005 for recent reviews). [ABSTRACT FROM AUTHOR]