Search

In contrast to the recent proliferation of studies incorporating ordinal methods to generate health state values from adults, to date relatively few studies have utilised ordinal methods to generate health state values from adolescents. This paper reports upon a study to apply profile case best worst scaling methods to derive a new adolescent specific scoring algorithm for the Child Health Utility 9D (CHU9D), a generic preference based instrument that has been specifically designed for the estimation of quality adjusted life years for the economic evaluation of health care treatment and preventive programs targeted at young people. A survey was developed for administration in an on-line format in which consenting community based Australian adolescents aged 11-17 years (N = 1982) indicated the best and worst features of a series of 10 health states derived from the CHU9D descriptive system. The data were analyzed using latent class conditional logit models to estimate values (part worth utilities) for each level of the nine attributes relating to the CHU9D. A marginal utility matrix was then estimated to generate an adolescent-specific scoring algorithm on the full health = 1 and dead = 0 scale required for the calculation of QALYs. It was evident that different decision processes were being used in the best and worst choices. Whilst respondents appeared readily able to choose 'best' attribute levels for the CHU9D health states, a large amount of random variability and indeed different decision rules were evident for the choice of 'worst' attribute levels, to the extent that the best and worst data should not be pooled from the statistical perspective. The optimal adolescent-specific scoring algorithm was therefore derived using data obtained from the best choices only. The study provides important insights into the use of profile case best worst scaling methods to generate health state values with adolescent populations. Refereed/Peer-reviewed

BackgroundValues used in economic evaluation are typically obtained from the general public, which is problematic when measures are to be used with people experiencing a life-course stage such as the end of life.ObjectiveTo assess the feasibility of obtaining values for the ICECAP-Supportive Care Measure (SCM) from patients receiving advanced supportive care through a hospice.MethodsParticipants completed eight best-worst scaling questions in a think-aloud interview to explain choices in different hypothetical end-of-life scenarios. Three independent raters identified errors in completion of the best-worst scaling task, and thematic analysis of associated qualitative data was undertaken to explore task difficulty and choices.ResultsTwelve hospice patients were recruited. Most were able to complete the task and prioritise aspects of supportive care with either no difficulty (n=50%) or difficulty in just one of the eight scenarios (n=25%). Two patients (n=17%) were unable to comprehend the hypothetical nature of the task. The qualitative data confirmed there was good engagement with the task and identified the importance the respondents attached to maintaining dignity.ConclusionThe findings suggest that people at the end of life will be able to complete a short, interviewer-administered, best-worst scaling task. To maximise engagement, it is recommended that the task is short and initiated with an example. Scenarios are best presented on show-cards in large print. A full evaluation of the ICECAP-SCM with those at the end of life is feasible.

Objectives: To examine the performance of two recently developed preference-based instruments-the Child Health Utility 9D (CHU9D) and the EuroQol five-dimensional questionnaire Youth version (EQ-5D-Y)-in assessing the health-related quality of life (HRQOL) of Australian adolescents. Methods: An online survey including the CHU9D and the EQ-5D-Y, self-reported health status, and a series of sociodemographic questions was developed for administration to a community-based sample of adolescents (aged 11-17 years). Individual responses to both instruments were translated into utilities using scoring algorithms derived from the Australian adult general population. Results: A total of 2020 adolescents completed the online survey. The mean ± SD utilities of the CHU9D and the EQ-5D-Y were very similar (0.82 ± 0.13 and 0.83 ± 0.19, respectively), and the intraclass correlation coefficient (0.80) suggested good levels of agreement. Both instruments were able to discriminate according to varying levels of self-reported health status (P < 0.001). Although exhibiting good levels of agreement overall, some wide divergences were apparent at an individual level. Conclusions: The study results are encouraging and illustrate the potential for both the CHU9D and the EQ-5D-Y to be more widely used for measuring and valuing the HRQOL of adolescent populations in Australia and internationally. Generating adolescent-specific scoring algorithms pertaining to each instrument and an empirical comparison of the resulting utilities is a natural next step. More evidence is required from the application of the CHU9D and the EQ-5D-Y in specific patient groups in adolescent health settings to inform the choice of instrument for measuring and valuing the HRQOL for the economic evaluation of adolescent health care treatments and services Refereed/Peer-reviewed

The main objective of this study was to compare and contrast adolescent and adult values for the Child Health Utility-9D (CHU9D), a new generic preference-based measure of health-related quality of life designed for application in the economic evaluation of treatment and preventive programmes for children and adolescents. Previous studies have indicated that there may be systematic differences in adolescent and adult values for identical health states. An online survey including a series of best-worst scaling discrete choice experiment questions for health states defined by the CHU9D was administered to two general population samples comprising adults and adolescents, respectively. The results highlight potentially important age-related differences in the values attached to CHU9D dimensions. Adults, in general, placed less weight upon impairments in mental health (worried, sad, annoyed) and more weight upon moderate to severe levels of pain relative to adolescents. The source of values (adults or adolescents) has important implications for economic evaluation and may impact significantly upon healthcare policy.

End of life care may have elements of value that go beyond health. A generic measure of the benefits of end of life care could be helpful to decision makers. Such a measure, based on the capability approach, has recently been developed: the ICECAP Supportive Care Measure. This paper reports the first valuation exercise for that measure, with data from 6020 individuals collected from an on-line general population panel during June 2013. Individuals were asked to complete a stated choice experiment that combined best-worst scaling and a standard discrete choice experiment. Analysis of the best-worst data used limited dependent variable models within the random utility framework including the multinomial logit models and latent class choice model analysis. Exploratory steps were taken to determine the similarity of the best-worst and DCE data before formal testing and pooling of the two data sources. Combined data were analysed in a heteroscedastic conditional logit model adjusting for continuous scale. Two sets of tariffs were generated, one from the best-worst data capturing only main effects, and a second from the pooled data allowing for two-way interactions. Either tariff could be used in economic evaluation of interventions at the end of life, although there are advantages and disadvantages with each. This extensive valuation exercise for the ICECAP Supportive Care Measure, with a large number of members of the general public, could be complemented in the future with best-worst scaling studies amongst those experiencing the end of life. Refereed/Peer-reviewed

We commonly determine the most preferred (best) and least preferred (worst) of a set of options, yet it is unclear whether the 2 choices are based on the same or different information. We examined best and worst choices using discrete choice tasks, in which participants selected either the best option from a set, the worst option, or selected both the best and the worst options. One experiment used perceptual judgments of area, and another used consumer preferences for various attributes of mobile phones. In both domains, we found that the task (best, worst, or best and worst) does not alter the preferences expressed for the best (respectively, the worst) option. We also observed that the choice probabilities were consistent with a single latent dimension—options that were frequently selected as best were infrequently selected as worst, and vice versa—both within and between respondents. A quantitative model of choice and response time provided convergent evidence on those relations, with model variants that assumed an inverse relationship between the estimated parameters for best and worst choices accounting well for the data.We conclude that the diverse types of best and worst choices that we studied can be conceived as opposing ends of a single continuous dimension rather than distinct latent entities. We discuss these results in the light of rather different results for accepting (e.g.,purchasing) and rejecting (e.g., not purchasing) options from a set Refereed/Peer-reviewed

This paper reports the results of a best–worst scaling (BWS) study to value the Investigating Choice Experiments Capability Measure for Adults (ICECAP-A), a new capability measure among adults, in a UK setting. A main effects plan plus its foldover was used to estimate weights for each of the four levels of all five attributes. The BWS study was administered to 413 randomly sampled individuals, together with sociodemographic and other questions. Scale-adjusted latent class analyses identified two preference and two (variance) scale classes. Ability to characterize preference and scale heterogeneity was limited, but data quality was good, and the final model exhibited a high pseudo-r-squared. After adjusting for heterogeneity, a population tariff was estimated. This showed that ‘attachment’ and ‘stability’ each account for around 22% of the space, and ‘autonomy’, ‘achievement’ and ‘enjoyment’ account for around 18% each. Across all attributes, greater value was placed on the difference between the lowest levels of capability than between the highest. This tariff will enable ICECAP-A to be used in economic evaluation both within the field of health and across public policy generally. © 2013 The Authors. Health Economics published by John Wiley & Sons Ltd.

We review and discuss recent developments in best–worst scaling (BWS) that allow researchers to measure items or objects on measurement scales with known properties. We note that BWS has some distinct advantages compared with other measurement approaches, such as category rating scales or paired comparisons. We demonstrate how to use BWS to measure subjective quantities in two different empirical examples. One of these measures preferences for weekend getaways and requires comparing relatively few objects; a second measures academics' perceptions of the quality of academic marketing journals and requires comparing a significantly large set of objects. We conclude by discussing some limitations and future research opportunities related to BWS. Refereed/Peer-reviewed

Objectives This study aimed to determine quantitatively the attributes of such screening tests that couples placed most value on. Methods A stated preference discrete choice experiment was conducted among a sample of pregnant women and their male partners. One four-level attribute (cost) and three two-level attributes (detection rate, gestation and time to wait for results) were used to generate eight hypothetical pairs of tests. Results A total of 103 participants fully completed the questionnaire (63 women, 40 men). Overall, the sample was most concerned with test cost. However, latent class analysis showed three groups of participants whose concern for attributes differed, with cost, detection rate and delay in receiving results being the most important to each. Willingness to pay calculations showed that participants who were most concerned with detection rate were prepared to pay more than four times the amount than those most cost-sensitive would pay for the ‘best test’ option as compared with the ‘worst test’. Conclusions Overall, couples were sensitive to the price of the screening test, but explicit subgroups are also shown that would pay large amounts for improvements in other attributes, particularly detection rates. This could provide important information to policymakers and practitioners in antenatal care, specifically in relation to the trade-offs made when couples decide about antenatal screening tests. © 2013 John Wiley & Sons, Ltd.

Conventional economic evaluation methods are likely unsuitable for valuing end-of-life care. This chapter describes much simpler tasks called discrete choice experiments as an alternative. It uses an Australian study and summarises work on the research frontier which offers the prospect of validating the stated preferences of individuals using physiological (response time) data. Such data also appear to be able to distinguish Kahneman’s “fast” (emotional) and “slow” (considered) decision-making styles, which will provide clinicians and policymakers with much more information about what is driving an individual’s views.

This paper explores five under-recognised conceptual issues in the use of discrete choice tasks in eliciting a tariff for health and/or quality of life instruments. Its purpose is not to explore econometric and statistical techniques but to offer advice on design and practical matters gleaned from a series of valuation exercises the author has been intimately involved with. These were largely Best-Worst Scaling (BWS) studies, but the implications hold true in many cases for traditional discrete choice experiments (DCEs) and ranking studies. The first two issues deal largely with the construction of the descriptive system (items). The third issue uses a within-subject DCE led by the author to introduce the worrying possibility that the quest for high statistical efficiency in discrete choice studies may have severe, unintended consequences. The need for quota, rather than representative, sampling is discussed as the fourth issue. Finally there is a discussion of the often used Case 2 type of BWS and how it relates to traditional “multiprofile” (choice between states) DCEs and Case 3 BWS. It hopes to correct misconceptions that continue to be propagated concerning the differences between the methods. The author offers advice on future studies and is encouraged by work funded by the EuroQoL Group.

This article appeared in a journal published by Elsevier Ltd. Under Elsevier's copyright, mandated authors are not permitted to make work available in an institutional repository., The Child Health Utility-9D (CHU-9D) and the Assessment of Quality of Life-6D (AQOL-6D) presently represent the only two generic preference-based instruments developed for application with young people with both adult- and adolescent-specific scoring algorithms. The main objective of this study was to compare and contrast the application of adult and adolescent scoring algorithms for the CHU-9D and AQOL-6D in valuing the health of a community-based sample of adolescents., Australian National Health & Medical Research Council

Purpose The benefits of health and social care are not confined to patient health alone and therefore broader measures of wellbeing may be useful for economic evaluation. This paper reports the development of a simple measure of capability wellbeing for adults (ICECAP-A). Methods In-depth, informant-led, interviews to identify the attributes of capability wellbeing were conducted with 36 adults in the UK. Eighteen semi-structured, repeat interviews were carried out to develop a capability-based descriptive system for the measure. Informants were purposively selected to ensure variation in socio-economic status, age, sex, ethnicity and health. Data analysis was carried out inductively and iteratively alongside interviews, and findings were used to shape the questions in later interviews. Results Five over-arching attributes of capability wellbeing were identified for the measure: "stability", "attachment", "achievement", "autonomy" and "enjoyment". One item, with four response categories, was developed for each attribute for the ICECAP-A descriptive system. Conclusions The ICECAP-A capability measure represents a departure from traditional health economics outcome measures, by treating health status as an influence over broader attributes of capability wellbeing. Further work is required to value and validate the attributes and test the sensitivity of the ICECAP-A to healthcare interventions. © Springer Science+Business Media B.V. 2011.

Attribute generation for discrete choice experiments (DCEs) is often poorly reported, and it is unclear whether this element of research is conducted rigorously. This paper explores issues associated with developing attributes for DCEs and contrasts different qualitative approaches. The paper draws on eight studies, four developed attributes for measures, and four developed attributes for more ad hoc policy questions. Issues that have become apparent through these studies include the following: the theoretical framework for random utility theory and the need for attributes that are neither too close to the latent construct nor too intrinsic to people's personality; the need to think about attribute development as a two-stage process involving conceptual development followed by refinement of language to convey the intended meaning; and the difficulty in resolving tensions inherent in the reductiveness of condensing complex and nuanced qualitative findings into precise terms. The comparison of alternative qualitative approaches suggests that the nature of data collection will depend both on the characteristics of the question (its sensitivity, for example) and the availability of existing qualitative information. An iterative, constant comparative approach to analysis is recommended. Finally, the paper provides a series of recommendations for improving the reporting of this element of DCE studies.

QALYs are increasingly being utilized as a health outcome measure to calculate the benefits of new treatments and interventions within cost-utility analyses for economic evaluation. Cost-utility analyses of adolescent-specific treatment programmes are scant in comparison with those reported upon for adults and tend to incorporate the views of clinicians or adults as the main source of preferences. However, it is not clear that the views of adults are in accordance with those of adolescents on this issue. Hence, the treatments and interventions most highly valued by adults may not correspond with those most highly valued by adolescents. Ordinal methods for health state valuation may be more easily understood and interpreted by young adolescent samples than conventional approaches. The availability of young adolescent-specific health state values for the estimation of QALYs will provide new insights into the types of treatment programmes and health services that are most highly valued by young adolescents.The first objective of this study was to assess the feasibility of applying best-worst scaling (BWS) discrete-choice experiment (DCE) methods in a young adolescent sample to value health states defined by the Child Health Utility 9D (CHU9D) instrument, a new generic preference-based measure of health-related quality of life developed specifically for application in young people. The second objective was to compare BWS DCE questions (where respondents are asked to indicate the best and worst attribute for each of a number of health states, presented one at a time) with conventional time trade-off (TTO) and standard gamble (SG) questions in terms of ease of understanding and completeness.A feasibility study sample of consenting young adolescent school children (n16) aged 1113 years participated in a face-to-face interview in which they were asked to indicate the best and worst attribute levels from a series of health states defined by the CHU9D, presented one at a time. Participants were also randomly allocated to receive additional conventional TTO or SG questions and prompted to indicate how difficult they found them to complete.The results indicate that participants were able to readily choose 'best' and 'worst' dimension levels in each of the CHU9D health states presented to them and provide justification for their choices. Furthermore, when presented with TTO or SG questions and prompted to make comparisons, participants found the BWS DCE task easier to understand and complete.The results of this feasibility study suggest that BWS DCE methods are potentially more readily understood and interpretable by vulnerable populations (e.g. young adolescents). These findings lend support to the potential application of BWS DCE methods to undertake large-scale health state valuation studies directly with young adolescent population samples. © 2011 Adis Data Information BV. All rights reserved.

Background: One of the greatest difficulties in evaluating healthcare system reform in any country is that governments often do not clearly articulate what it is they are attempting to do. In Australia, a recent inquiry set out 15 principles to guide the reform process, but it remains unclear how the Australian public values the principles, how such values vary across the country, and, more fundamentally, if Australians understand the principles. Objectives: To evaluate the Australian healthcare reform principles from the perspective of the Australian public, to test if such preferences are valued consistently across geographic and socioeconomic strata, and to test for the degree of understanding of the principles among the public. Methods: We employed best-worst scaling (BWS), a stated-preference method grounded in random utility theory, to elicit public preference for 15 healthcare reform principles. The BWS tasks were incorporated into an online survey that also gathered geographic and socioeconomic information and included questions relating to the understanding of the reform principles. Respondents were a geographically diverse set of Australians who were randomized to receive one of two versions of the survey, each containing a block of 15 choice tasks. Tasks in block one contained a subset of the choice tasks containing subsets of seven principles based on a balanced incomplete block design, while tasks in block two contained tasks with eight principles defined by the complement of the former. In each BWS task, respondents were simply asked to identify the most and least important principle. Analysis of preference was based on assigning the most valued principles a 1 and the least valued principles -1, and with each item appearing eight times in each block, preferences were analyzed over a cardinal utility scale bounded by -8 and +8. Analysis was based on simple summary statistics and stratified by geographic and socioeconomic measures. Results: A sample of 204 respondents participated in the survey (a participation rate of 85%). Quality and safety was the most important principle and a culture of reflective improvement and innovation was the least important. Public voice and community engagement was the second least important principle and was also understood by barely half the respondents. Conclusions: This research demonstrates how random-utility-based methods can be used to provide estimates of the importance of reform principles that have known statistical properties. The BWS task used forced respondents to discriminate between the principles on offer, unlike rating scales. Researchers and practitioners in healthcare should consider using BWS tasks in preference to rating scales. © 2010 Adis Data Information BV. All rights reserved.

We briefly review and discuss traditional conjoint analysis (CA) and discrete choice experiments (DCEs), widely used stated preference elicitation methods in several disciplines. We pay particular attention to the origins and basis of CA, and show that it is generally inconsistent with economic demand theory, and is subject to several logical inconsistencies that make it unsuitable for use in applied economics, particularly welfare and policy assessment. We contrast this with DCEs that have a long-standing, well-tested theoretical basis in random utility theory, and we show why and how DCEs are more general and consistent with economic demand theory. Perhaps the major message, though, is that many studies that claim to be doing conjoint analysis are really doing DCE.

This paper deals with three concerns about the evaluative framework that is currently dominant within health economics. These concerns are: that the evaluative framework is concerned entirely with health; that the evaluative framework has an individualistic focus on patients alone; and that the methods used to estimate 'health' within the current evaluative framework could be improved both in terms of the generation of descriptive systems and in using valuation methods that rely less on people's ability to express their preferences on a cardinal scale. In exploring these issues the Investigating Choice Experiments for Preferences of Older People (ICEPOP) programme has explicitly focused on both the topic of older people and the methods of discrete choice experiments. A capability index has been developed and attributes for an economic measure of end-of-life care are currently being generated, providing the possibility of extending the evaluative framework beyond health alone. A measure of carer's experience and a framework for extending measurement in end-of-life care to loved ones are both also in development, thus extending the evaluative framework beyond the patient alone. Rigorous qualitative methods employing an iterative approach have been developed for use in constructing attributes, and best-worst scaling has been utilized to reduce task complexity and provide insights into heterogeneity. There are a number of avenues for further research in all these areas, but in particular there is need for greater attention to be paid to the theory underlying the evaluative framework within health economics. © The Royal Society of Medicine Press Ltd 2008.

We characterize a class of probabilistic choice models where the choice probabilities depend on two scales, one with a value for each available option and the other with a value for the set of available options. Then, we develop similar results for a task in which a person is presented with a profile of attributes, each at a pre-specified level, and chooses the best or the best and the worst of those attribute-levels. The latter design is an important variant on previous designs using best–worst choice to elicit preference information, and there is various evidence that it yields reliable interpretable data. Nonetheless, the data from a single such task cannot yield separate measures of the “importance” of an attribute and the “utility” of an attribute-level. We discuss various empirical designs, involving more than one task of the above general type, that may allow such separation of importance and utility.

This paper reports the first application of the capabilities approach to the development and valuation of an instrument for use in the economic evaluation of health and social care interventions. The ICECAP index of capability for older people focuses on quality of life rather than health or other influences on quality of life, and is intended to be used in decision making across health and social care in the UK. The measure draws on previous qualitative work in which five conceptual attributes were developed: attachment, security, role, enjoyment and control. This paper details the innovative use within health economics of further iterative qualitative work in the UK among 19 informants to refine lay terminology for each of the attributes and levels of attributes used in the eventual index. For the first time within quality of life measurement for economic evaluation, a best-worst scaling exercise has been used to estimate general population values (albeit for the population of those aged 65+ years) for the levels of attributes, with values anchored at one for full capability and zero for no capability. Death was assumed to be a state in which there is no capability. The values obtained indicate that attachment is the attribute with greatest impact but all attributes contribute to the total estimation of capability. Values that were estimated are feasible for use in practical applications of the index to measure the impact of health and social care interventions. © 2008 Elsevier Ltd. All rights reserved.

There is growing use of discrete choice experiments (DCEs) to investigate preferences for products and programs and for the attributes that make up such products and programs. However, a fundamental issue overlooked in the interpretation of many choice experiments is that attribute parameters estimated from DCE response data are confounded with the underlying subjective scale of the utilities, and strictly speaking cannot be interpreted as the relative "weight" or "impact" of the attributes, as is frequently done in the health economics literature. As such, relative attribute impact cannot be compared using attribute parameter size and significance. Instead, to investigate the relative impact of each attribute requires commensurable measurement units; that is, a common, comparable scale. We present and demonstrate empirically a menu of five methods that allow such comparisons: (1) partial log-likelihood analysis; (2) the marginal rate of substitution for non-linear models; (3) Hicksian welfare measures; (4) probability analysis; and (5) best-worst attribute scaling. We discuss the advantages and disadvantages of each method and suggest circumstances in which each is appropriate. © 2006 Elsevier Ltd. All rights reserved.

Although efficient designs have sample size advantages for discrete choice experiments (DCEs), it has been hypothesised that they may result in biased estimates owing to some respondents using simplistic heuristics. The main objective was to provide a case study documenting that many respondents choose on the basis of a single attribute when exposed to highly efficient DCE designs but switch to a conventional multi-attribute decision rule when the design efficiency was lowered (resulting in less need to trade across all attributes). Additional objectives included comparisons of the sizes of the estimated coefficients and characterisation of heterogeneity, thus providing evidence of the magnitude of bias likely present in highly efficient designs. Five hundred and twenty-five respondents participating in a wider end-of-life survey each answered two DCEs that varied in their design efficiency. The first was a Street and Burgess 100 % efficient Orthogonal Main Effects Plan design (27 in 8), using the top and bottom levels of all attributes. The second DCE comprised one eighth of the full Orthogonal Main Effects Plan in 32 pairs, (a 2 × 46). Linear probability models estimated every respondent’s complete utility function in DCE1. The number of respondents answering on the basis of one attribute level was noted, as was the proportion of these who then violated this rule in DCE2, the less efficient DCE. Latent class analyses were used to identify heterogeneity. Sixty per cent of respondents answered all eight tasks comprising DCE1 using a single attribute; most used the rule “choose cheapest end-of-life care plan”. However, when answering the four less efficient tasks in DCE2, one third of these (20 % overall) then traded across attributes at least once. Among those whose decision rule could not be described qualitatively, latent class models identified two classes; compared to class one, class two was more concerned with quality rather than cost of care and wished to die in an institution rather than at home. Higher efficiency was also associated with smaller regression coefficients, suggesting either weaker preferences or lower choice consistency (larger errors). This is the first within-subject study to investigate the association between DCE design efficiency and utility estimates. It found that a majority of people did not trade across attributes in the more efficient design but that one third of these then did trade in the less efficient design. More within-subject studies are required to establish how common this is. It may be that future DCEs should attempt to maximise some joint function of statistical and cognitive efficiency to maximise overall efficiency and minimise bias.

As noted in Chapter 1, the BWS object case (henceforth, Case 1) deals with a list of objects, or things, that one wants to measure. Generally speaking, Case 1 can be used to measure things typically measured with rating scales and/or with variants of the method of paired comparisons (Thurstone, 1928 David, 1988). The types of things that Case 1 can be used to measure include the following indicative, but by no means exhaustive, list: • batteries of "agree/disagree" statements • traits or characteristics that apply to brands, people, even animals, etc. • pictures or graphical images, such as landscapes • politically relevant issues • electoral candidates • food products, wines, drinks, etc., tasted blind/non-blind • potential job candidates • academic journals and • product features. One first develops a list for measurement purposes then the process of using Case 1 to measure the things on the list is straightforward, which is part of its appeal. We are agnostic about the various ways researchers using Case 1 can compile lists of things to be measured. Therefore, we assume for the purpose of this discussion that a list of things is generated exogenous to the Case 1 process. It is the researcher's job to validate and justify the list and the things contained in it. However, we note that Case 1 can be applied equally to poorly justified and well-justified lists. Hence, poorly constituted and ill-justified lists of things to be measured have nothing whatsoever to do with the validity of BWS theory and measurement methods. Instead, the onus of doing this properly falls fully on researchers.

Best-worst scaling (BWS) is an extension of the method of paired comparison to multiple choices that asks participants to choose both the most and the least attractive options or features from a set of choices. It is an increasingly popular way for academics and practitioners in social science, business, and other disciplines to study and model choice. This book provides an authoritative and systematic treatment of best-worst scaling, introducing readers to the theory and methods for three broad classes of applications. It uses a variety of case studies to illustrate simple but reliable ways to design, implement, apply, and analyze choice data in specific contexts, and showcases the wide range of potential applications across many different disciplines. Best-worst scaling avoids many rating scale problems and will appeal to those wanting to measure subjective quantities with known measurement properties that can be easily interpreted and applied.

The corruption of the best things gives rise to the worst. David Hume (1711–1776) (1889 [1757], sect. 10, 339) 4.1 Introduction This chapter describes and discusses the final BWS case (Case 3), which is the multi-profile case. This case is closely related to the literature on discrete choice experiments, with two key differences: (1) the response task associated with DCEs for this case is a choice of the best and the worst profile (and/or choices of the best and the worst profile plus second-best, second-worst, etc.); and (2) an emphasis on individual-level analysis. The plan of the chapter is as follows: we briefly review traditional DCE literature to provide some familiarity with this stream of research; we then show how Case 3 models logically extend prior work, and discuss departures noted above; we next describe, discuss and illustrate Case 3 design, implementation and analysis; and conclude with unresolved issues and future research opportunities. DCEs are not new; the method of paired comparisons was formalized by Thurstone (1927). DCE developments since 1980 (1) concern multiple and paired choices, (2) rely on random-utility-theory-based probabilistic discrete choice models developed after 1970 (for example, McFadden, 1974), and (3) typically are constructed using principles from the design of statistical experiments. The latter stream of work was initiated by Louviere and Woodworth (1983), who pioneered a way to design multiple choice experiments consistent with McFadden's (1974) conditional logit model and more general models such as “mother logit” (McFadden, Train and Tye, 1978). Prior to Louviere and Woodworth (1983) there was little work on designed experiments for multiple choices, and researchers often used ad hoc combinations of pairs, triples, quadruples, etc. as choice experiments. Louviere and Woodworth provided a systematic way to design DCEs based on ideas from the design of statistical experiments. The focus in this chapter continues this tradition, and we describe and discuss relevant new developments when appropriate. Recent reviews of DCE literature include those by Louviere, Hensher and Swait (2000), Louviere (2001), Louviere and Street (2000), Louviere, Street and Burgess (2003), Street, Burgess and Louviere (2005), Burgess et al . (2006), Street et al . (2008), Street and Burgess (2007), Louviere and Lancsar (2009) and Rose and Bliemer (2009). Thus, there is no need to review what is already well known and well worn, so we give only brief details when they are relevant.

Singapore is in the midst of several healthcare reforms in efforts to finance and deliver health services for a rapidly aging population. The primary focus of these reforms is to make healthcare services, including those at the end of life (EOL), affordable. Given the increasingly high health care costs at the EOL, policy makers need to consider how best to allocate resources. One strategy is to allocate resources based on the preferences of sub-populations most likely to be affected. This paper thus aims to quantify preferences for EOL care both among community dwelling older adults (CDOAs) and among patients with a life-limiting illness. A discrete choice experiment was administered to CDOAs and advanced cancer patients in Singapore and willingness to pay (WTP) for specific EOL improvements was estimated. We find that patients have a higher WTP for nearly all EOL attributes compared with CDOAs. We also show that, for both groups, moderate life extension is not the most important consideration; WTP for one additional life year is lower than common thresholds for cost-effectiveness. Irrespective of whose preference are considered, the results highlight the importance of pain management and supporting home deaths at the EOL, perhaps at the expense of public funding for costly but only marginally effective treatments. (C) 2015 Elsevier Ireland Ltd. All rights reserved. Refereed/Peer-reviewed

Best worst scaling (BWS) can be a method of data collection, and/or a theory of how respondents provide top and bottom ranked items from a list. The three ‘cases’ of BWS are described, followed by a summary of the main models and related theoretical results, including an exposition of possible theoretical relationships between estimates from two of the cases. This is followed by the theoretical and empirical properties of ‘best minus worst scores.’ The entry ends with some directions forfuture research. Refereed/Peer-reviewed

15.1 Introduction The purpose of this chapter is to show how to combine an alternative-specific DCE design with a BW choice task. Secondary aims are (1) to show how to add informative choice sets to DCE tasks to gain insights and information about individual differences and (2) to show how to analyze and report choice totals from this type of DCE task to gain additional insights. Most private sector organizations need to understand pricing and the likely impacts of differences in and/or changes to prices of their own product/service prices and/or the likely impacts of competitor price changes. Similarly, many public sector organizations need to understand and anticipate the consequences of various pricing policies, such as offering incentives to uptake solar appliances, tolls on bridges and highways, usage fees for parks, and so on. As a result, there are numerous potential choice model pricing applications in both sectors. This chapter illustrates one such application, namely modeling the choices of a relevant sample of individuals faced with different airlines and fares for a holiday trip. I study the flight choices of a sample of 214 individuals randomly sampled from the Pureprofile online panel in Australia who (1) reside in the Sydney metropolitan area, (2) have flown to northern New South Wales (NSW) for a weekend or longer holiday in the past three years or (3) anticipate doing so in the next three years. At the time of the study there were five potential airline choices for such flights within NSW: Jetstar, Qantas, Rex, Tiger and Virgin. Fares for such flights ranged approximately between A$89 and A$239 at various times during the three years prior to the study. Differences between flights within NSW are not large, as most flights are no more than an hour. Airlines flying within NSW try to claim significant differences in services, and, to the extent that such differences exist, I would expect to see them manifest in differential choices of particular airlines. Thus, one can model and quantify preferences for airlines and fares by designing an alternative-specific discrete choice experiment (ASDCE). Typically, an ASDCE is appropriate when individuals in the market of interest will choose specific offers, such as brands, transport modes, holiday destinations, etc.

OBJECTIVES: In contrast to the proliferation of studies incorporating health state values from adults of all ages, relatively few studies have reported upon the application of the time trade off (TTO) approach to generate health state values from populations of younger adults. This study sought to employ a conventional TTO approach to obtain values for a selection of Child Health Utility 9D (CHU9D) health states from a sample of young adults aged 18-29 years and to compare with the values generated from application of the original UK adult standard gamble scoring algorithm and the Australian adolescent scoring algorithm. \ud \ud METHODS: A convenience sample of Flinders University undergraduate students aged 18-29 years were invited to participate in an interviewer administered conventional TTO task to value a series of five CHU9D health impairment states using the widely used variant developed by the York EQ-5D team. \ud \ud RESULTS: A total of 152 students within the target age range were approached to participate in the study of whom n = 38 consented to participate, giving an overall participation rate of 25 %. With the exception of one health state, the mean TTO values were consistently lower than those generated from application of the original scoring algorithm for the CHU9D elicited with adults of all ages. A significant proportion of participants (n = 17, 45 %) considered the most severe CHU9D (PITS) state to be worse than death. \ud \ud CONCLUSIONS: This study adds to a growing body of evidence indicating that the values attached to identical health states are typically lower for younger people in comparison with adults of all ages and dependent upon the elicitation method utilised. The values obtained are applicable for re-scaling raw CHU9D health state values obtained from younger adolescent samples using profile case best-worst scaling.

BACKGROUND: General practitioners with special interests (GPSIs) are increasingly being used to provide dermatology services in the U.K. Little is known about U.K. dermatology patient attitudes to ...

Objectives:This work has investigated under what conditions cost-effectiveness data from a cluster randomized trial (CRT) are suitable for analysis using a cluster-adjusted nonparametric bootstrap. The bootstrap's main advantages are in dealing with skewed data and its ability to take correlations between costs and effects into account. However, there are known theoretical problems with a commonly used cluster bootstrap procedure, and the practical implications of these require investigation.Methods:Simulations were used to estimate the coverage of confidence intervals around incremental cost-effectiveness ratios from CRTs using two bootstrap methods.Results:The bootstrap gave excessively narrow confidence intervals, but there was evidence to suggest that, when the number of clusters per treatment arm exceeded 24, it might give acceptable results. The method that resampled individuals as well as clusters did not perform well when cost and effectiveness data were correlated.Conclusions:If economic data from such trials are to be analyzed adequately, then there is a need for further investigations of more complex bootstrap procedures. Similarly, further research is required on methods such as the net benefit approach.

Funding: SPIRIT is funded as part of the Centre for Informing Policy in Health with Evidence from Research (CIPHER), an Australian National Health and Medical Research Council Centre for Research Excellence (APP1001436) and administered by the University of Western Sydney. The Sax Institute receives a grant from the NSW Ministry of Health. Introduction: Governments in different countries have committed to better use of evidence from research in policy. Although many programmes are directed at assisting agencies to better use research, there have been few tests of the effectiveness of such programmes. This paper describes the protocol for SPIRIT (Supporting Policy In health with Research: an Intervention Trial), a trial designed to test the effectiveness of a multifaceted programme to build organisational capacity for the use of research evidence in policy and programme development. The primary aim is to determine whether SPIRIT results in an increase in the extent to which research and research expertise is sought, appraised, generated and used in the development of specific policy products produced by health policy agencies. Methods and analysis: A stepped wedge cluster randomised trial involving six health policy agencies located in Sydney, Australia. Policy agencies are the unit of randomisation and intervention. Agencies were randomly allocated to one of three start dates (steps) to receive the 1-year intervention programme, underpinned by an action framework. The SPIRIT intervention is tailored to suit the interests and needs of each agency and includes audit, feedback and goal setting; a leadership programme; staff training; the opportunity to test systems to assist in the use of research in policies; and exchange with researchers. Outcome measures will be collected at each agency every 6 months for 30 months (starting at the beginning of step 1). Ethics and dissemination: Ethics approval was granted by the University of Western Sydney Human Research and Ethics Committee HREC Approval H8855. The findings of this study will be disseminated broadly through peer-reviewed publications and presentations at conferences and used to inform future strategies. Publisher PDF

Discrete choice experiments (DCEs) are used to elicit preferences of current and future patients and healthcare professionals about how they value different aspects of healthcare. Risk is an integral part of most healthcare decisions. Despite the use of risk attributes in DCEs consistently being highlighted as an area for further research, current methods of incorporating risk attributes in DCEs have not been reviewed explicitly. This study aimed to systematically identify published healthcare DCEs that incorporated a risk attribute, summarise and appraise methods used to present and analyse risk attributes, and recommend best practice regarding including, analysing and transparently reporting the methodology supporting risk attributes in future DCEs. The Web of Science, MEDLINE, EMBASE, PsycINFO and Econlit databases were searched on 18 April 2013 for DCEs that included a risk attribute published since 1995, and on 23 April 2013 to identify studies assessing risk communication in the general (non-DCE) health literature. Healthcare-related DCEs with a risk attribute mentioned or suggested in the title/abstract were obtained and retained in the final review if a risk attribute meeting our definition was included. Extracted data were tabulated and critically appraised to summarise the quality of reporting, and the format, presentation and interpretation of the risk attribute were summarised. This review identified 117 healthcare DCEs that incorporated at least one risk attribute. Whilst there was some evidence of good practice incorporated into the presentation of risk attributes, little evidence was found that developing methods and recommendations from other disciplines about effective methods and validation of risk communication were systematically applied to DCEs. In general, the reviewed DCE studies did not thoroughly report the methodology supporting the explanation of risk in training materials, the impact of framing risk, or exploring the validity of risk communication. The primary limitation of this review was that the methods underlying presentation, format and analysis of risk attributes could only be appraised to the extent that they were reported. Improvements in reporting and transparency of risk presentation from conception to the analysis of DCEs are needed. To define best practice, further research is needed to test how the process of communicating risk affects the way in which people value risk attributes in DCEs.

Best-Worst Scaling (BWS) can be a method of data collection, and/or a theory of how respondents provide top and bottom ranked items from a list. BWS is increasingly used to obtain more choice data from individuals and/or to understand choice processes. The three “cases”of BWS are described, together with the intuition behind the models that are applied in each case. A summary of the main theoretical results is provided, including an exposition of the possible theoretical relationships between estimates from the different cases, and of the theoretical properties of “best minus worst scores.”BWS data can be analysed using relatively simple extensions to maximum-likelihood based methods used in discrete choice experiments. These are summarised, before the benefits of simple functions of the best and worst counts are introduced. The chapter ends with some directions for future research. keywords: best worst scaling; discrete choice experiments; maxdiff model; repeated best and/or worst choice: scores.

Discrete choice experiments - selecting the best and/or worst from a set of options—are increasingly used to provide more efficient and valid measurement of attitudes or preferences than conventional methods such as Likert scales. Discrete choice data have traditionally been analyzed with random utility models that have good measurement properties but provide limited insight into cognitive processes. We extend a well-established cognitive model, which has successfully explained both choices and response times for simple decision tasks, to complex, multi-attribute discrete choice data. The fits, and parameters, of the extended model for two sets of choice data (involving patient preferences for dermatology appointments, and consumer attitudes toward mobile phones) agree with those of standard choice models. The extended model also accounts for choice and response time data in a perceptual judgment task designed in a manner analogous to best–worst discrete choice experiments. We conclude that several research fields might benefit from discrete choice experiments, and that the particular accumulator-based models of decision making used in response time research can also provide process-level instantiations for random utility models. Refereed/Peer-reviewed

Objective: To assess the economic impact of adding granulocyte colony-stimulating factor (G-CSF) to amphotericin B to treat a presumed deep-seated fungal infection in neutropenic patients. This study was conducted from the perspective of the National Health Service (NHS) hospital sector. Design: We used our previously reported trial as the clinical basis for the analysis (see Participants and interventions). These data were combined with resource utilisation data, enabling us to construct a decision tree model of the treatment paths attributable to managing patients in each arm of the trial. The model was used to calculate the cost effectiveness of using amphotericin B plus G-CSF compared to amphotericin B monotherapy in neutropenic patients with a presumed deep-seated fungal infection. Setting: An adult leukaemia/bone marrow transplant (BMT) unit in a large UK teaching hospital. Participants: Patients with a neutrophil count of

Choice modelling has been one of the most active fields in economics over recent years. This valuable new book contains leading contributions from academics and practitioners from across the different areas of study where choice modelling is a key analytical technique, drawn from a recent international conference.

Older people's valuation of health-related aspects of quality of life may be altered by response shift, where they lower expectations of aspects of well-being that are believed to naturally deteriorate with age. Policy-makers may wish to adjust estimated preferences if these reflect past inequities in health funding rather than the true production possibilities. Response shift might be quantified by changing the context of the choice task. The ICECAP-O valuation exercise achieved this by asking a binary choice holistic decision of respondents, in addition to the case 2 best-worst choice task among the five attributes. Answers to the former are more likely to be subject to response shift since they involve traditional trade-offs. Answers to the latter reflect only ‘relative disutility’ of various impairments. Individual level estimates for the latter were substituted into the design matrix in a series of latent class analyses of the binary choice data. The five attribute mean estimates from the conditional logit regressions are the attribute importance parameters and represent the (internal) scaling factors that respondents use in transforming their case 2 BWS utilities into ones relevant in multi-profile decision-making. The principal hypothesis was that there were classes of respondents who used identical attribute importance weights. Rejection of this hypothesis prompted testing of secondary hypotheses that respondents placed lower importance on control (independence) and role (doing things that make you feel valued), those attributes thought to be most vulnerable to response shift. Results showed that 17% of respondents never traded, in most cases illogically given their own ICECAP-O responses, and were dropped. Tests of parameter-equality suggested at least 30% and possibly as many as 53% of respondents for whom there is a single statistically significant attribute importance factor and 64 (21%) of respondents for whom there is a single statistically non-significant attribute importance factor. The remaining 9% of respondents had a moderate status-quo bias (preference for own life). These results do not provide strong support for response shift in the ICECAP-O valuation sample. There is only very limited support for differential weights for the five attributes when moving into a traditional DCE framework. Refereed/Peer-reviewed

\ud Purpose To investigate the construct validity of the\ud ICECAP-A capability wellbeing measure.\ud Methods A face-to-face interview-administered survey\ud was conducted with 418 members of the UK general\ud population, randomly sampled from the Postcode Address\ud File. Pre-specified hypotheses were developed about the\ud expected associations between individuals’ ICECAP-A\ud responses and their socio-economic circumstances, health\ud and freedom. The hypotheses were investigated using statistical\ud tests of association.\ud Results The ICECAP-A responses and scores reflected\ud differences across different health and socioeconomic\ud groups as anticipated, but did not distinguish individuals by\ud the level of local deprivation. Mean ICECAP-A scores\ud reflected individuals’ perceived freedom slightly more\ud closely than did measures of health and happiness.\ud Conclusion This study suggests that the ICECAP-A\ud measure can identify expected differences in capability\ud wellbeing in a general population sample. Further work\ud could establish whether self-reported capabilities exhibit\ud desirable validity and acceptability in sub-groups of the\ud population such as patients, social care recipients and\ud informal carers.

Objectives: To assess the applicability of the newly developed ICECAP-O capability index in the measurement and valuation of quality of life in a large community based sample of the Australian general population. With origins in Sen's capability theory, the ICECAP-O may more fully encapsulate the multi-dimensional outcomes of public health policies and interventions than traditional health economic constructs. Methods: 2,937 Australian residents participated in face-to-face interviews. The relationships between ICECAP-O scores according to age groups (65 years) and socio-economic status were investigated using descriptive statistics and multivariable linear regression models. Results: Lower income levels and being unemployed or physically unable to work were negatively associated with capability for both age groups. Capability was strongly and positively associated with marriage and cohabitation in the younger age group, whilst being Australian-born was a positive indicator for the older group. Conclusions: The results provide insights into the assessment of capability in the Australian general population. The ICECAP-O shows promise for application in the measurement and valuation of quality of life in general population surveys, and incorporation into economic evaluations of public health interventions. © 2012 Swiss School of Public Health.

Objectives: There is a growing need for efficient procedures for identification of emerging technologies by horizon scanning systems. We demonstrate the value of best-worst scaling (BWS) in exploring clinicians’ views on emerging technologies that will impact outcomes in hepatocellular carcinoma (HCC) in the next 5 to 10 years.Methods: Clinicians in Asia, Europe, and the United States were surveyed and their views about eleven emerging technologies relevant to HCC were explored using BWS (case 1). This involved systematically presenting respondents with subsets of five technologies and asking them to identify those that will have the most and least impact on HCC within 5 to 10 years. Statistical analysis was based on sequential best-worst and analyzed using conditional logistic regression.Results: A total of 120 clinicians uniformly distributed across ten countries completed the survey (37 percent response rate). Respondents were predominately hepatologist (41 percent) who focused on HCC (65 percent) and had national influence in this field (39 percent). Respondents viewed molecular targeted therapy (p < .001) and early detection of HCC (p < .001) as having most potential, while improved surgical techniques (p < .001) and biopsy free HCC diagnostics (p < .001) were viewed upon negatively.Conclusions: We demonstrate that BWS could be an important research tool to facilitate horizon scanning and HTA more broadly. Our research demonstrates the value of including clinicians’ preferences as a source of data in horizon scanning, but such methods could be used to incorporate the opinions of a broad array of stakeholders, including those in advocacy and public policy.

Objective: The aim of this study was to develop a measure of social care outcome, an equivalent to the quality-adjusted life year (QALY) in health, which could be used in a range of circumstances. Design: The project drew on previous and parallel work developing the Adult Social Care Outcome Toolkit and the national Adult Social Care Survey. We developed and tested an instrument designed to reflect service users' social care-related quality of life (SCRQoL) and tested it with 30 service users from a variety of user groups and 300 older home care service users. In parallel, we explored discrete choice experiment (DCE) and best-worst scaling (BWS) approaches to preference elicitation with 300 members of the general population, and cognitively tested these with service users. We also cognitively tested a computer-aided time trade-off (TTO) exercise using SCRQoL attributes with members of the general population. In the second phase, using the finalised instruments, BWS interviews were conducted with 500 members of the general population, TTO interviews with a follow-up sample of 126 of these respondents, and BWS interviews with 458 people using equipment services. Main outcome measures: The final measure had eight domains: personal cleanliness and comfort, accommodation cleanliness and comfort, food and drink, safety, social participation and involvement, occupation, control over daily life and dignity. In addition to measuring current SCRQoL, the instrument includes questions used to establish service users' views of their 'expected' SCRQoL in the absence of services. The difference between a person's current and 'expected' SCRQoL provides an indicator of service impact. Results: There was good evidence for the validity of the descriptive system and the validity of the current, expected and SCRQoL gain scales. The DCE and BWS approaches yielded similar results and, once introductions made clear, were understood by service users. BWS was used for the main stages, as it had technical and cognitive advantages. The computeraided approach to TTO worked well, and respondents found questions acceptable and understandable. There were no substantive differences in the preferences of service users and the general population. The key domain was control over daily life, with the lowest and highest levels strongly estimated in all models. After allowing for observable heterogeneity, service users' preferences appeared to be more closely associated with their own SCRQoL than with those of the general population. The consistency of the results with the results of a previous study allowed the final model to be based on the preferences of 1000 members of the general population. A formula based on the relationship between TTO and BWS values was estimated for a social care QALY, with '0' equivalent to 'being dead' and '1' being the 'ideal' SCRQoL state. Members of the population experienced significantly higher SCRQoL than service users. Conclusions: Although further work is needed, particularly to develop an equivalent measure for informal carers and to explore the links with health QALYs, the measure has considerable potential. A number of methodological advances were achieved, including the first application of TTO in a social care context and use of BWS to establish service user preferences. Funding: The National Institute for Health Research Health Technology Assessment programme. © Queen's Printer and Controller of HMSO 2012.

Author version not available., The way that health is measured and valued is fundamental to economic evaluation. To date, adult health state values have been routinely used in the calculation of QALYs for the economic evaluation of healthcare treatment and preventive programmes, including those targeted at adolescents. The main objective of this study was to apply profile case best-worst scaling (BWS) discrete-choice experiment (DCE) methods to obtain adolescent-specific values for the Child Health Utility 9D (CHU9D), a new generic preference-based measure of health-related quality of life developed specifically for application in cost-effectiveness analyses of treatments and interventions targeted at young people. A secondary aim was to assess the feasibility of a web-based method of data collection for the valuation of health states defined by the CHU9D. A web-based survey was developed including the CHU9D instrument and a series of BWS DCE questions. Specifically, respondents were asked to indicate the best and worst attribute levels from a series of ten health states defined by the CHU9D, presented one at a time. The survey was administered to a community-based sample of consenting adolescents (n = 590) aged 11-17 years. A conditional logistic regression model was applied to estimate values (part-worth utilities) for each level of the nine attributes relating to the CHU9D. A marginal utility matrix was then estimated to generate an adolescent-specific scoring algorithm on the full health = 1 and dead = 0 scale required for the calculation of QALYs. The results indicate that participants were able to readily choose 'best' and 'worst' attribute levels for the CHU9D health states. Large differences in value were found between the first and fifth levels (indicating 'no problems' and 'severe problems', respectively) for all nine attributes relating to the CHU9D. In general, there was little differentiation between the middle levels of all attributes indicating only limited additional value for adolescents of moving between these levels. Comparison of the adolescent-specific algorithm and the existing adult scoring algorithm for the CHU9D revealed some significant differences in values for identical health states, which may have important implications for the application of the CHU9D to value adolescent treatment and service programmes particularly for mental health. In general, adolescents appeared to place more weight upon the CHU9D attributes relating to mental health (worried, sad and annoyed) than would be implied by application of the existing algorithm based upon adult values. This study provides preliminary indications that there may be potentially important and systematic differences in the valuations attached to identical health states by adolescents in comparison with adult population groups. The study findings lend support to the potential future application of profile case BWS DCE methods to undertake large-scale health state valuation studies directly with young adolescent population samples and provide support for the feasibility and acceptability of a web-based mode of administration for this purpose.

Purpose: To assess the construct validity of the CHU9D in an adolescent general population sample. The CHU9D is a new generic preference-based measure of health-related quality of life developed specifically for application in the economic evaluation of health care treatments and interventions for young people. Methods: A web-based survey was developed including the CHU9D and HUI2 instruments and administered to a community-based sample of consenting adolescents (n = 710) aged 11-17 years. The practicality, face and construct validity of the CHU9D was examined. The relationship between the CHU9D and HUI2 instruments was assessed by a comparison of responses to similar dimensions and the utility scores derived from the two instruments. Results: The CHU9D demonstrated high completion rates. CHU9D was able to discriminate between respondents according to their self-reported general health (Kruskal-Wallis P value