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Health programs/services are often bundled, allowing for both substitution and complementarity. We adapt Discrete Choice Experiments to capture bundling, with application to a case study of exercise and nutrition; complementarity arises due to the goal of improving health. Our contributions are (1) to present a menu-based choice experiment to explore bundling; (2) to analyse the menu-based data using an extension of the choice set generation model (GenL) to account for correlations between bundles and component singles. A nationally representative sample of 333 Australians chose between a nutrition program only; exercise program only; both nutrition and exercise programs; or their status quo. Overall, we show that by incorporating the menu choice task and introducing the combined alternative, we capture a significant portion of the population seeking both exercise and nutrition components. We estimate a latent class GenL model, and identify two latent classes: Class 1 preferred to choose programs on offer, and Class 2 was more price sensitive and had a stronger preference for staying with their status quo. We show in the post-estimation analysis that heterogeneity in preferences translates into heterogeneity in the way alternatives are bundled, indicating that the combined offering is appealing to specific classes of individuals who prefer bundling. By implementing the menu choice task, researchers and policymakers can effectively identify, cater to and influence the demand for combined exercise and nutrition options, leading to more targeted and impactful interventions in promoting healthier lifestyle choices., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Labeled discrete choice experiments (DCEs) commonly present all alternatives using a full choice set design (FCSD), which could impose a high cognitive burden on respondents. In the setting of employment preferences, this study explored if a partial choice set design (PCSD) reduced cognitive burden whilst maintaining convergent validity compared with a FCSD. Respondents' preferences between the two designs were investigated. In the experimental design, labeled utility functions were rewritten into a single generic utility function using label dummy variables to generate an efficient PCSD with 3 alternatives shown in each choice task (out of 6). The DCE was embedded in a nationwide survey of 790 Australian pharmacy degree holders where respondents were presented with both a block of FCSD and PCSD tasks in random order. The PCSD's impact on error variances was investigated using a heteroscedastic conditional logit model. The convergent validity of PCSD was based on the equality of willingness-to-forgo-expected-salary estimates from Willingness-to-pay-space mixed logit models. A nested logit model was used combined with respondents' qualitative responses to understand respondents' design preferences. We show a promising future use of PCSD by providing evidence that PCSD can reduce cognitive burden while satisfying convergent validity compared to FCSD., (© 2023 The Authors. Health Economics published by John Wiley & Sons Ltd.)

This study undertook a head-to-head comparison of best-worst, best-best and ranking discrete choice experiments (DCEs) to help decide which method to use if moving beyond traditional single-best DCEs. Respondents were randomized to one of three preference elicitation methods. Rank-ordered (exploded) mixed logit models and respondent-reported data were used to compare methods and first and second choices. First choices differed from second choices and preferences differed between elicitation methods, even beyond scale and scale dynamics. First choices of best-worst had good choice consistency, scale dynamics and statistical efficiency, but this method's second choices performed worst. Ranking performed best on respondent-reported difficulty and preference; best-best's second choices on statistical efficiency. All three preference elicitation methods improve efficiency of data collection relative to using first choices only. However, differences in preferences between first and second choices challenge moving beyond single-best DCE. If nevertheless doing so, best-best and ranking are preferred over best-worst DCE., (© 2022 The Authors. Health Economics published by John Wiley & Sons Ltd.)

Objectives: The objective of this study was to investigate the degree of inconsistency in quality-of-life attribute preference orderings generated via successive best-worst scaling (a form of ranking whereby the respondent chooses the best and worst attributes from a choice set, these attributes are then eliminated and the best and worst attributes from the reduced choice set are then chosen and this process is continued until all presented attributes are eliminated) and conventional ranking methods (whereby the respondent chooses the best, second best and third best from a choice set until all presented attributes are eliminated)., Methods: An on-line survey was developed for administration to two general population samples comprising younger people (aged 18-64 years) and older people (aged 65 years and above). Data were analysed in STATA through an empirical examination of the relative level of choice inconsistency (randomness in responses or the variability in choice outcomes not explained by attributes and their associated preference weights) for successive best-worst in comparison with the conventional ranking method for the younger and older person samples., Results: For the younger person sample, both methods were found to be similarly consistent. In contrast, for the older person sample, ranking performed relatively worse than best-worst scaling with more inconsistent responses (tau = 0.515, p < 0.01)., Conclusions: These findings lend some support to the hypothesis initially propagated by the developers of best-worst scaling that it is a comparatively easier choice task for respondents to undertake than a traditional ranking task.

We provide a user guide on the analysis of data (including best-worst and best-best data) generated from discrete-choice experiments (DCEs), comprising a theoretical review of the main choice models followed by practical advice on estimation and post-estimation. We also provide a review of standard software. In providing this guide, we endeavour to not only provide guidance on choice modelling but to do so in a way that provides a 'way in' for researchers to the practicalities of data analysis. We argue that choice of modelling approach depends on the research questions, study design and constraints in terms of quality/quantity of data and that decisions made in relation to analysis of choice data are often interdependent rather than sequential. Given the core theory and estimation of choice models is common across settings, we expect the theoretical and practical content of this paper to be useful to researchers not only within but also beyond health economics.

Background: Discrete choice experiments with duration (DCE TTO ) can be used to estimate utility values for preference-based measures, such as the EQ-5D-5L. For self-completion, the health dimensions are presented in a standard order. However, for valuation, this may result in order effects. Thus, it is important to understand whether health state dimension ordering affects values. The aim of this study was to examine the importance of dimension ordering on DCE values using EQ-5D-5L., Methods: A choice experiment presenting two health profiles and a third immediate death option was developed. A three-arm study was used, with the same 120 choice sets presented online across each arm (n = 360 per arm). Arm 1 presented the standard EQ-5D-5L dimension order, arm 2 randomised order between respondents, and arm 3 randomised within respondents. Conditional logit regression was used to assess model consistency, and scale parameter testing was used to assess model poolability., Results: There were minor inconsistencies across each arm, but the magnitudes of the coefficients produced were generally consistent. Arm 3 produced the largest range of utility values (1 to -0.980). Scale parameter testing suggested that the models did not differ, and the data could be pooled. Follow-up questions did not suggest variation in terms of difficulty., Conclusions: The results suggest that the level of randomisation used in DCE health state valuation studies does not significantly impact values, and dimension order may not be as important as other study design issues. The results support past valuation studies that use the standard order of dimensions.

Perinatal depression and anxiety (PNDA) are an international healthcare priority, associated with significant short- and long-term problems for women, their children and families. Effective treatment is available but uptake is suboptimal: some women go untreated whilst others choose treatments without strong evidence of efficacy. Better understanding of women's preferences for treatment is needed to facilitate uptake of effective treatment. To address this issue, a discrete choice experiment (DCE) was administered to 217 pregnant or postnatal women in Australia, who were recruited through an online research company and had similar sociodemographic characteristics to Australian data for perinatal women. The DCE investigated preferences regarding cost, treatment type, availability of childcare, modality and efficacy. Data were analysed using logit-based models accounting for preference and scale heterogeneity. Predicted probability analysis was used to explore relative attribute importance and policy change scenarios, including how these differed by women's sociodemographic characteristics. Cost and treatment type had the greatest impact on choice, such that a policy of subsidising effective treatments was predicted to double their uptake compared with the base case. There were differences in predicted uptake associated with certain sociodemographic characteristics: for example, women with higher educational attainment were more likely to choose effective treatment. The findings suggest policy directions for decision makers whose goal is to reduce the burden of PNDA on women, their children and families.

In most societies resources are insufficient to provide everyone with all the health care they want. In practice, this means that some people are given priority over others. On what basis should priority be given? In this paper we are interested in the general public's views on this question. We set out to synthesis what the literature has found as a whole regarding which attributes or factors the general public think should count in priority setting and what weight they should receive. A systematic review was undertaken (in August 2014) to address these questions based on empirical studies that elicited stated preferences from the general public. Sixty four studies, applying eight methods, spanning five continents met the inclusion criteria. Discrete Choice Experiment (DCE) and Person Trade-off (PTO) were the most popular standard methods for preference elicitation, but only 34% of all studies calculated distributional weights, mainly using PTO. While there is heterogeneity, results suggest the young are favoured over the old, the more severely ill are favoured over the less severely ill, and people with self-induced illness or high socioeconomic status tend to receive lower priority. In those studies that considered health gain, larger gain is universally preferred, but at a diminishing rate. Evidence from the small number of studies that explored preferences over different components of health gain suggests life extension is favoured over quality of life enhancement; however this may be reversed at the end of life. The majority of studies that investigated end of life care found weak/no support for providing a premium for such care. The review highlights considerable heterogeneity in both methods and results. Further methodological work is needed to achieve the goal of deriving robust distributional weights for use in health care priority setting., (Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.)

Consumer directed care (CDC) is currently being embraced internationally as a means to promote autonomy and choice for consumers (people aged 65 and over) receiving community aged care services (CACSs). CDC involves giving CACS clients (consumers and informal carers of consumers) control over how CACSs are administered. However, CDC models have largely developed in the absence of evidence on clients' views and preferences. We explored CACS clients' preferences for a variety of CDC attributes and identified factors that may influence these preferences and potentially inform improved design of future CDC models. Study participants were clients of CACSs delivered by five Australian providers. Using a discrete choice experiment (DCE) approach undertaken in a group setting between June and December 2013, we investigated the relative importance to CACS consumers and informal (family) carers of gradations relating to six salient features of CDC (choice of service provider(s), budget management, saving unused/unspent funds, choice of support/care worker(s), support-worker flexibility and level of contact with service coordinator). The DCE data were analysed using conditional, mixed and generalised logit regression models, accounting for preference and scale heterogeneity. Mean ages for 117 study participants were 80 years (87 consumers) and 74 years (30 informal carers). All participants preferred a CDC approach that allowed them to: save unused funds from a CACS package for future use; have support workers that were flexible in terms of changing activities within their CACS care plan and; choose the support workers that provide their day-to-day CACSs. The CDC attributes found to be important to both consumers and informal carers receiving CACSs will inform the design of future CDC models of service delivery. The DCE approach used in this study has the potential for wide applicability and facilitates the assessment of preferences for elements of potential future aged care service delivery not yet available in policy., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

External validity is a crucial but under-researched topic when considering using discrete choice experiment (DCE) results to inform decision making in clinical, commercial or policy contexts. We present the theory and tests traditionally used to explore external validity that focus on a comparison of final outcomes and review how this traditional definition has been empirically tested in health economics and other sectors (such as transport, environment and marketing) in which DCE methods are applied. While an important component, we argue that the investigation of external validity should be much broader than a comparison of final outcomes. In doing so, we introduce a new and more comprehensive conceptualisation of external validity, closely linked to process validity, that moves us from the simple characterisation of a model as being or not being externally valid on the basis of predictive performance, to the concept that external validity should be an objective pursued from the initial conceptualisation and design of any DCE. We discuss how such a broader definition of external validity can be fruitfully used and suggest innovative ways in which it can be explored in practice.

Objectives: This exploratory study sought to investigate the effect of cognitive functioning on the consistency of individual responses to a discrete choice experiment (DCE) study conducted exclusively with older people., Methods: A DCE to investigate preferences for multidisciplinary rehabilitation was administered to a consenting sample of older patients (aged 65 years and older) after surgery to repair a fractured hip (N = 84). Conditional logit, mixed logit, heteroscedastic conditional logit, and generalized multinomial logit regression models were used to analyze the DCE data and to explore the relationship between the level of cognitive functioning (specifically the absence or presence of mild cognitive impairment as assessed by the Mini-Mental State Examination) and preference and scale heterogeneity., Results: Both the heteroscedastic conditional logit and generalized multinomial logit models indicated that the presence of mild cognitive impairment did not have a significant effect on the consistency of responses to the DCE., Conclusions: This study provides important preliminary evidence relating to the effect of mild cognitive impairment on DCE responses for older people. It is important that further research be conducted in larger samples and more diverse populations to further substantiate the findings from this exploratory study and to assess the practicality and validity of the DCE approach with populations of older people., (Copyright © 2014 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.)

Background: There is current interest in incorporating weights based on public preferences for health and healthcare into priority-setting decisions., Objective: The aim of this systematic review was to explore the extent to which public preferences and trade-offs for priority-setting criteria have been quantified, and to describe the study contexts and preference elicitation methods employed., Methods: A systematic review was performed in April 2013 to identify empirical studies eliciting the stated preferences of the public for the provision of healthcare in a priority-setting context. Studies are described in terms of (i) the stated preference approaches used, (ii) the priority-setting levels and contexts, and (iii) the criteria identified as important and their relative importance., Results: Thirty-nine studies applying 40 elicitation methods reported in 41 papers met the inclusion criteria. The discrete choice experiment method was most commonly applied (n = 18, 45.0 %), but other approaches, including contingent valuation and the person trade-off, were also used. Studies prioritised health systems (n = 4, 10.2 %), policies/programmes/services/interventions (n = 16, 41.0 %), or patient groups (n = 19, 48.7 %). Studies generally confirmed the importance of a wide range of process, non-health and patient-related characteristics in priority setting in selected contexts, alongside health outcomes. However, inconsistencies were observed for the relative importance of some prioritisation criteria, suggesting context and/or elicitation approach matter., Conclusions: Overall, findings suggest caution in directly incorporating public preferences as weights for priority setting unless the methods used to elicit the weights can be shown to be appropriate and robust in the priority-setting context.

Stated-preference methods are a class of evaluation techniques for studying the preferences of patients and other stakeholders. While these methods span a variety of techniques, conjoint-analysis methods-and particularly discrete-choice experiments (DCEs)-have become the most frequently applied approach in health care in recent years. Experimental design is an important stage in the development of such methods, but establishing a consensus on standards is hampered by lack of understanding of available techniques and software. This report builds on the previous ISPOR Conjoint Analysis Task Force Report: Conjoint Analysis Applications in Health-A Checklist: A Report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. This report aims to assist researchers specifically in evaluating alternative approaches to experimental design, a difficult and important element of successful DCEs. While this report does not endorse any specific approach, it does provide a guide for choosing an approach that is appropriate for a particular study. In particular, it provides an overview of the role of experimental designs for the successful implementation of the DCE approach in health care studies, and it provides researchers with an introduction to constructing experimental designs on the basis of study objectives and the statistical model researchers have selected for the study. The report outlines the theoretical requirements for designs that identify choice-model preference parameters and summarizes and compares a number of available approaches for constructing experimental designs. The task-force leadership group met via bimonthly teleconferences and in person at ISPOR meetings in the United States and Europe. An international group of experimental-design experts was consulted during this process to discuss existing approaches for experimental design and to review the task force's draft reports. In addition, ISPOR members contributed to developing a consensus report by submitting written comments during the review process and oral comments during two forum presentations at the ISPOR 16th and 17th Annual International Meetings held in Baltimore (2011) and Washington, DC (2012)., (Copyright © 2013 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.)

A key objective of discrete choice experiments is to obtain sufficient quantity of high quality choice data to estimate choice models to be used to explore various policy/clinically relevant issues. This paper focuses on a relatively new form of choice experiment, 'Best Worst Discrete Choice Experiments' (BWDCEs) and their relevance to health research as a new way to meet such an objective. We explain what BWDCEs are, how and when to apply them and we present several analytical approaches to model the resulting data. We demonstrate this preference elicitation approach in an empirical application exploring preferences of 898 members of the general public in Edmonton and Calgary, Canada for treatment of cardiac arrest occurring in a public place and show the gains achieved compared to traditional analysis of first best data. We suggest that BWDCEs are a valuable way to investigate preferences in the health sector and discuss implications for task design, analysis and areas for future research., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

This paper presents the first attempt to use a discrete choice experiment to derive distributional weights for quality adjusted life years (QALYs), based on characteristics (age and severity) of the beneficiaries. A novel approach using the Hicksian compensating variation is applied. Advantages include derivation of weights for QALYs, not just for life or life years saved, and investigation of the impact of the size of the health gain by allowing the gain to be traded against other characteristics. Results suggest one would generally not weight QALYs, except in a small number of specific cases and in those cases the weights are relatively small. Methodological challenges are highlighted as is a future research agenda., (Copyright © 2011 Elsevier B.V. All rights reserved.)

Compared to many applied areas of economics, health economics has a strong tradition in eliciting and using stated preferences (SP) in policy analysis. Discrete choice experiments (DCEs) are one SP method increasingly used in this area. Literature on DCEs in health and more generally has grown rapidly since the mid-1990s. Applications of DCEs in health have come a long way, but to date few have been 'best practice', in part because 'best practice' has been somewhat of a moving target. The purpose of this paper is to briefly survey the history of DCEs and the state of current knowledge, identify and discuss knowledge gaps, and suggest potentially fruitful areas for future research to fill such gaps with the aim of moving the application of DCEs in health economics closer to best practice.

Discrete choice experiments (DCEs) are regularly used in health economics to elicit preferences for healthcare products and programmes. There is growing recognition that DCEs can provide more than information on preferences and, in particular, they have the potential to contribute more directly to outcome measurement for use in economic evaluation. Almost uniquely, DCEs could potentially contribute to outcome measurement for use in both cost-benefit and cost-utility analysis. Within this expanding remit, our intention is to provide a resource for current practitioners as well as those considering undertaking a DCE, using DCE results in a policy/commercial context, or reviewing a DCE. We present the fundamental principles and theory underlying DCEs. To aid in undertaking and assessing the quality of DCEs, we discuss the process of carrying out a choice study and have developed a checklist covering conceptualizing the choice process, selecting attributes and levels, experimental design, questionnaire design, pilot testing, sampling and sample size, data collection, coding of data, econometric analysis, validity, interpretation and welfare and policy analysis. In this fast-moving area, a number of issues remain on the research frontier. We therefore outline potentially fruitful areas for future research associated both with DCEs in general, and with health applications specifically, paying attention to how the results of DCEs can be used in economic evaluation. We also discuss emerging research trends. We conclude that if appropriately designed, implemented, analysed and interpreted, DCEs offer several advantages in the health sector, the most important of which is that they provide rich data sources for economic evaluation and decision making, allowing investigation of many types of questions, some of which otherwise would be intractable analytically. Thus, they offer viable alternatives and complements to existing methods of valuation and preference elicitation.

Effective control of asthma requires regular preventive medication. Poor medication adherence suggests that patient preferences for medications may differ from the concerns of the prescribing clinicians. This study investigated patient preferences for preventive medications across symptom control, daily activities, medication side-effects, convenience and costs, using a discrete choice experiment embedded in a randomized clinical trial involving patients with mild-moderate persistent asthma. The present data were collected after patients had received 6 weeks' treatment with one of two drugs. Three choice options were presented, to continue with the current drug, to change to an alternative, hypothetical drug, or to take no preventive medication. Analysis used random parameter multinomial logit. Most respondents chose to continue with their current drug in most choice situations but this tendency differed depending on which medication they had been allocated. Respondents valued their ability to participate in usual daily activities and sport, preferred minimal symptoms, and were less likely to choose drugs with side-effects. Cost was also significant, but other convenience attributes were not. Demographic characteristics did not improve the model fit. This study illustrates how discrete choice experiments may be embedded in a clinical trial to provide insights into patient preferences., (Copyright (c) 2007 John Wiley & Sons, Ltd.)

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.

Investigation of the 'rationality' of responses to discrete choice experiments (DCEs) has been a theme of research in health economics. Responses have been deleted from DCEs where they have been deemed by researchers to (a) be 'irrational', defined by such studies as failing tests for non-satiation, or (b) represent lexicographic preferences. This paper outlines a number of reasons why deleting responses from DCEs may be inappropriate after first reviewing the theory underpinning rationality, highlighting that the importance placed on rationality depends on the approach to consumer theory to which one ascribes. The aim of this paper is not to suggest that all preferences elicited via DCEs are rational. Instead, it is to suggest a number of reasons why it may not be the case that all preferences labelled as 'irrational' are indeed so. Hence, deleting responses may result in the removal of valid preferences; induce sample selection bias; and reduce the statistical efficiency and power of the estimated choice models. Further, evidence suggests random utility theory may be able to cope with such preferences. Finally, we discuss a number of implications for the design, implementation and interpretation of DCEs and recommend caution regarding the deletion of preferences from stated preference experiments., (Copyright 2006 John Wiley & Sons, Ltd.)

Bryan and Dolan have offered a critique of the use of discrete choice experiments in health economics. Their call for more open debate on "the relative strengths and limitations of the DCE method, particularly when applied in health settings" is warranted. However, their paper has only added to part of this debate in that it focuses on the application of choice experiments in the health sector but says little on the strengths and limitations of the DCE method in general. We argue that while the criticisms posed by Bryan and Dolan rightly challenge the manner in which DCEs have been applied in health economics, such criticism does not challenge the theoretical/methodological basis of DCEs per se.

Discrete choice experiments (DCEs) are being used increasingly in health economics to elicit preferences for products and programs. The results of such experiments have been used to calculate measures of welfare or more specifically, respondents' 'willingness to pay' (WTP) for products and programs and their 'marginal willingness to pay' (MWTP) for the attributes that make up such products and programs. In this note we show that the methods currently used to derive measures of welfare from DCEs in the health economics literature are not consistent with random utility theory (RUT), or with microeconomic welfare theory more generally. The inconsistency with welfare theory is an important limitation on the use of such WTP estimates in cost-benefit analyses. We describe an alternative method of deriving measures of welfare (compensating variation) from DCEs that is consistent with RUT and is derived using welfare theory. We demonstrate its use in an empirical application to derive the WTP for asthma medication and compare it to the results elicited from the method currently used in the health economics literature.