One Method, Many Methodological Choices: A Structured Review of Discrete-Choice Experiments for Health State Valuation

Creating an SF-6Dv2 social value set for New Zealand

The SF-6D health descriptive system and its second version published in 2020, the SF-6Dv2, is used worldwide for valuing health-related quality of life (HRQoL) for economic evaluation and measuring patient-reported health outcomes. In this study, a valuation tool was developed and applied to create a social value set, comprising 18,750 health state values, for the SF-6Dv2 for New Zealand (NZ). This tool was adapted and extended from the one used to create a social value set for the EQ-5D-5L, a simpler health descriptive system with fewer dimensions and health states. The tool implements the PAPRIKA method, a type of adaptive discrete choice experiment, and a binary search algorithm to identify health states worse than dead and has extensive data quality controls to ensure the validity and reliability of the social value set derived from participants' personal value sets. The tool, accompanied by a short introductory video designed specifically for the SF-6Dv2, was distributed via an online survey to a large representative sample of adult New Zealanders in June-July 2022. The tool's data quality controls enabled participants who failed to understand or sincerely engage with the valuation tasks to be identified and excluded, resulting in the participants being pared down to a sub-sample of 2985 'high-quality' participants whose personal value sets were averaged for the social value set. These results, including participants' positive feedback, demonstrate the feasibility and acceptability of using the tool to value larger health descriptive systems such as the SF-6Dv2. Having successfully created an SF-6Dv2 social value set for NZ, the valuation tool can be readily applied to other countries, used to generate personal value sets for personalised medicine and adapted to create value sets for other health descriptive systems.

Japanese value set for the EORTC QLU-C10D: A multi-attribute utility instrument based on the EORTC QLQ-C30 cancer-specific quality-of-life questionnaire

PURPOSE

This study aimed to develop a Japanese value set for the EORTC QLU-C10D, a multi-attribute utility measure derived from the cancer-specific health-related quality-of-life (HRQL) questionnaire, the EORTC QLQ-C30. The QLU-C10D contains ten HRQL dimensions: physical, role, social and emotional functioning, pain, fatigue, sleep, appetite, nausea, and bowel problems.

METHODS

Quota sampling of a Japanese online panel was used to achieve representativeness of the Japanese general population by sex and age (≥ 18 years). The valuation method was an online discrete choice experiment. Each participant considered 16 choice pairs, randomly assigned from 960 choice pairs. Each pair included two QLU-C10D health states and life expectancy. Data were analyzed using conditional logistic regression, parameterized to fit the quality-adjusted life-year framework. Preference weights were calculated as the ratio of each dimension-level coefficient to the coefficient for life expectancy.

RESULTS

A total of 2809 eligible panel members consented, 2662/2809 (95%) completed at least one choice pair, and 2435/2662 (91%) completed all choice pairs. Within dimensions, preference weights were generally monotonic. Physical functioning, role functioning, and pain were associated with the largest utility weights. Intermediate utility weights were associated with social functioning and nausea; the remaining symptoms and emotional functioning were associated with smaller utility decrements. The value of the worst health state was - 0.221, lower than that seen in most other existing QLU-C10D country-specific value sets.

CONCLUSIONS

The Japan-specific QLU-C10D value set is suitable for evaluating the cost and utility of oncology treatments for Japanese health technology assessment and decision-making.

Putting the Choice in Choice Tasks: Incorporating Preference Elicitation Tasks in Health Preference Research

Choice-based preference elicitation methods such as the discrete choice experiment (DCE) present hypothetical choices to respondents, with an expectation that these hypothetical choices accurately reflect a 'real world' health-related decision context and that consequently the choice data can be held to be a true representation of the respondent's health or treatment preferences. For this to be the case, careful consideration needs to be given to the format of the choice task in a choice experiment. The overarching aim of this paper is to highlight important aspects to consider when designing and 'setting up' the choice tasks to be presented to respondents in a DCE. This includes the importance of considering the potential impact of format (e.g. choice context, choice set presentation and size) as well as choice set content (e.g. labelled and unlabelled choice sets and inclusion of reference alternatives) and choice questions (stated choice versus additional questions designed to explore complete preference orders) on the preference estimates that are elicited from studies. We endeavoure to instil a holistic approach to choice task design that considers format alongside content, experimental design and analysis.

Development and Valuation of a Preference-Weighted Measure in Age-Related Macular Degeneration From the Vision Impairment in Low Luminance Questionnaire: A MACUSTAR Report

OBJECTIVES

This study generates VILL-UI (Vision Impairment in Low Luminance - Utility Index), a preference-weighted measure (PWM) derived from the VILL-33 measure for use in patients with age-related macular degeneration (AMD) and valued to generate United Kingdom and German preference weights.

METHODS

A PWM consists of a classification system to describe health and utility values for every state described by the classification. The classification was derived using existing data collected as part of the MACUSTAR study, a low-interventional study on AMD, conducted at 20 clinical sites across Europe. Items were selected using psychometric and Rasch analyses, published criteria around PWM suitability, alongside instrument developer views and concept elicitation work that informed VILL-33 development. An online discrete choice experiment (DCE) with duration of the health state was conducted with the United Kingdom and German public. Responses were modeled to generate utility values for all possible health states.

RESULTS

The classification system has 5 items across the 3 domains of VILL-33: reading and accessing information, mobility and safety, and emotional well-being. The DCE samples (United Kingdom: n = 1004, Germany: n = 1008) are broadly representative and demonstrate good understanding of the tasks. The final DCE analyses produce logically consistent and significant coefficients.

CONCLUSIONS

This study enables responses to VILL-33 to be directly used to inform economic evaluation in AMD. The elicitation of preferences from both United Kingdom and Germany enables greater application of VILL-UI for economic evaluation throughout Europe. VILL-UI fills a gap in AMD in which generic preference-weighted measures typically lack sensitivity.

Estimating a Preference-Based Value Set for the Mental Health Quality of Life Questionnaire (MHQoL)

BACKGROUND

Health economic evaluations using common health-related quality of life measures may fall short in adequately measuring and valuing the benefits of mental health care interventions. The Mental Health Quality of Life questionnaire (MHQoL) is a standardized, self-administered mental health-related quality of life instrument covering 7 dimensions known to be relevant across and valued highly by people with mental health problems. The aim of this study was to derive a Dutch value set for the MHQoL to facilitate its use in cost-utility analyses.

METHODS

The value set was estimated using a discrete choice experiment (DCE) with duration that accommodated nonlinear time preferences. The DCE was embedded in a web-based self-complete survey and administered to a representative sample (N = 1,308) of the Dutch adult population. The matched pairwise choice tasks were created using a Bayesian heterogeneous D-efficient design. The overall DCE design comprised 10 different subdesigns, with each subdesign containing 15 matched pairwise choice tasks. Each participant was asked to complete 1 of the subdesigns to which they were randomly assigned.

RESULTS

The obtained coefficients indicated that "physical health,""mood," and "relationships" were the most important dimensions. All coefficients were in the expected direction and reflected the monotonic structure of the MHQoL, except for level 2 of the dimension "future." The predicted values for the MHQoL ranged from -0.741 for the worst state to 1 for the best state.

CONCLUSIONS

This study derived a Dutch value set for the recently introduced MHQoL. This value set allows for the generation of an index value for all MHQoL states on a QALY scale and may hence be used in Dutch cost-utility analyses of mental healthcare interventions.

HIGHLIGHTS

A discrete choice experiment was used to derive a Dutch value set for the MHQoL.This allows the use of the MHQoL in Dutch cost-utility analyses.The dimensions physical health, mood, and relationships were the most important.The utility values range from -0.741 for the worst state to 1 for the best state.

United States Value Set for the Functional Assessment of Cancer Therapy-General Eight Dimensions (FACT-8D), a Cancer-Specific Preference-Based Quality of Life Instrument

OBJECTIVES

To develop a value set reflecting the United States (US) general population's preferences for health states described by the Functional Assessment of Cancer Therapy (FACT) eight-dimensions preference-based multi-attribute utility instrument (FACT-8D), derived from the FACT-General cancer-specific health-related quality-of-life (HRQL) questionnaire.

METHODS

A US online panel was quota-sampled to achieve a general population sample representative by sex, age (≥ 18 years), race and ethnicity. A discrete choice experiment (DCE) was used to value health states. The valuation task involved choosing between pairs of health states (choice-sets) described by varying levels of the FACT-8D HRQL dimensions and survival (life-years). The DCE included 100 choice-sets; each respondent was randomly allocated 16 choice-sets. Data were analysed using conditional logit regression parameterized to fit the quality-adjusted life-year framework, weighted for sociodemographic variables that were non-representative of the US general population. Preference weights were calculated as the ratio of HRQL-level coefficients to the survival coefficient.

RESULTS

2562 panel members opted in, 2462 (96%) completed at least one choice-set and 2357 (92%) completed 16 choice-sets. Pain and nausea were associated with the largest utility weights, work and sleep had more moderate utility weights, and sadness, worry and support had the smallest utility weights. Within dimensions, more severe HRQL levels were generally associated with larger weights. A preference-weighting algorithm to estimate US utilities from responses to the FACT-General questionnaire was generated. The worst health state's value was -0.33.

CONCLUSIONS

This value set provides US population utilities for health states defined by the FACT-8D for use in evaluating oncology treatments.

Developing an Australian utility value set for the Early Childhood Oral Health Impact Scale-4D (ECOHIS-4D) using a discrete choice experiment

PURPOSE

Preference-based quality of life measures (PBMs) are used to generate quality-adjusted life years (QALYs) in economic evaluations. A PBM consists of (1) a health state classification system and (2) a utility value set that allows the instrument responses to be converted to QALYs. A new, oral health-specific classification system, the Early Childhood Oral Health Impact Scale-4D (ECOHIS-4D) has recently been developed. The aim of this study was to generate an Australian utility value set for the ECOHIS-4D.

METHODS

A discrete choice experiment with duration (DCETTO) was used as the preference elicitation technique. An online survey was administered to a representative sample of Australian adults over 18 years. Respondents were given 14 choice tasks (10 tasks from the DCE design of 50 choice sets blocked into five blocks, 2 practice tasks, a repeated and a dominant task). Data were analyzed using the conditional logit model.

RESULTS

A total of 1201 respondents from the Australian general population completed the survey. Of them, 69% (n = 829) perceived their oral health status to be good, very good, or excellent. The estimated coefficients from the conditional logit models were in the expected directions and were statistically significant (p < 0.001). The utility values for health states defined by the ECOHIS-4D ranged from 0.0376 to 1.0000.

CONCLUSIONS

This newly developed utility value set will enable the calculation of utility values for economic evaluations of interventions related to oral diseases such as dental caries among young children. This will facilitate more effective resource allocation for oral health services.

Valuing the Dental Caries Utility Index in Australia

INTRODUCTION

The Dental Caries Utility Index (DCUI) is a new oral health-specific health state classification system for adolescents, consisting of 5 domains: pain/discomfort, difficulty eating food/drinking, worried, ability to participate in activities, and appearance. Each domain has 4 response levels. This study aims to generate an Australian-specific utility algorithm for the DCUI.

METHODS

An online survey was conducted using a representative sample of the adult Australian general population. The discrete choice experiment (DCE) was used to elicit the preferences on 5 domains. Then, the latent utilities were anchored onto the full health-dead scale using the visual analogue scale (VAS). DCE data were modeled using conditional logit, and 2 anchoring procedures were considered: anchor based on the worst health state and a mapping approach. The optimal anchoring procedure was selected based on the model parsimony and the mean absolute error (MAE).

RESULTS

A total of 995 adults from the Australian general population completed the survey. The conditional logit estimates on 5 dimensions and levels were monotonic and statistically significant, except for the second level of the "worried" and "appearance" domains. The mapping approach was selected based on a smaller MAE between the 2 anchoring procedures. The Australian-specific tariff of DCUI ranges from 0.1681 to 1.

CONCLUSION

This study developed a utility algorithm for the DCUI. This value set will facilitate utility value calculations from the participants' responses for DCUI in economic evaluations of dental caries interventions targeted for adolescents.

HIGHLIGHTS

Preference-based quality-of-life measures (PBMs), which consist of a health state classification system and a set of utility values (a scoring algorithm), are used to generate utility weights for economic evaluations.This study is the first to develop an Australian utility value set for the Dental Caries Utility Index (DCUI), a new oral health-specific classification system for adolescents.The availability of a utility value set will enable using DCUI in economic evaluations of oral health interventions targeted for adolescents and may ultimately lead to more effective and efficient planning of oral health care services.

Whose Time Trade-Off Should Be Used? Anchoring Discrete Choice Experiment Latent Utilities in Health State Valuation

OBJECTIVES

To compare anchored discrete choice experiment (DCE) utility values using own versus others' time trade-off (TTO) responses in the valuation of SF-6Dv2.

METHODS

A representative sample of the general population was recruited in China. Through face-to-face interviews, both DCE and TTO data were collected from a randomly selected half of the respondents (own TTO sample), whereas only TTO data were collected from the other half (others' TTO sample). Conditional logit model was used to estimate DCE latent utilities. Three anchoring methods, including using the observed and the modeled TTO values for the worst state, and mapping DCE values onto TTO, were used to scale the latent utilities to health utilities. Prediction accuracy was assessed using intraclass correlation coefficient, mean absolute difference, and root mean squared difference compared with the mean observed TTO values between the anchoring results using the own versus others' TTO data.

RESULTS

Demographic characteristics were comparable between the own TTO sample (n = 252) and the others' TTO sample (n = 251). The mean (SD) observed TTO value for the worst state was -0.259 (0.591) for the own TTO sample and -0.236 (0.616) for the others' TTO sample. Anchoring DCE using own TTOs consistently showed a better prediction accuracy than using others' TTOs across the 3 anchoring methods in terms of the intraclass correlation coefficient (0.835-0.873 vs 0.771-0.804), mean absolute difference (0.127-0.181 vs 0.146-0.203), and root mean squared difference (0.164-0.237 vs 0.192-0.270).

CONCLUSION

When anchoring DCE-derived latent utilities onto the health utility scale, respondents' own TTO data would be preferred to TTO data obtained from a different sample.

Understanding the valuation of paediatric health-related quality of life: a qualitative study protocol

INTRODUCTION

There is evidence from previous studies that adults value paediatric health-related quality of life (HRQoL) and adult HRQoL differently. Less is known about how adolescents value paediatric HRQoL and whether their valuation and decision-making processes differ from those of adults. Discrete choice experiments (DCEs) are widely used to develop value sets for measures of HRQoL, but there is still much to understand about whether and how the methods choices in the implementation of DCE valuation tasks, such as format, presentation and perspective, affect the decision-making process of participants. This paper describes the protocol for a qualitative study that aims to explore the decision-making process of adults and adolescents when completing DCE valuation tasks. The study will also explore the impact of methodological choices in the design of DCE studies (including decisions about format and presentation) on participants' thinking process.

METHODS AND ANALYSIS

An interview protocol has been developed using DCE valuation tasks. Interviews will be conducted online via Zoom with both an adolescent and adult sample. In the interview, the participant will be asked to go through some DCE valuation tasks while 'thinking aloud'. After completion of the survey, participants will then be asked some predetermined questions in relation to various aspects of the DCE tasks. Interviews will be recorded and transcribed and analysed using a thematic analysis approach.

ETHICS AND DISSEMINATION

Ethics approval for this study has been received for the adult sample (UTS ETH20-9632) as well as the youth sample (UTS ETH22-6970) from the University of Technology Sydney Human Research Ethics Committee. Results from this study will inform the methods to be used in development of value sets for use in the health technology assessment of paediatric interventions and treatments. Findings from this study will also be disseminated through national/international conferences and peer-reviewed journals.

Cancer Survivor Preferences for Models of Breast Cancer Follow-Up Care: Selecting Attributes for Inclusion in a Discrete Choice Experiment

BACKGROUND AND OBJECTIVE

It is critical to evaluate cancer survivors' preferences when developing follow-up care models to better address the needs of cancer survivors. This study was conducted to understand the key attributes of breast cancer follow-up care for use in a future discrete choice experiment (DCE) survey.

METHODS

Key attributes of breast cancer follow-up care models were generated using a multi-stage, mixed-methods approach. Focus group discussions were conducted with cancer survivors and clinicians to generate a range of attributes of current and ideal follow-up care. These attributes were then prioritised using an online survey with survivors and healthcare providers. The DCE attributes and levels were finalised via an expert panel discussion based on the outcomes of the previous stages.

RESULTS

Four focus groups were held, two with breast cancer survivors (n = 7) and two with clinicians (n = 8). Focus groups generated sixteen attributes deemed important for breast cancer follow-up care models. The prioritisation exercise was conducted with 20 participants (14 breast cancer survivors and 6 clinicians). Finally, the expert panel selected five attributes for a future DCE survey tool to elicit cancer survivors' preferences on breast cancer follow-up care. The final attributes included: the care team, allied health and supportive care, survivorship care planning, travel for appointments, and out-of-pocket costs.

CONCLUSIONS

Attributes identified can be used in future DCE studies to elicit cancer survivors' preferences for breast cancer follow-up care. This strengthens the design and implementation of follow-up care programs that best suit the needs and expectations of breast cancer survivors.

Discrete Choice Experiments in Health State Valuation: A Systematic Review of Progress and New Trends

BACKGROUND

Discrete choice experiments (DCEs) are increasingly used in health state valuation studies.

OBJECTIVE

This systematic review updates the progress and new findings of DCE studies in the health state valuation, covering the period since the review of June 2018 to November 2022. The review reports the methods that are currently being used in DCE studies to value health and study design characteristics, and, for the first time, reviews DCE health state valuation studies published in the Chinese language.

METHODS

English language databases PubMed and Cochrane, and Chinese language databases Wanfang and CNKI were searched using the self-developed search terms. Health state valuation or methodology study papers were included if the study used DCE data to generate a value set for a preference-based measure. Key information extracted included DCE study design strategies applied, methods for anchoring the latent coefficient on to a 0-1 QALY scale and data analysis methods.

RESULTS

Sixty-five studies were included; one Chinese language publication and 64 English language publications. The number of health state valuation studies using DCE has rapidly increased in recent years and these have been conducted in more countries than prior to 2018. Wide usage of DCE with duration attributes, D-efficient design and models accounting for heterogeneity has continued in recent years. Although more methodological consensus has been found than in studies conducted prior to 2018, this consensus may be driven by valuation studies for common measures with an international protocol (the 'model' valuation research). Valuing long measures with well-being attributes attracted attention and more realistic design strategies (e.g., inconstant time preference, efficient design and implausible states design) were identified. However, more qualitative and quantitative methodology study is still necessary to evaluate the effect of those new methods.

CONCLUSIONS

The use of DCEs in health state valuation continues to grow dramatically and the methodology progress makes the method more reliable and pragmatic. However, study design is driven by international protocols and method selection is not always justified. There is no gold standard for DCE design, presentation format or anchoring method. More qualitative and quantitative methodology study is recommended to evaluate the effect of new methods before researchers make methodology decisions.

Interaction Effects in Health State Valuation Studies: An Optimal Scaling Approach

OBJECTIVES

This study aimed to introduce a parsimonious modeling approach that enables the estimation of interaction effects in health state valuation studies.

METHODS

Instead of supplementing a main-effects model with interactions between each and every level, a more parsimonious optimal scaling approach is proposed. This approach is based on the mapping of health state levels onto domain-specific continuous scales. The attractiveness of health states is then determined by the importance-weighted optimal scales (ie, main effects) and the interactions between these domain-specific scales (ie, interaction effects). The number of interaction terms only depends on the number of health domains. Therefore, interactions between dimensions can be included with only a few additional parameters. The proposed models with and without interactions are fitted on 3 valuation data sets from 2 different countries, that is, a Dutch latent-scale discrete choice experiment (DCE) data set with 3699 respondents, an Australian time trade-off data set with 400 respondents, and a Dutch DCE with duration data set with 788 respondents.

RESULTS

Important interactions between health domains were found in all 3 applications. The results confirm that the accumulation of health problems within health states has a decreasing marginal effect on health state values. A similar effect is obtained when so-called N3 or N5 terms are included in the model specification, but the inclusion of 2-way interactions provides superior model fits.

CONCLUSIONS

The proposed interaction model is parsimonious, produces estimates that are straightforward to interpret, and accommodates the estimation of interaction effects in health state valuation studies with realistic sample size requirements. Not accounting for interactions is shown to result in biased value sets, particularly in stand-alone DCE with duration studies.

The Use of a Discrete Choice Experiment Including Both Duration and Dead for the Development of an EQ-5D-5L Value Set for Australia

BACKGROUND/AIMS

Discrete choice experiments (DCEs) with either duration included an attribute or with dead included as an option can be used as a stand-alone approach to value health states. This paper reports on a DCE with both of these features to develop an EQ-5D-5L value set for Australia.

METHODS

A DCE was undertaken using a large Australian panel of internet respondents, from which a sample of more than 4000 Australian adults was chosen, stratified to be population representative on age and gender. The DCE contained 500 choice triplets, with two EQ-5D-5L health states with duration, and dead as the third option. Each respondent answered 12 choice sets from the 500, stating both the best and worst options from the three available. The design was constructed to estimate a utility algorithm with main effects plus some key interaction terms. A variety of approaches to parameterising interactions, and to anchoring the value set on the required 0-1 scale, were tested. A preferred Australian adult utility algorithm for use in cost-utility analysis was then generated.

RESULTS

In total, 4477 people completed at least one choice set and were included in the analysis. The results reflected the monotonic structure of the EQ-5D-5L, in that moving from no problems to extreme problems led to worsening utility in each dimension. Inclusion of interaction terms demonstrates that the disutility of the first dimension moving to a poor level (defined as either level 5, or level 4 or 5) had a large impact, but subsequent dimensions moving to a poor level had a relatively smaller disutility.

DISCUSSION

This work develops a value set for the EQ-5D-5L in Australia, and also provides a range of methodological insights which can inform future work using a stand-alone DCE to value health in other countries.

Developing an Australian utility value set for MacNew-7D health states

BACKGROUND

A new preference-based measure (MacNew-7D) has recently been developed to allow condition-specific data to be used to capture the quality of life in health economic evaluations in cardiology; however, a general population value set has not yet been developed. This study developed a population utility value set for the MacNew-7D heart disease-specific instrument.

METHODS

The discrete choice experiments (DCE) technique was chosen as the preference-elicitation method. The DCE asked respondents to compare two options and to state their preferences. The survey was conducted using an online panel of respondents, with quota sampling using age groups, sex and jurisdictions to achieve representativeness of the Australian population. The total design consisted of 200 choice sets, of which each respondent answered eight. Additionally, each respondent answered two quality control choice sets. The best-fitting models were selected on the basis of consistency, parsimony, and goodness of fit.

RESULTS

In total, 1903 respondents were included in the analyses. The MacNew-7D utility value set ranged from -0.4456 to 1.000 for health states defined by the classification system. The best-fitting model retained all levels for five dimensions and collapsed one adjacent level for the other two dimensions. Findings were robust to sensitivity analyses related to the inclusion or exclusion of dominancy and repeat tasks.

CONCLUSION

Findings indicated that the MacNew-7D utility value set is likely suitable for estimating quality-adjusted life years derived from the MacNew heart disease health-related quality-of-life questionnaire. This value set was derived from an Australian population-based sample and may not be generalisable to dissimilar populations.

Do they just know more, or do they also have different preferences? An exploratory analysis of the effects of self-reporting serious health problems on health state valuation

BACKGROUND

Health state valuation is often conducted by people valuing either only their own health state (experience-based valuation) or several stylised states (hypothetical valuation). The approach used can affect the elicited values, but it is not clear whether this is caused by different understandings of the states (an "information mechanism") and/or by different opinions of the states (a "preference mechanism"). Justifying privileging the valuations of those with the relevant health state experience solely because they are better informed is insufficient when their opinions differ. This study proposes a new framework to examine the effect of having health problems on health state valuation by distinguishing "within-dimensional" effects (which can be due to better information or due to differences in opinion) and "cross-dimensional" effects (which must be due to differences in opinion).

METHODS

Secondary data from the UK that valued EQ-5D-5L using Discrete Choice Experiments with duration (DCE_TTO) are remodelled controlling for whether a respondent self-reports serious (viz., severe or extreme) problems in "pain or discomfort" (PD) or "anxiety or depression" (AD). The main analysis uses respondents who have serious PD or serious AD alongside matched respondents who do not, and assumes constant proportional time trade-off.

RESULTS

Self-reporting serious PD or serious AD problems has no within-dimensional effect on health state preferences. However, self-reporting serious AD problems has negative cross-dimensional effects on the utility of having any problem in PD, which suggests that the preference mechanism is present. A similar pattern holds when all available (unmatched) data are used and when constant proportional time trade-off is relaxed.

CONCLUSIONS

Findings consistent with the preference mechanism indicate that those with serious health problems may have different opinions on the value of health states compared to the rest of the population, which has normative implications for the debate on which values to use.

Using water-adjusted person years to quantify the value of being water secure for an individual's quality of life

Domestic water insecurity harms quality of life outcomes beyond health, for example in worry about water availability or anger at disrupted plans. However, these outcomes are excluded from cost-benefit analyses of water supply interventions, which typically measure and value only prevented disease and time savings. In this paper, I propose a means of quantifying the value of being water secure for an individual's quality of life, the water-adjusted person year (WAPY). One WAPY represents a year lived in complete water security. It is inspired by the quality-adjusted life year in health economics, which combines time with a health-related quality of life index. The WAPY combines time using water services with a water-related quality of life (WaterQoL) index, where 0 = completely water insecure and 1 = completely water secure. The index could be derived from an existing four-attribute Water Insecurity Experiences scale, which includes questions such as "how often did you worry that you would not have enough water for all of your needs?". Other questions concern drinking water, disrupted plans, and handwashing. Responses can be combined in a weighted index based on the relative importance of the four attributes to people. If someone has a WaterQoL index of 0.6, over a 10 year period they would have 6 WAPYs. If a water supply intervention raised WaterQoL to 0.8, they would gain 2 WAPYs over 10 years. The monetary value of WAPYs gained (e.g. in US$) could be estimated by willingness to pay and included in a cost-benefit analysis. Some interventions might result in greater WaterQoL gains than others, or longer-lasting services. Incorporating WAPYs in cost-benefit analyses, alongside prevented disease and time savings, could help identify interventions which provide better water services to more people within a given budget.

Analytical Considerations When Anchoring Discrete Choice Experiment Values Using Composite Time Trade-Off Data: The Case of EQ-5D-Y-3L

Discrete choice experiments (DCEs) are becoming increasingly used to elicit preferences for children's health states. However, DCE data need to be anchored to produce value sets, and composite time trade-off (cTTO) data are typically used in the context of EQ-5D-Y-3L valuation. The objective of this paper is to compare different anchoring methods, summarise the characteristics of the value sets they produce, and outline key considerations for analysts. Three anchoring methods were compared using data from published studies: (1) rescaling using the mean value for the worst health state; (2) linear mapping; and (3) hybrid modelling. The worst state rescaling value set had the largest range. The worst state rescaling and linear mapping value sets preserved the relative importance of the dimensions from the DCE, whereas the hybrid model value set did not. Overall, the predicted values from the hybrid model value set were more closely aligned with the cTTO values. These findings are relatively generalisable. Deciding upon which anchoring approach to use is challenging, as there are numerous considerations. Where cTTO data are collected for more than one health state, anchoring on the worst health state will arguably be suboptimal. However, the final choice of approach may require value judgements to be made. Researchers should seek input from relevant stakeholders when commencing valuation studies to help guide decisions and should clearly set out their rationale for their preferred anchoring approach in study outputs.

Valuing the Quality-of-Life Aged Care Consumers (QOL-ACC) Instrument for Quality Assessment and Economic Evaluation

OBJECTIVE

This paper reports on the valuation of the classification system for the Quality-of-Life Aged Care Consumers (QOL-ACC) instrument using a discrete choice experiment (DCE) with duration with a large sample of older people receiving aged care services.

METHODS

A DCE with 160 choice sets of two quality-of-life state-survival duration combinations blocked into 20 survey versions, with eight choice sets in each version, was designed and administered through an on-line survey to older Australians receiving aged care services in home and via interviewer facilitation with older people in residential aged care settings. Model specifications investigating preferences with respect to survival duration and interactions between QOL-ACC dimension levels were estimated. Utility weights were developed, with estimated coefficients transformed to the 0 (being dead) to 1 (full health) scale to generate a value set suitable for application in quality assessment and for the calculation of quality-adjusted life-years for use in economic evaluation.

RESULTS

In total, 953 older people completed the choice experiment with valid responses. The estimation results from econometric model specifications indicated that utility increased with survival duration and decreased according to quality-of-life impairment levels. An Australian value set (range - 0.56 to 1.00) was generated for the calculation of utilities for all QOL-ACC states.

CONCLUSION

The QOL-ACC is unique in its focus on measuring and valuing quality of life from the perspective of older people themselves, thereby ensuring that the preferences of aged care service users are the primary focus for quality assessment and economic evaluation.

A Systematic Review of the Methodologies and Modelling Approaches Used to Generate International EQ-5D-5L Value Sets

BACKGROUND

The international protocol for valuing EQ-5D-5L focuses upon prescribed preference elicitation methods and design. However, there are no recommendations around sampling, recruitment, data analysis or modelling to generate the EQ-5D-5L value set. This review examines methods used to generate international EQ-5D-5L values sets, across sampling, recruitment, data analysis, modelling, assessing model performance and selection of the recommended value set.

METHODS

All published EQ-5D-5L value sets were identified by a systematic search and confirmed by the EuroQol Group. Data were extracted to assess sampling, recruitment, preference elicitation techniques and design, data analysis, modelling, assessing model performance, and vale set selection. These are summarised in tables.

RESULTS

The review included 29 studies with 27 value sets generated using time-trade-off (TTO) data (n = 10) only or using a hybrid model that combines TTO and discrete choice experiment data (n = 17). TTO data were most commonly estimated using a heteroscedastic Tobit model with censoring at - 1, and the hybrid model using a specially created program. Model performance was generally assessed using goodness of fit, logical consistency and significance of coefficients, suitability of the model for the data characteristics and parsimony, though not all selected models account for the specific characteristics of the data.

DISCUSSION

Different assessments of model performance and reasoning are provided for the selection of the recommended value set for a country. This raises the question of valid criteria for selecting a recommended value set and whether this should rely upon researchers' recommendations when value sets are often used to inform public policy.

A Discrete Choice Experiment to Elicit General Population Preferences Around the Factors Influencing the Choice to Make Clinical Negligence Claims

OBJECTIVES

This article determines public stated preferences around different factors that influence the choice to make clinical negligence claims against a national healthcare system.

METHODS

A large online survey was conducted using a discrete choice experiment (DCE) with the UK general population (N = 1013). DCE tasks involved a single profile and participants chose whether to make a claim for compensation (yes/no) after one of 3 randomly allocated patient safety incident (PSI) "scenarios" of different severities (mild, moderate, severe). DCE attributes described the actions of the healthcare system after a PSI and characteristics of the clinical negligence claims process. The data were modeled separately for each scenario (mild, moderate, severe) using logistic regression. Marginal effects and the probability of making a claim in a baseline case were estimated.

RESULTS

Probability of choosing to claim was reduced by receipt of an apology, investigation and prevention of recurrence of the PSI, and longer time until claim decision and increased by an easy and straightforward claims process and high chance of compensation and for the mild scenario higher compensation amounts. Marginal effects and baseline case probabilities differed by scenario severity.

CONCLUSIONS

The results suggest the actions of the healthcare system after a PSI and characteristics of the claims process have a larger impact on the probability of making a claim for milder PSIs. For more severe PSIs, a larger probability of making a claim was observed, and the choice was less influenced by the actions of the healthcare system after the PSI and characteristics of the claims process.

The critical role of mixed methods research in developing valid and reliable patient-reported outcome measures

Randomised controlled clinical trials provide the gold standard for evidence underpinning clinical guidelines and patient centred care. However, this is only true when they are robustly designed, conducted and reported and then only if they include outcomes that are important to patients and clinicians. Important outcomes include those that measure impact on patient experience, quality of life, overall well-being, and physical, social, cognitive and emotional functioning, all of which require patient reported outcome measures (PROMs). Patient centred care must be underpinned by objective evidence of the effect of interventions on outcomes that are important to patients. Evidence for patient reported outcomes must be supported by valid and reliable PROMs. Importantly the PROM must reflect patient experience of the impact of the intervention on the outcome and enable quantitative evaluation of that impact. The purpose of this paper is to highlight the critical role of mixed methods research in developing PROMs that are valid (measure what they purport to measure), acceptable to those reporting the outcome and able to reliably detect meaningful differences between individuals with different conditions or severity and with time. This can only be achieved through a structured mixed methods program combining qualitative and quantitative research techniques.

Valuing EQ-5D-Y: the current state of play

BACKGROUND

For nearly a decade, value sets for the EQ-5D-Y were not available, reflecting challenges in valuing child HRQoL. A methodological research programme led to publication of a valuation protocol in 2020, which was rapidly taken up by local study teams. By the end of 2022, between 11 and 17 EQ-5D-Y value sets will be available, more than for any other child HRQoL measure. It is timely to review the experience of those using the protocol to identify early learnings and remaining issues where more research is needed.

METHODS

In June 2021, the EuroQol Group organised a three-day workshop, bringing together all those involved in EQ-5D-Y value set studies and related methodological research concerning EQ-5D-Y and valuation. Workshop discussions were captured by note taking and recording all sessions and online chat. A narrative summary of all sessions was produced and synthesised to identify points of agreement and aspects of methods where uncertainty remains.

RESULTS

There was broad agreement that DCE is working well as the principal valuation method. However, the most appropriate means of anchoring the latent scale values produced by DCE remains unclear. Some studies have deviated from the protocol by extending the number of states included in TTO tasks, to better support modelling of DCE and TTO. There is ongoing discussion about the relative merits of alternative variants of TTO and other methods for anchoring. Very few studies have consulted with local end-users to gauge the acceptability of methods used to value EQ-5D-Y.

CONCLUSIONS

Priority areas for research include testing alternative methods for anchoring DCE data; exploring the preferences of adolescents; and scale differences in values for EQ-5D-Y and adult EQ-5D states, and implications of such differences for the use of EQ-5D-Y values in HTA. Given the normative elements of the protocol, engaging with HTA bodies and other local users should be the first step for all future value set studies. Value sets undertaken to date are for the three-level EQ-5D-Y. However, the issues discussed in this paper are equally relevant to valuation of the five-level version of EQ-5D-Y; indeed, similar challenges are encountered valuing any measure of child HRQoL.

Does Changing the Age of a Child to be Considered in 3-Level Version of EQ-5D-Y Discrete Choice Experiment-Based Valuation Studies Affect Health Preferences?

OBJECTIVES

There has been some debate about the choice of perspective and the age of the child considered when completing preference elicitation tasks in the 3-level version of EQ-5D-Y (EQ-5D-Y-3L) valuation protocol. This study aimed to clarify the impact on latent scale EQ-5D-Y-3L values of varying the age of the child experiencing the health state considered by respondents completing the discrete choice experiment (DCE) tasks of the protocol.

METHODS

We conducted an online DCE with a representative sample of 1000 adults in the United Kingdom and 1000 adults in the United States. Respondents selected the health state they prefer from a series of DCE paired EQ-5D-Y-3L health state comparisons using their own perspective and that of a hypothetical child from the following age groups: "5-7 years old," "8-10 years old," "11-13 years old," and "14-15 years old." Data analysis was conducted using separate multinomial logit models for each perspective and country. We also estimated combined models including data from each possible pair of perspectives and used interactions between EQ-5D-Y-3L levels and perspective to determine whether any differences were statistically significant.

RESULTS

No statistically significant differences in coefficients between perspectives were found in the United States. In the United Kingdom, there were differences between the own perspective and the 5 to 7 years old perspective (looking after myself level 3) and between the 5 to 7 years old perspective and the 8 to 10 years old perspective (usual activities level 3).

CONCLUSIONS

Our results suggest that there is minimal impact on latent scale values when using different ages of the hypothetical child in the current EQ-5D-Y-3L valuation protocol.

Discrete choice experiment with duration versus time trade-off: a comparison of test-retest reliability of health utility elicitation approaches in SF-6Dv2 valuation

OBJECTIVES

To evaluate and compare the test-retest reliability of discrete choice experiments with duration (DCE_TTO) and time trade-off (TTO) in the Chinese SF-6Dv2 valuation study.

METHODS

During face-to-face interviews, a representative sample of the Chinese general population completed 8 TTO tasks and 10 DCE_TTO tasks. Retest interviews were conducted after two weeks. For both DCE_TTO and TTO, the consistency of raw responses between the two tests was firstly evaluated at the individual level. Regressions were conducted to investigate the association between the test-retest reliability and the respondents' characteristics and the severity of health states. Consistency was then analyzed at the aggregate level by comparing the rank order of the coefficients of dimensions.

RESULTS

In total, 162 respondents (51.9% male; range 18-80 years) completed the two tests. The intraclass correlations coefficient 0.958 for TTO, with identical values accounting for 59.3% of observations. 76.4% of choices were identical for DCE_TTO, with a Kappa statistic of 0.528. Respondents' characteristics had no significant impact while the severity of health states valued in TTO and DCE_TTO tasks had a significant impact on the test-retest reliability. Both approaches produced relatively stable rank order of dimensions in constrained model estimations between test and retest data.

CONCLUSIONS

Individual responses of both approaches are relatively stable over time. The rank orders of dimensions in model estimations between test and retest for TTO and DCE_TTO are also consistent. The differences of utility estimation between the two tests for DCE_TTO need to be further investigated based on a larger sample size.

Not all respondents use a multiplicative utility function in choice experiments for health state valuations, which should be reflected in the elicitation format (or statistical analysis)

Discrete choice experiments (DCEs) that include health states and duration are becoming a common method for estimating quality-adjusted life year (QALY) tariffs. These DCEs need to be analyzed under the assumption that respondents treat health and duration multiplicatively. However, in the most commonly used DCE duration format there is no guarantee that respondents actually do so; in fact, respondents can easily simplify the choice tasks by considering health and duration separately. This would result in valid DCE responses but preclude subsequent QALY tariff calculations. Using a Bayesian latent class model and data from two existing valuation studies, our analyses confirm that in both datasets the majority of respondents do not appear to have used a multiplicative utility function. Moreover, a statistical correction for respondents who used an incorrect function changes the range of the QALY weights. Hence our results imply that one can neither assume that respondents use the theoretically required multiplicative utility function nor assume that the type of utility function that respondents use does not affect the estimated QALY weights. As a solution, we advise researchers to use an alternative, more constrained DCE elicitation format that avoids these behavioral problems.

Methodological similarities and variations among EQ-5D-5L value set studies: a systematic review

OBJECTIVES

(1) To descriptively compare the selected elements of valuation methods for EQ-5D-5L value set studies, (2) to compare the characteristics of the value sets, and (3) to examine the associations between the selected elements of valuation methods and the EQ-5D-5L value sets.

METHODS

A systematic literature search of EQ-5D-5L valuation studies from 1 January 2009 to 22 April 2021 was conducted in selected databases. Following the initial search, we also explored additional studies published during the completion of the final version of the manuscript. Similarities and variations for selected elements of valuation methods were descriptively compared. The relative importance of dimensions, utility decrements between the levels, and distribution of the utility scores were used to compare value sets. A meta-regression analysis examined the associations between the selected methodological elements and the utility scores and dimension levels of EQ-5D-5L.

RESULTS

A total of 31 studies were included in this review. Methodological similarities centered around data collection and preference elicitation method. On the other hand, variations include sampling technique, sample size, and value set modeling. The variations in value sets based on the relative importance of dimension, decrement in utility score, and distribution of utility score across countries were observed. Although the distribution of the utility scores differed across countries, higher levels of each dimension tended to have a larger decrement in the utility scores. Mean utility scores for the experience-based value sets were higher than those estimated using stated choice methods. The selected methodological elements were not significantly associated with the mean predicted utility scores or most dimension-level coefficients.

CONCLUSIONS

EQ-5D-5L health state valuation methods and characteristics of value sets differed across studies. The impact of the variation of methodological elements on the value sets should be further investigated.

Behavioral Theories That Have Influenced the Way Health State Preferences Are Elicited and Interpreted: A Bibliometric Mapping Analysis of the Time Trade-Off Method With VOSviewer Visualization

Aim

The aim of this paper is to develop an understanding of how behavioral theories have influenced the way preferences for health-related quality of life are elicited and interpreted. We focus on the Time Trade-off (TTO) method given it represents the quality-adjusted life-year (QALY) concept-that survival in less-than-full health can be deemed equivalent to a shorter survival in full health. To our knowledge this is the first review using a combination of systematic scoping review, bibliometrics and VOSviewer visualization to map the development of ideas in health economics.

Methods

A priori, we selected three behavioral theories to explore within our review, referred to here as Expected Utility Theory, Non-Expected Utility Theory and Probabilistic Choice Theory. A fourth topic, Order Effects, is defined broadly to encompass behavioral theories around timing/sequence of events. For the main search, Scopus was used to identify literature that had (a) elicited TTO values and/or (b) contributed to the way TTO values were elicited and interpreted, from inception to July 2021. Papers that focused on the latter category were given the label "behavioral" and underwent additional analyses. A two stage-screening was applied to assess eligibility. Co-citation, co-authorship and co-occurrence of keywords was used to chart the development of TTO over time.

Results

A total of 1,727 records were retrieved from Scopus and were supplemented by an additional 188 papers. There were 856 applied and 280 behavioral papers included in the final corpus, with the behavioral set split equally into four sets of 70 papers to chart the development of keywords over time: (1) 1972-1999; (2) 2000-2010, (3) 2010-2015 and (4) 2015-2021.

Discussion

The keyword analysis suggested that whilst some ideas transition quickly from economic theory to the TTO literature, such as the impact of Order Effects, others take longer to be assimilated, for example Non-Expected Utility models or failure of constant discounting. It is therefore important that researchers within health economics work more closely with those in mainstream economics and keep abreast of the wider economics and behavioral sciences to expedite the uptake of new and relevant ideas.

Valuing SF-6Dv2 in Australia Using an International Protocol

BACKGROUND

The SF-6Dv2 is an updated version of the SF-6D, with improved consistency and dimension descriptors, and value sets are required. The aim of this study was to estimate an SF-6Dv2 value set for Australia using an international protocol, while secondary aims were to compare Australian and UK values and to understand heterogeneity.

METHODS

The study design was based on the SF-6Dv2 valuation protocol, which involved the administration of two different discrete choice experiment (DCE) tasks. The first task presented pairs of SF-6Dv2 health states with duration (Design 1), and the second task presented triplets that appended 'immediate death' to the health state pairs (Design 2). Respondents completed 12 choice sets online (10 from Design 1; 2 from Design 2). Value sets were estimated for Design 1 separately and combining Designs 1 and 2 using a variety of logit model specifications. Value set characteristics were compared descriptively with the UK model.

RESULTS

The sample included 3001 Australians. A range of ordered and consistent models were estimated. The models only including Design 1 data resulted in a relatively wider utility range. The value range was reduced by an interaction added when the health state included a dimension with a severe level. The model matched with the UK value set resulted in a larger utility range and consistent ordering of the most important dimensions.

CONCLUSION

The model including the Design 1 data and moderating interaction was chosen for the Australian value set. This allows the SF-6Dv2 to be used in health technology assessment decision making in Australia.

Deriving a Preference-Based Measure for People With Duchenne Muscular Dystrophy From the DMD-QoL

OBJECTIVES

This study generates a preference-based measure for capturing the quality of life of people with Duchenne muscular dystrophy (DMD) from a new measure of quality of life, DMD-QoL.

METHODS

A health state classification system was derived from the DMD-QoL based on psychometric performance of items, factor analysis, and item response theory analysis. Preferences for health states described by the classification system were elicited using an online discrete choice experiment survey with life years as an additional attribute, from members of the UK general population (n = 1043). Discrete choice experiment data was modeled using a conditional fixed-effects logit model and utility estimates were directly anchored on the 1 to 0 full health-dead scale.

RESULTS

The health state classification system has 8 dimensions: mobility, difficulty using hands, difficulty breathing, pain, tiredness, worry, participation, and feeling good about yourself. The standard model had mostly statistically significant coefficients and reflected the instrument's monotonic structure. However, 2 dimensions had inconsistent coefficients (where utility increased as health worsened) and a consistent model was estimated that merged adjacent inconsistent severity levels. The best state defined by the classification system has a value of 1 and the worst state has a value of -0.559.

CONCLUSION

The modeled results enable DMD-QoL-8D utility values to be generated using DMD-QoL or DMD-QoL-8D data to generate QALYs for people with DMD. QALYs can then be used to inform economic models of the cost-effectiveness of interventions in DMD. Future research comparing the psychometric performance of DMD-QoL-8D to existing generic preference-based measures, including EQ-5D-5L, is recommended.

Estimation of an EORTC QLU-C10 Value Set for Spain Using a Discrete Choice Experiment

BACKGROUND

The EORTC QLU-C10D is a preference-based measure derived from the EORTC QLQ-C30. For use in economic evaluations, country-specific value sets are needed. This study aimed to generate an EORTC QLU-C10 value set for Spain.

METHODS

A sample of the Spanish general population completed an online discrete choice experiment. An attribute-balanced incomplete block design was used to select 960 choice tasks, with a total of 1920 health states. Each participant was randomly assigned 16 choice sets without replacement. Data were modelled using generalized estimating equations and mixed logistic regressions.

RESULTS

A total of 1625 panel members were invited to participate, 1010 of whom were included in the study. Dimension decrements were generally monotonic with larger disutilities at increased severity levels. Dimensions associated with larger decrements were physical functioning and pain, while the dimension with the smallest decrement was sleep disturbances. The PITS state (i.e. worst attainable health) for the Spanish population is - 0.043.

CONCLUSIONS

This study generated the first Spanish value set for the QLU-C10D. This can facilitate cost-utility analyses when applied to data collected with the EORTC QLQ-C30.

An Exploration on Attribute Non-attendance Using Discrete Choice Experiment Data from the Irish EQ-5D-5L National Valuation Study

BACKGROUND

Generic measures of health-related quality of life (HRQoL) permit comparisons of competing demands for healthcare resources using outcomes that reflect the preferences of tax payers. EQ-5D instruments are the most commonly used generic, preference-based measures of HRQoL. The EQ-5D-5L enables respondents to describe their health state using five dimensions of health, each with five response levels. The standardised protocol for the valuation of EQ-5D-5L health states comprises use of the composite time trade-off valuation technique, supplemented by a discrete choice experiment (DCE).

OBJECTIVE

This paper presents the first exploration on attribute non-attendance (ANA) to the dimensions of the EQ-5D-5L using DCE data collected following the standardised protocol.

METHOD

This paper uses the equality constrained latent class model and the endogenous attribute attendance model to examine ANA to the dimensions of the EQ-5D-5L.

RESULTS

The results suggest that respondents are less likely to consider the physical dimensions of the EQ-5D-5L (such as self-care and usual activities) when evaluating the health states. The effects of ANA on utility scores depends on the interpretation of the underlying reasons for ANA.

CONCLUSIONS

We recommend that future value sets based in whole or in part on DCE data examine the impact of and reasons for non-attendance in national valuation studies.

Valuation of SF-6Dv2 Health States in China Using Time Trade-off and Discrete-Choice Experiment with a Duration Dimension

OBJECTIVES

Our objective was to generate a value set for the SF-6Dv2 using time trade-off (TTO) and a discrete-choice experiment with a duration dimension (DCE_TTO) in China.

METHODS

A large representative sample of the Chinese general population was recruited from eight provinces/municipalities in China, stratified by age, sex, education level, and proportion of urban/rural residence. Respondents completed eight TTO tasks and ten DCE_TTO tasks during face-to-face interviews. Ordinary least squares (OLS), random-effects, fixed-effects, and Tobit models were used for TTO data, and conditional logit and mixed logit models were used for DCE_TTO. The monotonicity of model coefficients and the consistency of the predicted values according to intraclass correlation coefficient (ICC), mean absolute difference (MAD), and mean squared difference (MSD) were compared between the two approaches.

RESULTS

In total, 3320 respondents (50.3% male; range 18-90 years) were recruited. The random-effects model and the conditional logit model were preferred for the TTO and DCE_TTO, respectively. The TTO values ranged from - 0.277 to 1, with 927 (4.94%) states considered as worse than dead (WTD). The corresponding range for DCE_TTO was - 0.535 to 1, with a higher WTD of 8.50%. DCE_TTO presented minor non-monotonicity with the coefficients in two dimensions. Values from the two approaches were highly consistent (ICC 0.9804, MAD 0.0588, MSD 0.0055), albeit those with DCE_TTO were slightly lower than those with TTO. The value set generated by TTO was preferred given the better monotonicity and the statistical significance of coefficients.

CONCLUSIONS

The Chinese value set for the SF-6Dv2 was established based on the TTO approach, but the DCE_TTO also performed well. Minor issues of non-monotonicity did present for DCE_TTO.

United States Utility Algorithm for the EORTC QLU-C10D, a Multiattribute Utility Instrument Based on a Cancer-Specific Quality-of-Life Instrument

BACKGROUND

The EORTC QLU-C10D is a multiattribute utility measure derived from the cancer-specific quality-of-life questionnaire, the EORTC QLQ-C30. The QLU-C10D contains 10 dimensions (physical, role, social and emotional functioning, pain, fatigue, sleep, appetite, nausea, bowel problems). The objective of this study was to develop a United States value set for the QLU-C10D.

METHODS

A US online panel was quota recruited to achieve a representative sample for sex, age (≥18 y), race, and ethnicity. Respondents undertook a discrete choice experiment, each completing 16 choice-pairs, randomly assigned from a total of 960 choice-pairs. Each pair included 2 QLU-C10D health states and duration. Data were analyzed using conditional logistic regression, parameterized to fit the quality-adjusted life-year framework. Utility weights were calculated as the ratio of each dimension-level coefficient to the coefficient for life expectancy.

RESULTS

A total of 2480 panel members opted in, 2333 (94%) completed at least 1 choice-pair, and 2273 (92%) completed all choice-pairs. Within dimensions, weights were generally monotonic. Physical functioning, role functioning, and pain were associated with the largest utility weights. Cancer-specific dimensions, such as nausea and bowel problems, were associated with moderate utility decrements, as were general issues such as problems with emotional functioning and social functioning. Sleep problems and fatigue were associated with smaller utility decrements. The value of the worst health state was 0.032, which was slightly greater than 0 (equivalent to being dead).

CONCLUSIONS

This study provides the US-specific value set for the QLU-C10D. These estimated health state scores, based on responses to the EORTC QLQ-C30 questionnaire, can be used to evaluate the cost-utility of oncology treatments.

Valuing EQ-5D-Y-3L Health States Using a Discrete Choice Experiment: Do Adult and Adolescent Preferences Differ?

Background

An important question in the valuation of children’s health is whether the preferences of younger individuals should be captured within value sets for measures that are aimed at them. This depends on whether younger individuals can complete valuation exercises and whether their preferences differ from those of adults. This study compared the preferences of adults and adolescents for EQ-5D-Y-3L health states using latent scale values elicited from a discrete choice experiment (DCE).

Methods

An online DCE survey, comprising 15 pairwise choices, was provided to samples of UK adults and adolescents (aged 11–17 y). Adults considered the health of a 10-year-old child, whereas adolescents considered their own health. Mixed logit models were estimated, and comparisons were made using relative attribute importance (RAI) scores and a pooled model.

Results

In total, 1000 adults and 1005 adolescents completed the survey. For both samples, level 3 in pain/discomfort was most important, and level 2 in self-care the least important, based on the relative magnitudes of coefficients. The RAI scores (normalized on self-care) indicated that adolescents gave less weight relative to adults to usual activities (1.18 v. 1.51; P < 0.05), pain/discomfort (1.77 v. 3.12; P < 0.01), and anxiety/depression (1.64 vs. 2.65; P < 0.01). The pooled model indicated evidence of differences between the two samples in both levels in pain/discomfort and anxiety/depression.

Limitations

The perspective of the DCE task differed between the 2 samples, and no data were collected to anchor the DCE data to generate value sets.

Conclusions

Adolescents could complete the DCE, and their preferences differed from those of adults taking a child perspective. It is important to consider whether their preferences should be incorporated into value sets.

Public Preferences for Allocating Ventilators in an Intensive Care Unit: A Discrete Choice Experiment

Background and Objective

During the COVID-19 pandemic, resources in intensive care units (ICUs) have the potential to be inadequate to treat all those who might benefit. Therefore, it is paramount to identify the views of the community regarding how to allocate such resources. This study aims to quantify Australian community preferences for ventilation allocation.

Methods

A discrete choice experiment was designed and administrated to an adult Australian online panel. Each survey respondent answered 12 choice sets from a total design of 120. Each choice set placed the respondent in the role of hypothetical decision maker, prioritising care between two patients. Conditional logit, mixed logit regression and latent class analysis were used to analyse the data. Additionally, we asked a series of attitudinal questions about different methods of making such decisions in practice, focusing on who should be responsible.

Results

A total of 1050 community members completed the survey and responded to each choice. Dimensions considered most important were age, likely effectiveness, smoking status, whether the person has dependents, whether they are a healthcare worker, and whether they have a disability or not. Estimating marginal rates of substitution between patient characteristics and chance of survival if ventilated yielded values of up to 30 percentage points if the patient was 70 years old relative to being 30. However, respondents typically said they would prefer such decisions to be made by medical professionals.

Conclusion

This study demonstrated the preferences of the community to allocation of ventilators during the COVID-19 pandemic. The use of such information should be treated with some caution as the underlying reason for such preferences are unclear, and respondents themselves preferred the decision to be made by others.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40271-021-00498-z.

French Value-Set of the QLU-C10D, a Cancer-Specific Utility Measure Derived from the QLQ-C30

BACKGROUND AND OBJECTIVE

The EORTC Quality of Life Utility Measure-Core 10 Dimensions (QLU-C10D) is a new multi-attribute utility instrument derived from the EORTC Quality of Life Questionnaire-Core 30 (QLQ-C30), a widely used cancer-specific quality-of-life questionnaire. It covers ten dimensions: physical, role functioning, social, emotional functioning, pain, fatigue, sleep, appetite, nausea and bowel problems. To allow national health preferences to be reflected, country-specific valuations are being performed through collaboration between the Multi-Attribute Utility Cancer (MAUCa) Consortium and the EORTC. The aim of this study was to determine the utility weights for health states in the French version of the QLU-C10D.

METHODS

Valuations were run in a web-based setting in a general population sample of 1033 adults. Utilities were elicited using a discrete-choice experiment (DCE). Data were analyzed by conditional logistic regression and mixed logits.

RESULTS

The sample was representative of the general French population in terms of gender and age. Dimensions with the largest impact on utility weights were, in this order: physical functioning, pain and emotional functioning. The impact on utilities was lower for role functioning, nausea, bowel problems and social functioning. The dimensions of sleep, fatigue and lacking appetite were associated with the smallest utility decrement.

CONCLUSION

The results of the present study provide utility weights for the QLU-C10D and offer interesting prospects, as some cancer-specific dimensions also received sizeable utility weights (nausea and bowel problems). In fact, the EQ-5D and the HUI 3 are recommended in France and commonly used for cancer-related CUA; however, both these instruments are generic. The availability of a new cancer-specific utility instrument, such as the QLU-C10D, could improve the quality and the pertinence of future CUA in oncology.

Evidence of a disability paradox in patient-reported outcomes in haemophilia

INTRODUCTION

People with inherited and long-term conditions such as haemophilia have been shown to adapt to their levels of disability, often reporting better quality of life (QoL) than expected from the general population (the disability paradox).

AIM

To investigate the disability paradox in people with haemophilia in the United States by examining preference differences in health state valuations versus the general population.

METHODS

We conducted a discrete choice experiment including duration to capture valuations of health states based on patient-reported preferences. Participants indicated their preferences for hypothetical health states using the EQ-5D-5L, where each participant completed 15 of the 120 choice tasks. Response inconsistencies were evaluated with dominated and repeated scenarios. Conditional-logit regressions with random sampling of the general population responses were used to match the sample of patients with haemophilia. We compared model estimates and derived preferences associated with EQ-5D-5L health states.

RESULTS

After removing respondents with response inconsistencies, 1327/2138 (62%) participants remained (177/283 haemophilia; 1150/1900 general population). Patients with haemophilia indicated higher preference value for 99% of EQ-5D-5L health states compared to the general population (when matched on age and gender). The mean health state valuation difference of 0.17 indicated a meaningful difference compared to a minimal clinically important difference threshold of 0.07. Results were consistent by haemophilia type and severity.

CONCLUSION

Our findings indicated the presence of a disability paradox among patients with haemophilia, who reported higher health states than the general population, suggesting the impact of haemophilia may be underestimated if general population value sets are used.

Dutch utility weights for the EORTC cancer-specific utility instrument: the Dutch EORTC QLU-C10D

Purpose

To measure utilities among cancer patients, a cancer-specific utility instrument called the European Organization for Research and Treatment of Cancer (EORTC) QLU-C10D has been developed based on EORTC quality of life core module (QLQ-C30). This study aimed to provide Dutch utility weights for the QLU-C10D.

Methods

A cross-sectional valuation study was performed in 1017 participants representative in age and gender of the Dutch general population. The valuation method was a discrete choice experiment containing 960 choice sets, i.e. pairs of QLU-C10D health states, each health state described in terms of the 10 QLU-C10D domains and the duration of that health state. Each participant considered 16 choice sets, choosing their preferred health state from each pair. Utility scores were derived using generalized estimation equation models. Non-monotonic levels were combined.

Results

Utility decrements were generated for all 10 QLU-C10D domains, with largest decrements for pain (− 0.242), physical functioning (− 0.228), and role functioning (− 0.149). Non-monotonic levels of emotional functioning, pain, fatigue, sleep problems, and appetite loss were combined. No decrement in utility was seen in case of a little or quite a bit impairment in emotional functioning or a little pain. The mean QLU-C10D utility score of the participants was 0.85 (median = 0.91, interquartile range = 0.82 to 0.96).

Conclusion

Dutch utility decrements were generated for the QLU-C10D. These are important for evaluating the cost-utility of new cancer treatments and supportive care interventions. Further insight is warranted into the added value of the QLU-C10D alongside other utility instruments.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11136-021-02767-8.

Valuation study for a preference-based quality of life measure for dental caries (Dental Caries Utility Index - DCUI) among Australian adolescents - study protocol

INTRODUCTION

A new health state classification system has been developed for dental caries - Dental Caries Utility Index (DCUI) to facilitate the assessment of oral health interventions in the cost-utility analysis (CUA). This paper reports the protocol for a valuation study, which aims to generate a preference-based algorithm for the classification system for the DCUI.

METHODS AND ANALYSIS

Discrete choice experiments (DCEs) will be conducted to value health states generated by the DCUI classification system and preferences for these health states will be modelled to develop a utility algorithm. DCEs produce utility values on a latent scale and these values will be anchored into the full health-dead scale to calculate the quality-adjusted life years in CUA. There is no previous evidence for the most suitable anchoring method for dental caries health state valuation. Hence, we will first conduct pilot studies with two anchoring approaches; DCE including duration attribute and DCE anchoring to worst heath state in Visual Analogue Scale. Based on the pilot studies, the most suitable anchoring method among two approaches will be used in the main valuation survey, which will be conducted as an online survey among a representative sample of 2000 adults from the Australian general population. Participants will be asked to complete a set of DCE choice tasks along with anchoring tasks, basic social-demographic questions, DCUI, a generic preference-based measure and oral health quality of life instrument.

ETHICS AND DISSEMINATION

Ethical approval for this study was obtained from the Human Research Ethics Committee, Griffith University (reference number HREC/2019/550). The generated algorithm will facilitate the use of the new dental caries preference-based measure in economic evaluations of oral health interventions. The results will be disseminated through journal articles and professional conferences.

Do Discrete Choice Experiments Approaches Perform Better Than Time Trade-Off in Eliciting Health State Utilities? Evidence From SF-6Dv2 in China

OBJECTIVES

To explore the acceptability, consistency, and accuracy of eliciting health state utility values using discrete choice experiment (DCE) and DCE with life duration dimension (DCE_TTO) as compared with conventional time trade-off (TTO) by using the SF-6Dv2.

METHODS

During face-to-face interviews, a representative sample of the general population in Tianjin, China, completed 8 TTO tasks and 10 DCE/DCE_TTO tasks, with the order of TTO and DCE/DCE_TTO being randomized. The fixed-effect model and conditional logit models were used for TTO and DCEs data estimation, respectively. Acceptability was assessed by self-reported difficulties in understanding/answering. Consistency was observed by the monotonicity of model coefficients. Accuracy was evaluated by investigating differences between observed and predicted TTO values using intraclass correlation coefficient, mean absolute difference, and root mean square difference.

RESULTS

A total of 503 respondents (53.7% male; range, 18-86 years) were included, with comparable characteristics between respondents who completed DCE (N = 252) and DCE_TTO (N = 251). No significant difference was observed in self-reported difficulties among 3 approaches. The monotonicity of coefficients could not be achieved for 2 DCE approaches, even when combining the inconsistent levels. The health state utility values generated by DCE were generally higher than those generated by TTO, whereas DCE_TTO was lower than TTO. The TTO had a better prediction accuracy than the DCEs.

CONCLUSIONS

Two DCE approaches are feasible for eliciting health state utility values; however, they are not considered to be easier to understand/answer than TTO. There are systematic differences in the health state utility values generated by 3 approaches. The issue of non-monotonicity from 2 DCE approaches remains a concern.

Discrete choice experiments to generate utility values for multi-attribute utility instruments: a systematic review of methods

OBJECTIVES

In recent years, discrete choice experiments (DCEs) have become frequently used to generate utility values, but there are a diverse range of approaches to do this. The primary focus of this systematic review is to summarise the methods used for the design and analysis of DCEs when estimating utility values in both generic and condition-specific preference-based measures.

METHODS

Published literature using DCEs to estimate utility values from preference-based instruments were identified from MEDLINE, Embase, Cochrane Library and CINAHL using PRISMA guidelines. To assess the different DCE methods, standardised information was extracted from the articles including the DCE design method, the number of choice sets, the number of DCE pairs per person, randomisation of questions, analysis method, logical consistency tests and techniques for anchoring utilities. The CREATE checklist was used to assess the quality of the studies.

RESULTS

A total of 38 studies with samples from the general population, students and patients were included. Values for health states described using generic multi attribute instruments (MAUIs) (especially the EQ-5D) were the most commonly explored using DCEs. The studies showed considerable methodology and design diversity (number of alternatives, attributes, sample size, choice task presentation and analysis). Despite these differences, the quality of articles reporting the methods used for the DCE was generally high.

CONCLUSION

DCEs are an important approach to measure utility values for both generic and condition-specific instruments. However, a gold standard method cannot yet be recommended.

Understanding the composite dimensions of the EQ-5D: An experimental approach

The EQ-5D(-5L) includes two composite dimensions: "Pain or Discomfort" (P/D) and "Anxiety or Depression" (A/D), which involves an inherent ambiguity. Little is known about how these composite dimensions are interpreted across contexts where (i) individuals self-report their own health; and (ii) individuals value stylised health states. We detail the nature of the ambiguity and present experimental evidence from two large online surveys (n = 1007 and n = 1415). In one survey, individuals reported both their current health and their health at the time they felt the worst because of their health. In the other, they valued stylised EQ-5D states using Discrete Choice Experiments with duration as an attribute. In both surveys, participants were randomised into treatments in which the presentation of one of the composite dimensions was altered, or a control. Our results suggest (1) In self-report, use of the composite dimensions differs across the dimensions, with P/D used mainly to report Pain, but A/D used mainly to mean the more severe component of Anxiety and Depression. (2) In valuation, Pain was perceived to be worse than Discomfort at the same level, and Depression was perceived to be worse than Anxiety at the same level. (3) In valuation, the composite dimension P/D was interpreted to mean Pain, whilst the composite dimension A/D was interpreted to lie between Anxiety and Depression. We conclude that care must be taken when interpreting responses to existing health (or wellbeing) descriptive systems that rely on composite dimensions, and that caution should be applied when designing new ones.

Valuing the AD-5D Dementia Utility Instrument: An Estimation of a General Population Tariff

OBJECTIVE

This paper reports on the valuation of quality-of-life states in the Alzheimer's Disease Five Dimensions (AD-5D) instrument in a representative sample of the general population in Australia using the discrete-choice experiment with duration (DCE_TTO) elicitation technique.

METHOD

A DCE with 200 choice sets of two quality-of-life (QoL) state-duration combinations blocked into 20 survey versions, with ten choice sets in each version, was designed and administered online to a sample representative of the Australian population. Two additional choice sets comprising internal consistency and dominance checks were included in each survey version. A range of model specifications investigating preferences with respect to duration and interactions between AD-5D dimension levels were estimated. Utility weights were developed, with estimated coefficients transformed to the 0 (being dead) to 1 (full health) scale, suitable for the calculation of quality-adjusted life-year (QALY) weights for use in economic evaluation.

RESULTS

In total, 1999 respondents completed the choice experiment. Overall, respondents were slightly better educated and had higher annual incomes than the Australian general population. The estimation results from different specifications and models were broadly consistent with the monotonic nature of the AD-5D: utility increased with increased life expectancy and decreased as the severity level for each dimension worsened. A utility value set was generated for the calculation of utilities for all QoL states defined by the AD-5D descriptive system.

CONCLUSION

The DCE-based utility value set is now available to use to generate QALYs for the economic evaluation of treatments and interventions targeting people with dementia and/or their family caregivers.

EORTC QLU-C10D value sets for Austria, Italy, and Poland

Objective

To develop Austrian, Italian, and Polish general population value sets for the EORTC QLU-C10D, a cancer-specific utility instrument based on the EORTC QLQ-C30, and to descriptively compare their index scores for distinct health states.

Methods

The QLU-C10D descriptive system comprises 10 health attributes and each can take on 4 levels. A standardised and pre-tested methodology has been applied for valuations including a web-based discrete choice experiment (DCE). It was administered in 1000 general population respondents per country recruited via online panels, aiming at representativeness for core socio-demographic variables.

Results

In all three countries, the attributes with the largest impact on utility were physical functioning, pain, and role functioning. Cancer-specific dimensions with the largest impact were nausea and fatigue or bowel problems. Utility values of the worst health state (i.e. severe problems on all 10 dimension) were -0.111 (Austria), 0.025 (Italy), and 0.048 (Poland). Country-specific utilities differed for a selection of health states across the continuum. Austrian utilities were systematically lower for moderately and severely impaired health states.

Conclusion

QLU-C10D cancer-specific utilities can now be calculated in three more countries. Differences between countries indicate that careful consideration is required when using non-country-specific value sets in economic evaluations.

Electronic supplementary material

The online version of this article (10.1007/s11136-020-02536-z) contains supplementary material, which is available to authorized users.

U.K. utility weights for the EORTC QLU-C10D

The EORTC QLU-C10D is a new multi-attribute utility instrument derived from the widely used cancer-specific quality of life questionnaire, EORTC QLQ-C30. It contains 10 dimensions (physical functioning, role functioning, social functioning, emotional functioning, pain, fatigue, sleep, appetite, nausea, bowel problems), each with four levels. The aim of this study was to provide U.K. general population utility weights for the QLU-C10D. A U.K. online panel was quota-sampled to align the sample to the general population proportions of sex and age (≥18 years). The online valuation survey included a discrete choice experiment (DCE). Each participant was asked to complete 16 choice-pairs, each comprising two QLU-C10D health states plus duration. DCE data were analysed using conditional logistic regression to generate utility weights. Data from 2,187 respondents who completed at least one choice set were included in the DCE analysis. The final U.K. QLU-C10D utility weights comprised decrements for each level of each health dimension. For nine of the 10 dimensions (all except appetite), the expected monotonic pattern was observed across levels: Utility decreased as severity increased. For the final model, consistent monotonicity was achieved by merging inconsistent adjacent levels for appetite. The largest utility decrements were associated with physical functioning and pain. The worst possible health state (the worst level of each dimension) is -0.083, which is considered slightly worse than being dead. The U.K.-specific utility weights will enable cost-utility analysis (CUA) for the economic evaluation of new oncology therapies and technologies in the United Kingdom, where CUA is commonly used to inform resource allocation.