Using a discrete choice experiment to value the QLU-C10D: feasibility and sensitivity to presentation format

A scoping review to create a framework for the steps in developing condition-specific preference-based instruments de novo or from an existing non-preference-based instrument: use of item response theory or Rasch analysis

BACKGROUND

There is no widely accepted framework to guide the development of condition-specific preference-based instruments (CSPBIs) that includes both de novo and from existing non-preference-based instruments. The purpose of this study was to address this gap by reviewing the published literature on CSPBIs, with particular attention to the application of item response theory (IRT) and Rasch analysis in their development.

METHODS

A scoping review of the literature covering the concepts of all phases of CSPBI development and evaluation was performed from MEDLINE, Embase, PsychInfo, CINAHL, and the Cochrane Library, from inception to December 30, 2022.

RESULTS

The titles and abstracts of 1,967 unique references were reviewed. After retrieving and reviewing 154 full-text articles, data were extracted from 109 articles, representing 41 CSPBIs covering 21 diseases or conditions. The development of CSPBIs was conceptualized as a 15-step framework, covering four phases: 1) develop initial questionnaire items (when no suitable non-preference-based instrument exists), 2) establish the dimensional structure, 3) reduce items per dimension, 4) value and model health state utilities. Thirty-nine instruments used a type of Rasch model and two instruments used IRT models in phase 3.

CONCLUSION

We present an expanded framework that outlines the development of CSPBIs, both from existing non-preference-based instruments and de novo when no suitable non-preference-based instrument exists, using IRT and Rasch analysis. For items that fit the Rasch model, developers selected one item per dimension and explored item response level reduction. This framework will guide researchers who are developing or assessing CSPBIs.

Community Preferences for the Care of Older People at the End of Life: How Important is the Disease Context?

BACKGROUND

Population preferences for care at the end of life can inform palliative care policy and direction. Research investigating preferences for care at the end of life has focused predominantly on the context of advanced cancer, with relatively little attention to other life-limiting illnesses that are common causes of death.

OBJECTIVES

We aimed to investigate preferences for the care of older people at the end of life in three different disease contexts. The purpose was to understand if population preferences for care in the last 3 weeks of life would differ for patients dying from cancer, heart failure or dementia.

METHODS

Three discrete choice experiments were conducted in Australia with a general population sample using similar methods but different end-of-life disease contexts. Some attributes were common across the three experiments and others differed to accommodate the specific disease context. Each survey was completed by a different panel sample aged ≥45 years (cancer, n = 1548; dementia, n = 1549; heart failure, n = 1003). Analysis was by separate mixed logit models.

RESULTS

The most important attributes across all three surveys were costs to the patient and family, patient symptoms and informal carer stress. The probability of choosing an alternative was lowest (0.18-0.29) when any one of these attributes was at the least favourable level, holding other attributes constant across alternatives. The cancer survey explored symptoms more specifically and found patient anxiety with a higher relative importance score than the symptom attribute of pain. Dementia was the only context where most respondents preferred to not have a medical intervention to prolong life; the probability of choosing an alternative with a feeding tube was 0.40 (95% confidence interval 0.36-0.43).

CONCLUSIONS

This study suggests a need for affordable services that focus on improving patient and carer well-being irrespective of the location of care, and this message is consistent across different disease contexts, including cancer, heart failure and dementia. It also suggests some different considerations in the context of people dying from dementia where medical intervention to prolong life was less desirable.

Calculating ex-ante Utilities From the Neck Disability Index Score: Quantifying the Value of Care For Cervical Spine Pathology

STUDY DESIGN

General population utility valuation study.

OBJECTIVE

To develop a technique for calculating utilities from the Neck Disability Index (NDI) score.

METHODS

We recruited a sample of 1200 adults from a market research panel. Using an online discrete choice experiment (DCE), participants rated 10 choice sets based on NDI health states. A multi-attribute utility function was estimated using a mixed multinomial-logit regression model (MIXL). The sample was partitioned into a training set used for model fitting and validation set used for model evaluation.

RESULTS

The regression model demonstrated good predictive performance on the validation set with an AUC of .77 (95% CI: .76-.78). The regression model was used to develop a utility scoring rubric for the NDI. Regression results also revealed that participants did not regard all NDI items as equally important. The rank order of importance was (in decreasing order): pain intensity = work; personal care = headache; concentration = sleeping; driving; recreation; lifting; and lastly reading.

CONCLUSIONS

This study provides a simple technique for converting the NDI score to utilities and quantify the relative importance of individual NDI items. The ability to evaluate quality-adjusted life-years using these utilities for cervical spine pain and disability could facilitate economic analysis and aid in allocation of healthcare resources.

Danish value sets for the EORTC QLU-C10D utility instrument

PURPOSE

In this study, we developed Danish utility weights for the European Organisation for Research and Treatment of Cancer (EORTC) QLU-C10D, a cancer-specific utility instrument based on the EORTC QLQ-C30.

METHODS

Following a standardized methodology, 1001 adult participants from the Danish general population were quota-sampled and completed a cross-sectional web-based survey and discrete choice experiment (DCE). In the DCE, participants considered 16 choice sets constructed from the key 10 dimensions of the QLU-C10D and chose their preferred health state for each one. Utility weights were calculated using conditional logistic regression with correction for non-monotonicity.

RESULTS

The sample (n = 1001) was representative of the Danish general population with regard to age and gender. The domains with the largest utility decrements, i.e., the domains with the biggest impact on health utility, were physical functioning (- 0.224), pain (- 0.160), and role functioning (- 0.136). The smallest utility decrements were observed for the domains lack of appetite (- 0.024), sleep disorders (- 0.057), and fatigue (- 0.064). Non-monotonicity of severity levels was observed for the domains sleep disturbances, lack of appetite, and bowel problems. Deviations from monotonicity were not statistically significant.

CONCLUSION

The EORTC QLU-C10D is a relatively new multi-attribute utility instrument and is a promising cancer-specific health technology assessment candidate measure. The country-specific Danish utility weights from this study can be used for cost-utility analyses in Danish patients and for comparison with other country-specific utility data.

Stated-Preference Survey Design and Testing in Health Applications

Following the conceptualization of a well-formulated and relevant research question, selection of an appropriate stated-preference method, and related methodological issues, researchers are tasked with developing a survey instrument. A major goal of designing a stated-preference survey for health applications is to elicit high-quality data that reflect thoughtful responses from well-informed respondents. Achieving this goal requires researchers to design engaging surveys that maximize response rates, minimize hypothetical bias, and collect all the necessary information needed to answer the research question. Designing such a survey requires researchers to make numerous interrelated decisions that build upon the decision context, selection of attributes, and experimental design. Such decisions include considering the setting(s) and study population in which the survey will be administered, the format and mode of administration, and types of contextual information to collect. Development of a survey is an interactive process in which feedback from respondents should be collected and documented through qualitative pre-test interviews and pilot testing. This paper describes important issues to consider across all major steps required to design and test a stated-choice survey to elicit patient preferences for health preference research.

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.

The EORTC QLU-C10D: the Hong Kong valuation study

OBJECTIVE

The EORTC QLU-C10D is a new preference-based measure derived from the EORTC QLQ-C30. Country-specific value sets are required to support the cost-utility analysis of cancer-related interventions. This study aimed to generate an EORTC QLU-C10 value set for Hong Kong (HK).

METHODS

A HK online panel was quota-sampled to achieve an adult general population sample representative by sex and age. Participants were invited to complete an online discrete choice experiment survey. Each participant was asked to complete 16 choice-pairs, randomly assigned from a total of 960 choice-pairs, each comprising two QLU-C10D health states and a duration attribute. Conditional and mixed logistic regression analyses were used to analyse the data.

RESULTS

The analysis included data from 1041 respondents who had successfully completed the online survey. The distribution of sex did not differ from that of the general population, but a significant difference was found among age groups. A weighting analysis for non-representative variable (age) was used. Utility decrements were generally monotonic, with the largest decrements for physical functioning (- 0.308), role functioning (- 0.165), and pain (- 0.161). The mean QLU-C10D utility score of the participants was 0.804 (median = 0.838, worst to best = - 0.169 to 1). The value of the worst health state was - 0.223, which was sufficiently lower than 0 (being dead).

CONCLUSIONS

This study established HK utility weights for the QLU-C10D, which can facilitate cost-utility analyses across cancer-related health programmes and technologies.

Valuing Chinese medicine quality of life-11 dimensions (CQ-11D) health states using a discrete choice experiment with survival duration (DCETTO)

OBJECTIVE

To explore generating a health utility value set for the Chinese medicine Quality of life-11 Dimensions (CQ-11D), a utility instrument designed to assess patients' health status while receiving TCM treatment, among the Chinese population.

METHODS

The study was designed to recruit at least 2400 respondents across mainland China to complete one-to-one, face-to-face interviews. Respondents completed ten discrete choice experiment with survival duration (DCETTO) tasks during interviews. The conditional logit models were used to generate the health utility value set for the CQ-11D using the DCETTO data.

RESULTS

A total of 2,586 respondents were invited to participate in the survey and 2498 valid interviews were completed (a completion rate of 96.60%). The modified conditional logit model with combing logically inconsistent levels was ultimately selected to construct the health utility value set for the CQ-11D instrument. The range of the measurable health utility value was -0.868 ~ 1.

CONCLUSION

The study provides the first utility value set for the CQ-11D among the Chinese population. The CQ-11D and corresponding utility value set can be used to measure the health utility values of patients undergoing traditional Chinese medicine interventions, and further facilitate relevant cost-utility analyses. The application of the CQ-11D can support TCM resource allocation in China.

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.

The European Organisation for Research and Treatment of Cancer Quality of Life Utility-Core 10 Dimensions: Development and Investigation of General Population Utility Norms for Canada, France, Germany, Italy, Poland, and the United Kingdom

OBJECTIVES

The European Organisation for Research and Treatment of Cancer Quality of Life Utility-Core 10 Dimensions (EORTC QLU-C10D) is a cancer-specific preference-based measure, providing health utilities for use in economic evaluations derived from the widely used health-related quality of life measure, EORTC QLQ-C30. Several EORTC QLU-C10D country-specific value sets are available. This article aimed to provide EORTC QLU-C10D general population utility norms for Canada, France, Germany, Italy, Poland, and the United Kingdom, to aid interpretability of obtained utilities in these countries.

METHODS

Data were collected in aforementioned countries via a quota-sampled, cross-sectional online survey (n = 100/age-sex group; N = approximately 1000/country). Participants were asked to complete the EORTC QLQ-C30 and provide sociodemographic data. Country-specific utility norms were calculated using the respective country tariff on the country's EORTC QLQ-C30 data after weighting to achieve population representativeness for age and sex. Norm values are provided as means (SDs) by country, age, and sex groups. Tukey's multiple comparison test investigated mean differences among countries. The impact of country, age, and sex on utility values was investigated with a multiple linear regression model.

RESULTS

Country-specific mean utilities range from 0.724 (United Kingdom) to 0.843 (Italy). Country-, sex-, and age-specific mean utilities range from 0.664 for 30- to 39-year-old male Canadians to 0.899 for > 70-year-old male Italians. Utilities were lower in females in 4 of 6 countries, and the impact of age differed among countries. Independent of the impact of age and sex, between-country differences were found (P ≤ .05).

CONCLUSION

Results showed a varying impact of age and sex on EORTC QLU-C10D utilities and significant between-country differences. Using national utility norms and utility decrements is recommended.

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.

Where do measures of health, social care and wellbeing fit within a wider measurement framework? Implications for the measurement of quality of life and the identification of bolt-ons

BACKGROUND

There is variability across studies in the dimensionality i.e., set of latent variables to which health, social care and wellbeing measures relate. This variability may impact the development of new measures and the identification of bolt-on dimensions. We examine the dimensionality of commonly used measures and identify a set of potential bolt-ons for the EQ-5D-5L.

METHODS

We used the OMS dataset, an online survey of health, social care and wellbeing measures in patients and members of the general public. A content analysis provided a theoretical framework for results interpretation. Quantitative analyses were based on a pool of 79 items from 7 measures. Confirmatory factor analysis was used to assess health, social care and wellbeing measures dimensionality and their contribution to quality of life. The relationship between EQ-5D-5L items and the identified factors was used for bolt-ons identification.

RESULTS

The dimensionality comprised of seven factors, namely physical functioning, psychological symptoms, energy/sleep, physical pain, social functioning, needs and satisfaction. Health measures covered five of the seven factors identified, wellbeing measures three and the social care measure one. A list of candidate bolt-on items for the EQ-5D-5L was presented e.g., cognition, energy, dignity.

CONCLUSIONS

This study provides evidence on the dimensionality of health, social care and wellbeing measures and presents a list of candidate bolt-ons for the EQ-5D-5L.

Attributes Underlying Patient Choice for Telerehabilitation Treatment: A Mixed-Methods Systematic Review to Support a Discrete Choice Experiment Study Design

BACKGROUND

Across most healthcare systems, patients are the primary focus. Patient involvements enhance their adherence to treatment, which in return, influences their health. The objective of this study was to determine the characteristics (ie, attributes) and associated levels (ie, values of the characteristics) that are the most important for patients regarding telerehabilitation (TR) healthcare to support a future discrete choice experiment (DCE) study design.

METHODS

A mixed-methods systematic review was conducted from January 2005 to the end of July 2020 and the search strategy was applied to five different databases. The initial selection of articles that met the eligibility criteria was independently made by one researcher, two researchers verified the accuracy of the extracted data, and all researchers discussed about relevant variables to include. Reporting of this systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Mixed Methods Appraisal Tool (MMAT) was used to assess the quality of the study. A qualitative synthesis was used to summarize findings.

RESULTS

From a total of 928 articles, 11 (qualitative [n = 5], quantitative [n = 3] and mixed-methods [n = 3] design) were included, and 25 attributes were identified and grouped into 13 categories: Accessibility, Distance, Interaction, Technology experience, Treatment mode, Treatment location, Physician contact mode, Physician contact frequency, Cost, Confidence, Ease of use, Feeling safer, and Training session. The attributes levels varied from two to five. The DCE studies identified showed the main stages to undertake these types of studies.

CONCLUSION

This study could guide the development of interview grid for individual interviews and focus groups to support a DCE study design in the TR field. By understanding the characteristics that enhance patients' preferences, healthcare providers can create or improve TR programs that provide high-quality and accessible care. Future research via a DCE is needed to determine the relative importance of the attributes.

Estimation of a Canadian preference-based scoring algorithm for the Veterans RAND 12-Item Health Survey: a population survey using a discrete-choice experiment

BACKGROUND

The Veterans RAND 12-Item Health Survey (VR-12) is a generic patient-reported outcome measure derived from the widely used 36- and 12-item Short Form Health Surveys. We aimed to estimate a Canadian preference-based scoring algorithm for the VR-12, enabling the derivation of health utility values for generating quality-adjusted life years (QALYs).

METHODS

We conducted a discrete-choice experiment in a sample of the Canadian population in January and February 2019. Participants - recruited from a consumer research panel - completed an online survey, in English or French, that included 11 discrete-choice questions, each comprising 2 health profiles. We defined the health profiles using 8 VR-12 items and a duration attribute. Using conditional logit regressions, where each level of the respective VR-12 items was interacted with duration, we applied the coefficients to estimate health utility values interpretable on a scale of 0 (dead) to 1 (full health). Negative values reflect states considered worse than dead.

RESULTS

A total of 3380 individuals completed the survey. Of these, 1688 (49.9%) were females, and 3101 (91.7%) completed the English version of the survey. Across all models, "feel downhearted and blue all of the time" and "pain interferes with your normal work extremely" were associated with the largest decrements in health utility. Excluding the 685 respondents (20.3%) who provided inconsistent responses had a negligible effect on the results. The recommended model, weighted to match population demographics, had health utility values ranging from -0.589 to 1.000.

INTERPRETATION

Health utility values that reflect the preferences of the Canadian population can now be derived from responses to the VR-12. These values can be used to generate QALYs in future analyses.

The FACT-8D, a new cancer-specific utility algorithm based on the Functional Assessment of Cancer Therapies-General (FACT-G): a Canadian valuation study

Introduction

Utility instruments are used to assess patients’ health-related quality of life for cost-utility analysis (CUA). However, for cancer patients, the dimensions of generic utility instruments may not capture all the information relevant to the impact of cancer. Cancer-specific utilities provide a useful alternative. Under the auspices of the Multi-Attribute Utility in Cancer Consortium, a cancer-specific utility algorithm was derived from the FACT-G. The new FACT-8D contains eight dimensions: pain, fatigue, nausea, sleep, work, support from family/friends, sadness, and worry health will get worse. The aim of the study was to obtain a Canadian value set for the FACT-8D.

Methods

A discrete choice experiment was administered to a Canadian general population online panel, quota sampled by age, sex, and province/territory of residence. Respondents provided responses to 16 choice sets. Each choice set consisted of two health states described by the FACT-8D dimensions plus an attribute representing survival duration. Sample weights were applied and the responses were analyzed using conditional logistic regression, parameterized to fit the quality-adjusted life year framework. The results were converted into utility weights by evaluating the marginal rate of substitution between each level of each FACT-8D dimension with respect to duration.

Results

2228 individuals were recruited. The analysis dataset included n = 1582 individuals, who completed at least one choice set; of which, n = 1501 completed all choice sets. After constraining to ensure monotonicity in the utility function, the largest decrements were for the highest levels of pain (− 0.38), nausea (− 0.30), and problems doing work (− 0.23). The decrements of the remaining dimensions ranged from − 0.08 to − 0.18 for their highest levels. The utility of the worst possible health state was defined as − 0.65, considerably worse than dead.

Conclusions

The largest impacts on utility included three generic dimensions (i.e., pain, support, and work) and nausea, a symptom caused by cancer (e.g., brain tumours, gastrointestinal tumours, malignant bowel obstruction) and by common treatments (e.g., chemotherapy, radiotherapy, opioid analgesics). This may make the FACT-8D more informative for CUA evaluating in many cancer contexts, an assertion that must now be tested empirically in head-to-head comparisons with generic utility measures.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12955-022-02002-z.

The EORTC QLU-C10D discrete choice experiment for cancer patients: a first step towards patient utility weights

Background

The European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Utility-Core 10 Dimensions (QLU-C10D) is a novel cancer-specific preference-based measure (PBM) for which value sets are being developed for an increasing number of countries. This is done by obtaining health preferences from the respective general population. There is an ongoing discussion if instead patients suffering from the disease in question should be asked for their preferences. We used the QLU-C10D valuation survey, originally designed for use in the general population, in a sample of cancer patients in Austria to assess the methodology’s acceptability and applicability in this target group before obtaining QLU-C10D patient preferences.

Methods

The core of the QLU-C10D valuation survey is a discrete choice experiment in which respondents are asked to give preferences for certain health states (described by a relatively large number of 10 quality of life domains) and an associated survival time. They therewith are asked to trade off quality of life against life time. As this might be a very burdensome task for cancer patients undergoing treatment, a cognitive interview was conducted in a pilot sample to assess burden and potential additional needs for explanation in order to be able to use the DCE for the development of QLU-C10D patient preferences. In addition, responses to general feedback questions on the survey were compared against responses from a matched control group from the already completed Austrian general population valuation survey.

Results

We included 48 patients (mean age 59.9 years; 46% female). In the cognitive interview, the majority indicated that their experience with the survey was positive (85%) and overall clarity as good (90%). In response to the general feedback questions, patients rated the presentation of the health states less clear than matched controls (p = 0.008). There was no difference between patients and the general population concerning the difficulty in choosing between the health states (p = 0.344).

Conclusion

Despite the relatively large number of DCE domains the survey was manageable for patients and allows going on with the QLU-C10D patient valuation study.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41687-022-00430-5.

Calculating Ex-ante Utilities From the Modified Japanese Orthopedic Association Score: A Prerequisite for Quantifying the Value of Care for Cervical Myelopathy

STUDY DESIGN

General population utility valuation study.

OBJECTIVE

The aim of this study was to develop a technique for calculating utilities from the modified Japanese Orthopedic Association (mJOA) Score.

SUMMARY OF BACKGROUND DATA

The ability to calculate quality-adjusted life-years (QALYs) for degenerative cervical myelopathy (DCM) would enhance treatment decision making and facilitate economic analysis. QALYs are calculated using utilities.

METHODS

We recruited a sample of 760 adults from a market research panel. Using an online discrete choice experiment, participants rated eight choice sets based on mJOA health states. A multiattribute utility function was estimated using a mixed multinomial-logit regression model. The sample was partitioned into a training set used for model fitting and validation set used for model evaluation.

RESULTS

The regression model demonstrated good predictive performance on the validation set with an area under the curve of 0.81 (95% confidence interval: 0.80-0.82)). The regression model was used to develop a utility scoring rubric for the mJOA. Regression results revealed that participants did not regard all mJOA domains as equally important. The rank order of importance was (in decreasing order): lower extremity motor function, upper extremity motor function, sphincter dysfunction, upper extremity sensation.

CONCLUSION

This study provides a simple technique for converting the mJOA score to utilities and quantify the importance of mJOA domains. The ability to evaluate QALYs for DCM will facilitate economic analysis and patient counseling. Clinicians should heed these findings and offer treatments that maximize function in the attributes viewed most important by patients.Level of Evidence: 3.

Adolescent valuation of CARIES-QC-U: a child-centred preference-based measure of dental caries

Objectives

This study develops an adolescent value set for a child-centred dental caries-specific measure of oral health-related quality of life (OHRQoL) based upon CARIES-QC (Caries Impacts and Experiences Questionnaire for Children). This study develops a new approach to valuing child health by eliciting adolescent preferences and anchoring these onto the 1–0 full health-dead QALY (quality adjusted life year) scale using ordinal adult preferences.

Methods

Two online surveys were created to elicit preferences for the CARIES-QC classification system. The first comprised best–worst scaling (BWS) tasks for completion by adolescents aged 11–16 years. The second comprised discrete choice experiment tasks with a duration attribute (DCE_TTO) for completion by adults aged over 18 years. Preferences were modelled using the conditional logit model. Mapping regressions anchored the adolescent BWS data onto the QALY scale using adult DCE_TTO values, since the BWS survey data alone cannot generate anchored values.

Results

723 adolescents completed the BWS survey and 626 adults completed the DCE_TTO survey. The samples were representative of UK adolescent and adult populations. Fully consistent and robust models were produced for both BWS and DCE_TTO data. BWS preferences were mapped onto DCE_TTO values, resulting utility estimates for each health state defined by the classification system.

Conclusion

This is the first measure with predetermined scoring based on preferences to be developed specifically for use in child oral health research, and uses a novel technique to generate a value set using adolescent preferences. The estimates can be used to generate QALYs in economic evaluations of interventions to improve children’s oral health.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12955-022-01918-w.

Preferred health outcome states following treatment for pulmonary exacerbations of cystic fibrosis

BACKGROUND

Treatment for pulmonary exacerbations of cystic fibrosis (CF) can produce a range of positive and negative outcomes. Understanding which of these outcomes are achievable and desirable to people affected by disease is critical to agreeing to goals of therapy and determining endpoints for trials. The relative importance of outcomes resulting from treatment of these episodes are not reported. We aimed to (i) quantify the relative importance of outcomes resulting from treatment for pulmonary exacerbations and (ii) develop patient and proxy carer-reported weighted outcome measures for use in adults and children, respectively.

METHODS

A discrete choice experiment (DCE) survey was conducted. Participants were asked to make a series of hypothetical decisions about treatment for pulmonary exacerbations to assess how they make trade-offs between different attributes of health. Data were analysed using a conditional logistic regression model. The correlation coefficients from these data were rescaled to enable generation of a composite health outcome score between 0 and 100 (worst to best health state).

RESULTS

362 individuals participated (167 people with CF and 195 carers); of these, 206 completed the survey (56.9%). Most participants were female and resided in Australia. Difficult/painful breathing had the greatest impact on the preferred health state amongst people with CF and carers alike. Avoidance of gastrointestinal problems also heavily influenced decision-making.

CONCLUSIONS

These data should be considered when making treatment decisions and determining endpoints for trials. Further research is recommended to quantify the preferences of children and to determine whether these align with those of their carer(s).

Clinical Outcome Assessment in Cancer Rehabilitation and the Central Role of Patient-Reported Outcomes

Simple Summary

After completion of acute cancer treatment, it is important to support patients in recovering physically and psychologically and to help them regain their social life. This is the goal of cancer rehabilitation. If we want to know which rehabilitation interventions are helpful, we must measure their effects. This can be done by asking clinicians, testing patients’ performance, observing their behaviors, or by asking patients directly about their experience. This paper focuses on reports from the patients. We give an overview of available questionnaires and offer advice regarding their use. Furthermore, we discuss how to integrate them into clinical practice and research. The most promising way to collect such data are electronic systems, which offer many advantages. The goal of assessing the patient perspective is to help patients, clinicians, and health insurance providers to decide which rehabilitation interventions suit patients’ needs, and therefore, which ones should be chosen and reimbursed.

Abstract

The aim of cancer rehabilitation is to help patients regain functioning and social participation. In order to evaluate and optimize rehabilitation, it is important to measure its outcomes in a structured way. In this article, we review the different types of clinical outcome assessments (COAs), including Clinician-Reported Outcomes (ClinROs), Observer-Reported Outcomes (ObsROs), Performance Outcomes (PerfOs), and Patient-Reported Outcomes (PROs). A special focus is placed on PROs, which are commonly defined as any direct report from the patient about their health condition without any interpretation by a third party. We provide a narrative review of available PRO measures (PROMs) for relevant outcomes, discuss the current state of PRO implementation in cancer rehabilitation, and highlight trends that use PROs to benchmark value-based care. Furthermore, we provide examples of PRO usage, highlight the benefits of electronic PRO (ePRO) collection, and offer advice on how to select, implement, and integrate PROs into the cancer rehabilitation setting to maximize efficiency.

Responsiveness and convergent validity of QLU-C10D and EQ-5D-3L in assessing short-term quality of life following esophagectomy

Aim

This study assessed the responsiveness and convergent validity of two preference-based measures; the newly developed cancer-specific EORTC Quality of Life Utility Measure-Core 10 dimensions (QLU-C10D) relative to the generic three-level version of the EuroQol 5 dimensions (EQ-5D-3L) in evaluating short-term health related quality of life (HRQoL) outcomes after esophagectomy.

Methods

Participants were enrolled in a multicentre randomised controlled trial to determine the impact of preoperative and postoperative immunonutrition versus standard nutrition in patients with esophageal cancer. HRQoL was assessed seven days before and 42 days after esophagectomy. Standardized Response Mean and Effect Size were calculated to assess responsiveness. Ceiling effects for each dimension were calculated as the proportion of the best level responses for that dimension at follow-up/post-operatively. Convergent validity was assessed using Spearman’s correlation and the level of agreement was explored using Bland–Altman plots.

Results

Data from 164 respondents (mean age: 63 years, 81% male) were analysed. HRQoL significantly reduced on both measures with large effect sizes (> 0.80), and a greater mean difference (0.29 compared to 0.16) on QLU-C10D. Both measures had ceiling effects (> 15%) on all dimensions at baseline. Following esophagectomy, ceiling effects were observed with self-care (86%), mobility (67%), anxiety/depression (55%) and pain/discomfort (19%) dimensions on EQ-5D-3L. For QLU-C10D ceiling effects were observed with emotional function (53%), physical function (16%), nausea (35%), sleep (31%), bowel problems (21%) and pain (20%). A strong correlation (r = 0.71) was observed between EQ-5D-3L anxiety and QLU-C10D emotional function dimensions. Good agreement (3.7% observations outside the limits of agreement) was observed between the utility scores.

Conclusion

The QLU-C10D is comparable to the more widely applied generic EQ-5D-3L, however, QLU-C10D was more sensitive to short-term utility changes following esophagectomy. Cognisant of requirements by policy makers to apply generic utility measures in cost effectiveness studies, the disease-specific QLU-C10D should be used alongside the generic measures like EQ-5D-3L.

Trial registration: The trial was registered with the Australian New Zealand Clinical Trial Registry (ACTRN12611000178943) on the 15th of February 2011.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12955-021-01867-w.

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.

Calculating Utilities From the Spine Oncology Study Group Outcomes Questionnaire: A Necessity for Economic and Decision Analysis

STUDY DESIGN

General population utility valuation study.

OBJECTIVE

The aim of this study was to develop a technique for calculating utilities from the Spine Oncology Study Group Outcomes Questionnaire v2.0 (SOSGOQ2.0).

SUMMARY OF BACKGROUND DATA

The ability to calculate quality-adjusted life-years (QALYs) for metastatic spine disease would enhance treatment decision-making and facilitate economic analysis. QALYs are calculated using utilities.

METHODS

Using a hybrid concept-retention and factorial analysis shortening approach, we first shortened the SOSGOQ2.0 to eight items (SOSGOQ-8D). This was done to lessen the cognitive burden of the utility valuation exercise. A general population sample of 2730 adults was then asked to evaluate 12 choice sets based on SOSGOQ-8D health states in a Discrete Choice Experiment. A utility scoring rubric was then developed using a mixed multinomial-logit regression model.

RESULTS

We were able to reduce the SOSGOQ2.0 to an SOSGOQ-8D with a mean error of 0.003 and mean absolute error of 3.078 compared to the full questionnaire. The regression model demonstrated good predictive performance and was used to develop a utility scoring rubric. Regression results revealed that participants did not regard all SOSGOQ-8D items as equally important.

CONCLUSION

We provide a simple technique for converting the SOSGOQ2.0 to utilities. The ability to evaluate QALYs in metastatic spine disease will facilitate economic analysis and patient counseling. We also quantify the importance of individual SOSGOQ-8D items. Clinicians should heed these findings and offer treatments that maximize function in the most important items.Level of Evidence: 3.

The Functional Assessment of Cancer Therapy Eight Dimension (FACT-8D), a Multi-Attribute Utility Instrument Derived From the Cancer-Specific FACT-General (FACT-G) Quality of Life Questionnaire: Development and Australian Value Set

OBJECTIVES

To develop a cancer-specific multi-attribute utility instrument derived from the Functional Assessment of Cancer Therapy - General (FACT-G) health-related quality of life (HRQL) questionnaire.

METHODS

We derived a descriptive system based on a subset of the 27-item FACT-G. Item selection was informed by psychometric analyses of existing FACT-G data (n = 6912) and by patient input (n = 82). We then conducted an online valuation survey, with participants recruited via an Australian general population online panel. A discrete choice experiment (DCE) was used, with attributes being the HRQL dimensions of the descriptive system and survival duration, and 16 choice-pairs per participant. Utility decrements were estimated with conditional logit and mixed logit modeling.

RESULTS

Eight HRQL dimensions were included in the descriptive system: pain, fatigue, nausea, sleep, work, social support, sadness, and future health worry; each with 5 levels. Of 1737 panel members who accessed the valuation survey, 1644 (95%) completed 1 or more DCE choice-pairs and were included in analyses. Utility decrements were generally monotonic; within each dimension, poorer HRQL levels generally had larger utility decrements. The largest utility decrements were for the highest levels of pain (-0.40) and nausea (-0.28). The worst health state had a utility of -0.54, considerably worse than dead.

CONCLUSIONS

A descriptive system and preference-based scoring approach were developed for the FACT-8D, a new cancer-specific multi-attribute utility instrument derived from the FACT-G. The Australian value set is the first of a series of country-specific value sets planned that can facilitate cost-utility analyses based on items from the FACT-G and related FACIT questionnaires containing FACT-G items.

Valuing End-of-Life Care for Older People with Advanced Cancer: Is Dying at Home Important?

BACKGROUND

Most health care systems are facing the challenge of providing health services to support the increasing numbers of older people with chronic life-limiting conditions at the end of life. Many policies focus primarily on increasing the proportion of deaths at home.

OBJECTIVES

This study aims to investigate preferences for care throughout the latter stages of a life-limiting illness, particularly the importance of location of care, location of death, and the use of life-sustaining measures. It focuses on preferences for the care of an older person with advanced cancer in the last 3 weeks of life.

METHODS

A survey using discrete choice experiment (DCE) methods was completed online by a general population sample of 1548 Australians aged 45 years and over. The experiment included 12 attributes, and each respondent completed 11 choice sets. Analysis was by a mixed logit model and latent class analysis (LCA).

RESULTS

The most important attributes influencing care preferences were cost, patient anxiety, pain control, and carer stress (relative importance scores 0.21, 0.19, 0.14, and 0.14, respectively), with less importance given to place of care and place of death (relative importance scores 0.03 and 0.01). The model predicted that 42% would consider receiving most care in hospital better than at home (58%) holding the levels of other attributes constant across the alternatives, while 42% would consider death in hospital better than at home (58%). Three population segments with different preferences were identified by the LCA, the largest (46.5%) prioritised how the patient and carer felt as well as the pain control achieved, the next largest (28.1%) prioritised cost, and the smallest segment (25.4%) prioritised a single room when an inpatient.

CONCLUSIONS

This study shows that investment in services to support people at the end of life would be better targeted toward programmes that improve patient and carer wellbeing irrespective of the location of care and death.

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.

The EORTC QLU-C10D was more efficient in detecting clinical known group differences in myelodysplastic syndromes than the EQ-5D-3L

BACKGROUND

The aim was to investigate the relative validity of the preference-based measure EORTC QLU-C10D in comparison with the EQ-5D-3L in myelodysplastic syndromes (MDS) patients.

METHODS

We used data from an international multicentre, observational cohort study of MDS patients. Baseline EORTC QLU-C10D and EQ-5D-3L scores were used and index scores calculated for Italy, Australia, and the UK. Criterion validity was established by Spearman and intraclass correlations (ICC) and Bland-Altman plots. Construct validity was established by the instruments' ability to discriminate known groups, i.e. groups whose health status is expected to differ.

RESULTS

We analyzed data from 619 MDS patients (61.1% male; median age 73.8 years). Correlations between theoretically corresponding domains were largely higher than between unrelated domains. ICCs and Bland-Altman plots indicated moderate to good criterion validity. Ceiling effects were lower for the QLU-C10D (4.7%) than for the EQ-5D-3L (22.6%). The EQ-5D-3L failed to discriminate known-groups in two and the QLU-C10D in one of the comparisons; the QLU-C10D's efficiency in doing so was higher in clinical known-groups. Results were comparable between the countries.

CONCLUSIONS

The QLU-C10D may be suitable to generate health utilities for economic research in MDS. Responsiveness and minimal important differences need yet to be established.

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.

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.

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.

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.

Review of Valuation Methods of Preference-Based Measures of Health for Economic Evaluation in Child and Adolescent Populations: Where are We Now and Where are We Going?

Methods for measuring and valuing health benefits for economic evaluation and health technology assessment in adult populations are well developed. In contrast, methods for assessing interventions for child and adolescent populations lack detailed guidelines, particularly regarding the valuation of health and quality of life in these age groups. This paper critically examines the methodological considerations involved in the valuation of child- and adolescent-specific health-related quality of life by existing preference-based measures. It also describes the methodological choices made in the valuation of existing generic preference-based measures developed with and/or applied in child and adolescent populations: AHUM, AQoL-6D, CHU9D, EQ-5D-Y, HUI2, HUI3, QWB, 16D and 17D. The approaches used to value existing child- and adolescent-specific generic preference-based measures vary considerably. While the choice of whose preferences and which perspective to use is a matter of normative debate and ultimately for decision by reimbursement agencies and policy makers, greater research around these issues would be informative and would enrich these discussions. Research can also inform the other methodological choices required in the valuation of child and adolescent health states. Gaps in research evidence are identified around the impact of the child described in health state valuation exercises undertaken by adults, including the possibility of informed preferences; the appropriateness and acceptability of valuation tasks for adolescents, in particular tasks involving the state 'dead'; anchoring of adolescent preferences; and the generation and use of combined adult and adolescent preferences.

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.

Discrete choice experiment to evaluate preferences of patients with cystic fibrosis among alternative treatment-related health outcomes: a protocol

INTRODUCTION

Clinical decision-making is a complex process. Patient preference information regarding desirable health states should inform treatment and is critical to agreeing on goals of therapy. Cystic fibrosis (CF) is a common, inheritable multisystem disorder for which the major manifestation is progressive, chronic lung disease. Intermittent pulmonary exacerbations are a hallmark of disease and these drive lung damage that results in premature death. We suspect that clinicians make assumptions, most likely implicit assumptions, about outcomes that are desired by patients who are treated for pulmonary exacerbations. The aim of this study is to identify and quantify the preferences of patients with cystic fibrosis regarding treatment outcomes.

METHODS AND ANALYSIS

We will develop a discrete choice experiment (DCE) in collaboration with people with CF and their carers, and evaluate how patients make trade-offs between different aspects of health-related status when considering treatment options.

ETHICS AND DISSEMINATION

Ethics approval for all aspects of this study was granted by the Western Australia Child and Adolescent Health Service Human Research Ethics Committee [RGS903]. Weighted preference information from the DCE will be used to develop a multiattribute utility instrument as a measure of treatment success in the upcoming Bayesian Evidence-Adaptive Trial to optimise management of CF. Dissemination of results will also occur through peer-reviewed publications and presentations to relevant stakeholders and research networks.

The EORTC QLU-C10D: The Canadian Valuation Study and Algorithm to Derive Cancer-Specific Utilities From the EORTC QLQ-C30

Objective. The EORTC QLQ-C30 is widely used for assessing quality of life in cancer. However, QLQ-C30 responses cannot be incorporated in cost-utility analysis because they are not based on general population's preferences, or utilities. To overcome this limitation, the QLU-C10D, a cancer-specific utility algorithm, was derived from the QLQ-C30. The aim of this study was to obtain Canadian population utility weights for the QLU-C10D. Methods. Respondents from a Canadian research panel expressed their preferences for 16 choice sets in an online discrete choice experiment. Each choice set consisted of two health states described by the 10 QLU-C10D domains plus an attribute representing duration of survival. Using a conditional logit model, responses were converted into utility decrements by evaluating the marginal rate of substitution between each QLU-C10D domain level with respect to duration. Results. A total of 3,363 individuals were recruited. A total of 2,345 completed at least one choice set and 2,271 completed all choice sets. The largest utility decrements were associated with the worse levels of Physical Functioning (-0.24), Pain (-0.18), Role Functioning (-0.15), Emotional Functioning (-0.12), and Nausea (-0.12). The remaining domains and levels had decrements of -0.05 to -0.09. The utility of the worst possible health state was -0.15. Conclusion. Respondents from the general population were most concerned with generic health domains, but Nausea and Bowel Problems also had an impact on the individual's utility. It is unclear as to whether cancer-specific domains will affect cost-utility analysis when evaluating cancer treatments; this will be tested in the next phase of the study.

Attribute level overlap (and color coding) can reduce task complexity, improve choice consistency, and decrease the dropout rate in discrete choice experiments

A randomized controlled discrete choice experiment (DCE) with 3,320 participating respondents was used to investigate the individual and combined impact of level overlap and color coding on task complexity, choice consistency, survey satisfaction scores, and dropout rates. The systematic differences between the study arms allowed for a direct comparison of dropout rates and cognitive debriefing scores and accommodated the quantitative comparison of respondents' choice consistency using a heteroskedastic mixed logit model. Our results indicate that the introduction of level overlap made it significantly easier for respondents to identify the differences and choose between the choice options. As a stand-alone design strategy, attribute level overlap reduced the dropout rate by 30%, increased the level of choice consistency by 30%, and avoided learning effects in the initial choice tasks of the DCE. The combination of level overlap and color coding was even more effective: It reduced the dropout rate by 40% to 50% and increased the level of choice consistency by more than 60%. Hence, we can recommend attribute level overlap, with color coding to amplify its impact, as a standard design strategy in DCEs.

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

BACKGROUND

Discrete-choice experiments (DCEs) are used in the development of preference-based measure (PBM) value sets. There is considerable variation in the methodological approaches used to elicit preferences.

OBJECTIVE

Our objective was to carry out a structured review of DCE methods used for health state valuation.

METHODS

PubMed was searched until 31 May 2018 for published literature using DCEs for health state valuation. Search terms to describe DCEs, the process of valuation and preference-based instruments were developed. English language papers with any study population were included if they used DCEs to develop or directly inform the production of value sets for generic or condition-specific PBMs. Assessment of paper quality was guided by the recently developed Checklist for Reporting Valuation Studies. Data were extracted under six categories: general study information, choice task and study design, type of designed experiment, modelling and analysis methods, results and discussion.

RESULTS

The literature search identified 1132 published papers, and 63 papers were included in the review. Paper quality was generally high. The study design and choice task formats varied considerably, and a wide range of modelling methods were employed to estimate value sets.

CONCLUSIONS

This review of DCE methods used for developing value sets suggests some recurring limitations, areas of consensus and areas where further research is required. Methodological diversity means that the values should be seen as experimental, and users should understand the features of the value sets produced before applying them in decision making.

Severity-Stratified Discrete Choice Experiment Designs for Health State Evaluations

BACKGROUND

Discrete choice experiments (DCEs) are increasingly used for health state valuations. However, the values derived from initial DCE studies vary widely. We hypothesize that these findings indicate the presence of unknown sources of bias that must be recognized and minimized. Against this background, we studied whether values derived from a DCE are sensitive to how well the DCE design spans the severity range.

METHODS

We constructed an experiment involving three variants of DCE tasks for health state valuation: standard DCE, DCE-death, and DCE-duration. For each type of DCE, an experimental design was generated under two different conditions, enabling a comparison of health state values derived from current best practice Bayesian efficient DCE designs with values derived from 'severity-stratified' designs that control for coverage of the severity range in health state selection. About 3000 respondents participated in the study and were randomly assigned to one of the six study arms.

RESULTS

Imposing the severity-stratified restriction had a large effect on health states sampled for the DCE-duration approach. The unstratified efficient design returned a skewed distribution of selected health states, and this introduced bias. The choice probability of bad health states was underestimated, and time trade-offs to avoid bad states were overestimated, resulting in too low values. Imposing the same restriction had limited effect in the DCE-death approach and standard DCE.

CONCLUSION

Variation in DCE-derived values can be partially explained by differences in how well selected health states spanned the severity range. Imposing a 'severity stratification' on DCE-duration designs is a validity requirement.

Symptoms and feelings valued by patients after a percutaneous coronary intervention: a discrete-choice experiment to inform development of a new patient-reported outcome

OBJECTIVE

To inform the development of a patient-reported outcome measure, the aim of this study was to identify which symptoms and feelings following percutaneous coronary intervention (PCI) are most important to patients.

DESIGN

Discrete-choice experiment consisting of two hypothetical scenarios of 10 symptoms and feelings (pain or discomfort; shortness of breath; concern/worry about heart problems; tiredness; confidence to do usual activities; ability to do usual activities; happiness; sleep disturbance; dizziness or light-headedness and bruising) experienced after PCI, described by three levels (never, some of the time, most of the time). Preference weights were estimated using a conditional logit model.

SETTING

Four Australian public hospitals that contribute to the Victorian Cardiac Outcomes Registry (VCOR) and a private insurer's claim database.

PARTICIPANTS

138 people aged >18 years who had undergone a PCI in the previous 6 months.

MAIN OUTCOME MEASURES

Patient preferences via trade-offs between 10 feelings and symptoms.

RESULTS

Of the 138 individuals recruited, 129 (93%) completed all 16 choice sets. Conditional logit parameter estimates were mostly monotonic (eg, moving to worse levels for each individual symptom and feeling made the option less attractive). When comparing the magnitude of the coefficients (based on the coefficient of the worst level relative to best level in each item), feeling unhappy was the symptom or feeling that most influenced perception of a least-preferred PCI outcome (OR 0.42, 95% CI 0.34 to 0.51, p<0.0001) and the least influential was bruising (OR 0.81, 95% CI 0.67 to 0.99, p=0.04).

CONCLUSION

This study provides new insights into how patients value symptoms and feelings they experience following a PCI.

Estimating a Dutch Value Set for the Pediatric Preference-Based CHU9D Using a Discrete Choice Experiment with Duration

OBJECTIVE

This article presents the development of the Dutch value set for the Child Health Utility 9D, a pediatric preference-based measure of quality of life that can be used to generate quality-adjusted life-years.

METHODS

A large online survey was conducted using a discrete choice experiment including a duration attribute with adult members of the Netherlands general population (N = 1276) who were representative in terms of age, gender, marital status, employment, education, and region. Respondents were asked which of two health states they prefer, where each health state was described using the nine dimensions of the Child Health Utility 9D (worried, sad, pain, tired, annoyed, school work/homework, sleep, daily routine, able to join in activities) and duration. The data were modeled using conditional logit with robust standard errors to produce utility values for every health state described by the Child Health Utility 9D.

RESULTS

The majority of the dimension level coefficients were monotonic, leading to a decrease in utility as severity increases. There was, however, evidence of some logical inconsistencies, particularly for the school work/homework dimension. The value set produced was based on the ordered model and ranges from -0.568 for the worst state to 1 for the best state.

CONCLUSION

The valuation of the Child Health Utility 9D using online discrete choice experiment with duration with adult members of the Dutch general population was feasible and produced a valid model for use in cost utility analysis. Normative questions are raised around the valuation of pediatric preference-based measures, including the appropriate perspective for imagining hypothetical pediatric health states.

Advocating a Paradigm Shift in Health-State Valuations: The Estimation of Time-Preference Corrected QALY Tariffs

BACKGROUND

Despite evidence of nonproportional trade-offs in time trade-off exercises and the explicit incorporation of exponential discounting in health technology assessment calculations, quality-adjusted life-year (QALY) tariffs are currently still established under the assumption of linear time preferences.

OBJECTIVES

The aim of this study was to introduce a general method of accommodating for nonlinear time preferences in discrete choice experiment (DCE) duration studies and to evaluate its impact on estimated QALY tariffs.

METHODS

A parsimonious utility function is proposed that accommodates any discounting function and preserves linear time preferences as a special case. Based on an efficient DCE design and 1775 respondents from a nationally representative scientific household panel, preferences and QALY tariffs for the Dutch SF-6D were estimated while accommodating for nonlinear time preferences via exponential and hyperbolic discounting functions.

RESULTS

When the discount rate was estimated directly, we found strong evidence of nonlinear time preferences (with an exponential and hyperbolic discount rate of 5.7% and 16.5%, respectively). When the discount rate was estimated as a function of health state severity, we found that years lived in better health states are discounted minus years lived in impaired health states. Finally, the best statistical fit was obtained when using a hyperbolic discount function, which resulted in smaller QALY decrements and fewer health states classified as worse than immediate death.

CONCLUSIONS

Our results highlight the relevance and even necessity of a paradigm shift in health valuation studies in favor of time-preference corrected QALY tariffs, with potentially important implications for health technology assessment calculations and regulatory decisions.

Test-Retest Reliability of Discrete Choice Experiment for Valuations of QLU-C10D Health States

BACKGROUND

Recently, a newly developed cancer-specific multiattribute utility instrument based on the widely used health-related quality of life instrument, the European Organisation for Research and Treatment of Cancer QLQ-C30, was introduced: the QLU-C10D. For the elicitation of utility weights, a discrete choice experiment (DCE) was designed. Our aim was to investigate the DCE in terms of individual choice consistency and utility estimate consistency by applying a test-retest design.

METHODS

We conducted the study in general population samples in Germany and France. The DCE was administered via a web-based self-complete survey using online panels. Respondents were presented 16 choice sets comprising 11 attributes with 4 levels each. Retest was conducted 4 to 6 weeks after first assessment. We used kappa and percentage agreement as measures of choice consistency and both intraclass correlations and mean utility differences as measures of utility estimate consistency.

RESULTS

A total of 300 German respondents (31% female, mean age 48 years [SD 14]) and 305 French respondents (46% female, mean age 47 years [SD 16]) completed test and retest assessments. Individual choice consistency was moderate to high (Germany: κ = 0.605, percentage agreement = 80.2%; France: κ = 0.411, percentage agreement = 70.6%). Utility estimate consistency was high when considering intraclass correlations (all >0.79). Mean utility differences were 0.08 in the German sample and 0.05 in the French sample.

CONCLUSIONS

Results indicate that the designed DCE elicits stable health state preferences rather than guesses or mood-specific or condition-specific judgments. Nevertheless, the identified mean utility differences between test and retest need to be taken into account when determining minimal important differences for the QLU-C10D in future research.

Effect of Level Overlap and Color Coding on Attribute Non-Attendance in Discrete Choice Experiments

OBJECTIVE

The aim of this study was to test the hypothesis that level overlap and color coding can mitigate or even preclude the occurrence of attribute nonattendance in discrete choice experiments.

METHODS

A randomized controlled experiment with five experimental study arms was designed to investigate the independent and combined impact of level overlap and color coding on respondents' attribute nonattendance. The systematic differences between the study arms allowed for a direct comparison of observed dropout rates and estimates of the average number of attributes attended to by respondents, which were obtained by using augmented mixed logit models that explicitly incorporated attribute non-attendance.

RESULTS

In the base-case study arm without level overlap or color coding, the observed dropout rate was 14%, and respondents attended, on average, only two out of five attributes. The independent introduction of both level overlap and color coding reduced the dropout rate to 10% and increased attribute attendance to three attributes. The combination of level overlap and color coding, however, was most effective: it reduced the dropout rate to 8% and improved attribute attendance to four out of five attributes. The latter essentially removes the need to explicitly accommodate for attribute non-attendance when analyzing the choice data.

CONCLUSIONS

On the basis of the presented results, the use of level overlap and color coding are recommendable strategies to reduce the dropout rate and improve attribute attendance in discrete choice experiments.

Australian Utility Weights for the EORTC QLU-C10D, a Multi-Attribute Utility Instrument Derived from the Cancer-Specific Quality of Life Questionnaire, EORTC QLQ-C30

BACKGROUND

The EORTC QLU-C10D is a new multi-attribute utility instrument derived from the widely used cancer-specific quality-of-life (QOL) questionnaire, EORTC QLQ-C30. The QLU-C10D contains ten dimensions (Physical, Role, Social and Emotional Functioning; Pain, Fatigue, Sleep, Appetite, Nausea, Bowel Problems), each with four levels. To be used in cost-utility analysis, country-specific valuation sets are required.

OBJECTIVE

The aim of this study was to provide Australian utility weights for the QLU-C10D.

METHODS

An Australian online panel was quota-sampled to ensure population representativeness by sex and age (≥ 18 years). Participants completed a discrete choice experiment (DCE) consisting of 16 choice-pairs. Each pair comprised two QLU-C10D health states plus life expectancy. Data were analysed using conditional logistic regression, parameterised to fit the quality-adjusted life-year framework. Utility weights were calculated as the ratio of each QOL dimension-level coefficient to the coefficient on life expectancy.

RESULTS

A total of 1979 panel members opted in, 1904 (96%) completed at least one choice-pair, and 1846 (93%) completed all 16 choice-pairs. Dimension weights were generally monotonic: poorer levels within each dimension were generally associated with greater utility decrements. The dimensions that impacted most on choice were, in order, Physical Functioning, Pain, Role Functioning and Emotional Functioning. Oncology-relevant dimensions with moderate impact were Nausea and Bowel Problems. Fatigue, Trouble Sleeping and Appetite had relatively small impact. The value of the worst health state was -0.096, somewhat worse than death.

CONCLUSIONS

This study provides the first country-specific value set for the QLU-C10D, which can facilitate cost-utility analyses when applied to data collected with the EORTC QLQ-C30, prospectively and retrospectively.

The Role of Condition-Specific Preference-Based Measures in Health Technology Assessment

A condition-specific preference-based measure (CSPBM) is a measure of health-related quality of life (HRQOL) that is specific to a certain condition or disease and that can be used to obtain the quality adjustment weight of the quality-adjusted life-year (QALY) for use in economic models. This article provides an overview of the role and the development of CSPBMs, and presents a description of existing CSPBMs in the literature. The article also provides an overview of the psychometric properties of CSPBMs in comparison with generic preference-based measures (generic PBMs), and considers the advantages and disadvantages of CSPBMs in comparison with generic PBMs. CSPBMs typically include dimensions that are important for that condition but may not be important across all patient groups. There are a large number of CSPBMs across a wide range of conditions, and these vary from covering a wide range of dimensions to more symptomatic or uni-dimensional measures. Psychometric evidence is limited but suggests that CSPBMs offer an advantage in more accurate measurement of milder health states. The mean change and standard deviation can differ for CSPBMs and generic PBMs, and this may impact on incremental cost-effectiveness ratios. CSPBMs have a useful role in HTA where a generic PBM is not appropriate, sensitive or responsive. However, due to issues of comparability across different patient groups and interventions, their usage in health technology assessment is often limited to conditions where it is inappropriate to use a generic PBM or sensitivity analyses.

How Should Discrete Choice Experiments with Duration Choice Sets Be Presented for the Valuation of Health States?

BACKGROUND

Discrete Choice Experiments including duration (DCE_TTO) can be used to generate utility values for health states from measures such as EQ-5D-5L. However, methodological issues concerning the optimum way to present choice sets remain. The aim of the present study was to test a range of task presentation approaches designed to support the DCE_TTO completion process.

METHODS

Four separate presentation approaches were developed to examine different task features including dimension level highlighting, and health state severity and duration level presentation. Choice sets included 2 EQ-5D-5L states paired with 1 of 4 duration levels, and a third "immediate death" option. The same design, including 120 choice sets (developed using optimal methods), was employed across all approaches. The online survey was administered to a sample of the Australian population who completed 20 choice sets across 2 approaches. Conditional logit regression was used to assess model consistency, and scale parameter testing investigated poolability.

RESULTS

Overall 1,565 respondents completed the survey. Three approaches, using different dimension level highlighting techniques, produced mainly monotonic coefficients that resulted in a larger disutility as the severity level increased (excepting usual activities levels 2/3). The fourth approach, using a level indicator to present the severity levels, has slightly more non-monotonicity and produced larger ordered differences for the more severe dimension levels. Scale parameter testing suggested that the data cannot be pooled.

CONCLUSIONS

The results provide information regarding how to present DCE tasks for health state valuation. The findings improve our understanding of the impact of different presentation approaches on valuation, and how DCE questions could be presented to be amenable to completion. However, it is unclear if the task presentation impacts online respondent engagement.