Role Preferences in Medical Decision Making: Relevance and Implications for Health Preference Research.

Health preference research (HPR) is being increasingly conducted to better understand patient preferences for medical decisions. However, patients vary in their desire to play an active role in medical decisions. Until now, few studies have considered patients' preferred roles in decision making. In this opinion paper, we advocate for HPR researchers to assess and account for role preferences in their studies, to increase the relevance of their work for medical and shared decision making. We provide recommendations on how role preferences can be elicited and integrated with health preferences: (1) in formative research prior to a health preference study that aims to inform medical decisions or decision makers, (2a) in the development of health preference instruments, for instance by incorporating a role preference instrument and (2b) by clarifying the respondent's role in the decision prior to the preference elicitation task or by including role preferences as an attribute in the task itself, and (3) in statistical analysis by including random parameters or latent classes to raise awareness of heterogeneity in role preferences and how it relates to health preferences. Finally, we suggest redefining the decision process as a model that integrates the role and health preferences of the different parties that are involved. We believe that the field of HPR would benefit from learning more about the extent to which role preferences relate to health preferences, within the context of medical and shared decision making.

Current Practices for Accounting for Preference Heterogeneity in Health-Related Discrete Choice Experiments: A Systematic Review.

BACKGROUND: Accounting for preference heterogeneity is a growing analytical practice in health-related discrete choice experiments (DCEs). As heterogeneity may be examined from different stakeholder perspectives with different methods, identifying the breadth of these methodological approaches and understanding the differences are major steps to provide guidance on good research practices. OBJECTIVES: Our objective was to systematically summarize current practices that account for preference heterogeneity based on the published DCEs related to healthcare. METHODS: This systematic review is part of the project led by the Professional Society for Health Economics and Outcomes Research (ISPOR) health preference research special interest group. The systematic review conducted systematic searches on the PubMed, OVID, and Web of Science databases, as well as on two recently published reviews, to identify articles. The review included health-related DCE articles published between 1 January 2000 and 30 March 2020. All the included articles also presented evidence on preference heterogeneity analysis based on either explained or unexplained factors or both. RESULTS: Overall, 342 of the 2202 (16%) articles met the inclusion/exclusion criteria for extraction. The trend showed that analyses of preference heterogeneity increased substantially after 2010 and that such analyses mainly examined heterogeneity due to observable or unobservable factors in individual characteristics. Heterogeneity through observable differences (i.e., explained heterogeneity) is identified among 131 (40%) of the 342 articles and included one or more interactions between an attribute variable and an observable characteristic of the respondent. To capture unobserved heterogeneity (i.e., unexplained heterogeneity), the studies largely estimated either a mixed logit (n = 205, 60%) or a latent-class logit (n = 112, 32.7%) model. Few studies (n = 38, 11%) explored scale heterogeneity or heteroskedasticity. CONCLUSIONS: Providing preference heterogeneity evidence in health-related DCEs has been found as an increasingly used practice among researchers. In recent studies, controlling for unexplained preference heterogeneity has been seen as a common practice rather than explained ones (e.g., interactions), yet a lack of providing methodological details has been observed in many studies that might impact the quality of analysis. As heterogeneity can be assessed from different stakeholder perspectives with different methods, researchers should become more technically pronounced to increase confidence in the results and improve the ability of decision makers to act on the preference evidence.

Using stated-preferences methods to develop a summary metric to determine successful treatment of children with a surgical condition: a study protocol.

INTRODUCTION: Wide variation in the management of key paediatric surgical conditions in the UK has likely resulted in outcomes for some children being worse than they could be. Consequently, it is important to reduce unwarranted variation. However, major barriers to this are the inability to detect differences between observed and expected hospital outcomes based on the casemix of the children they have treated, and the inability to detect variation in significant outcomes between hospitals. A stated-preference study has been designed to estimate the value key stakeholders place on different elements of the outcomes for a child with a surgical condition. This study proposes to develop a summary metric to determine what represents successful treatment of children with surgical conditions. METHODS AND ANALYSIS: Preferences from parents, individuals treated for surgical conditions as infants/children, healthcare professionals and members of the public will be elicited using paired comparisons and kaizen tasks. A descriptive framework consisting of seven attributes representing types of operations, infections treated in hospital, quality of life and survival was identified. An experimental design has been completed using a D-efficient design with overlap in three attributes and excluding implausible combinations. All participants will be presented with an additional choice task including a palliative scenario that will be used as an anchor. The survey will be administered online. Primary analysis will estimate a mixed multinomial logit model. A traffic light system to determine what combination of attributes and levels represent successful treatment will be created. ETHICS AND DISSEMINATION: Ethics approval to conduct this study has been obtained from the Medical Sciences Inter-Divisional Research Ethics Committee (IDREC) at the University of Oxford (R59631/RE001-05). We will disseminate all of our results in peer-review publications and scientific presentations. Findings will be additionally disseminated through relevant charities and support groups and professional organisations.

Exploring the importance of controlling heteroskedasticity and heterogeneity in health valuation: a case study on Dutch EQ-5D-5L.

BACKGROUND: Respondents in a health valuation study may have different sources of error (i.e., heteroskedasticity), tastes (differences in the relative effects of each attribute level), and scales (differences in the absolute effects of all attributes). Although prior studies have compared values by preference-elicitation tasks (e.g., paired comparison [PC] and best-worst scaling case 2 [BWS]), no study has yet controlled for heteroskedasticity and heterogeneity (taste and scale) simultaneously in health valuation. METHODS: Preferences on EQ-5D-5L profiles were elicited from a random sample of 380 adults from the general population of the Netherlands, using 24 PC and 25 BWS case 2 tasks. To control for heteroskedasticity and heterogeneity (taste and scale) simultaneously, we estimated Dutch EQ-5D-5L values using conditional, heteroskedastic, and scale-adjusted latent class (SALC) logit models by maximum likelihood. RESULTS: After controlling for heteroskedasticity, the PC and BWS values were highly correlated (Pearson's correlation: 0.9167, CI: 0.9109-0.9222) and largely agreed (Lin's concordance: 0.7658, CI: 0.7542-0.7769) on a pits scale. In terms of preference heterogeneity, some respondents (mostly young men) failed to account for any of the EQ-5D-5L attributes (i.e., garbage class), and others had a lower scale (59%; p-value: 0.123). Overall, the SALC model produced a consistent Dutch EQ-5D-5L value set on a pits scale, like the original study (Pearson's correlation:0.7295; Lin's concordance: 0.6904). CONCLUSIONS: This paper shows the merits of simultaneously controlling for heteroskedasticity and heterogeneity in health valuation. In this case, the SALC model dispensed with a garbage class automatically and adjusted the scale for those who failed the PC dominant task. Future analysis may include more behavioral variables to better control heteroskedasticity and heterogeneity in health valuation. HIGHLIGHTS: The Dutch EQ-5D-5L values based on paired comparison [PC] and best-worst scaling [BWS] responses were highly correlated and largely agreed after controlling for heteroskedasticity. Controlling for taste and scale heterogeneity simultaneously enhanced the Dutch EQ-5D-5Lvalues by automatically dispensing with a garbage class and adjusting the scale for those who failed the dominant task. After controlling for heteroskedasticity and heterogeneity, this study produced Dutch EQ-5D-5L values on a pits scale moderately concordant with the original values.

QALYs for COVID-19: A Comparison of US EQ-5D-5L Value Sets.

BACKGROUND: In economic evaluations, quality-adjusted life-years (QALYs) can serve as a unit of measurement for disease burden. Obtaining QALY values for COVID-19 presents a challenge owing to the availability of two US EQ-5D-5L value sets and the potentially asymptomatic presentation of the disease. The first value set was completed allowing for the discounting of future health outcomes while the second value set is undiscounted. OBJECTIVE: The objective of this study was to compare the distribution of QALY values using a national survey and the two published value sets; and to estimate the association between COVID-19 outcomes and QALY losses. METHODS: Between 9 and 11 November, 2020, 1153 US adults completed the EQ-5D-5L instrument (five items and a visual analog scale) as well as self-reported their demographics, COVID-19 symptoms, and memberships to populations that are at risk of COVID-19 infection. The two US value sets were applied to the EQ-5D-5L responses to produce QALY values. We estimated the mean QALYs by visual analog scale decile and a generalized linear model of COVID-19 outcomes. RESULTS: The discounted values are higher than the undiscounted values for each visual analog scale decile owing to methodological differences. Persons at increased risk, with a fever in the past day, and with one or more other symptoms have significantly greater QALY losses (p < 0.01). Overall, non-institutionalized individuals at risk of symptomatic clinical COVID-19 equal 0.68 for the 2016 value set (95% confidence interval 0.49-0.87) and 0.10 for the 2017 value set (95% confidence interval - 0.31 to 0.51) QALYs. CONCLUSIONS: Multiple studies have shown that decision makers discount future health outcomes, which increase QALY values. This study confronts the practical implications of these methodological advances for use in COVID-19 economic evaluations. Health economists will be able to use the QALY values in this study to better evaluate health interventions against COVID-19.

Does Controlling for Scale Heterogeneity Better Explain Respondents' Preference Segmentation in Discrete Choice Experiments? A Case Study of US Health Insurance Demand.

Analyses of preference evidence frequently confuse heterogeneity in the effects of attribute parameters (i.e., taste coefficients) and the scale parameter (i.e., variance). Standard latent class models often produce unreasonable classes with high variance and disordered coefficients because of confounding estimates of effect and scale heterogeneity. In this study, we estimated a scale-adjusted latent class model in which scale classes (heteroskedasticity) were identified using respondents' randomness in choice behavior on the internet panel (e.g., time to completion and time of day). Hence, the model distinctly explained the taste/preference variation among classes associated with individual socioeconomic characters, in which scales are adjusted. Using data from a discrete-choice experiment on US health insurance demand among single employees, the results demonstrated how incorporating behavioral data enhances the interpretation of heterogeneous effects. Once scale heterogeneity was controlled, we found substantial heterogeneity with 4 taste classes. Two of the taste classes were highly premium sensitive (economy class), coming mostly from the low-income group, and the class associated with better educational backgrounds preferred to have a better quality of coverage of health insurance plans. The third class was a highly quality-sensitive class, with a higher SES background and lower self-stated health condition. The last class was identified as stayers, who were not premium or quality sensitive. This case study demonstrates that one size does not fit all in the analysis of preference heterogeneity. The novel use of behavioral data in the latent class analysis is generalizable to a wide range of health preference studies.

Peruvian Valuation of the EQ-5D-5L: A Direct Comparison of Time Trade-Off and Discrete Choice Experiments.

OBJECTIVES: (1) To produce Peruvian general population EQ-5D-5L value sets on a quality-adjusted life-year scale, (2) to investigate the feasibility of a "Lite" protocol less reliant on the composite time trade-off (cTTO), and (3) to compare cTTO and discrete choice experiment (DCE) value sets. METHODS: A random sample of adults (N = 1000) in Lima, Arequipa, and Iquitos did a home interview; 300 were randomly selected to complete 11 cTTOs first. All respondents completed a DCE, including 10 latent-scale pairs (A/B) with 5 EQ-5D-5L attributes, and 12 matched pairs (A/B and B/C) with 5 EQ-5D-5L and one lifespan attributes. We estimated a cTTO heteroscedastic tobit (N = 300) model and 3 DCE Zermelo-Bradley-Terry models (N = 300, 700, and 1000). RESULTS: Each model produced a consistent value set (20 positive incremental parameters). Nevertheless, their lowest quality-adjusted life-year values differed greatly (cTTO: -1.076 [N = 300]; DCE: -0.984 [300], 0.048 [700], -0.213 [1000]). Compared with the cTTO, the DCE (N = 300) produced different parameters (Pearson's correlation = 0.541), fewer insignificant parameters (0 vs 8), and fewer values less than 0 (26% vs 44%). Compared with the DCE (N = 300), the DCE (N = 700) produced higher values but similar parameters (Pearson's correlation = 0.800). CONCLUSIONS: Besides producing EQ-5D-5L value sets for Peru, the results casts doubt about the feasibility of a Lite protocol like the one in this study. Additionally, fundamental differences between cTTO and DCE-without the existence of a gold standard-need further clarification. The choice between the two rational value sets produced in the current study is a matter of judgment and may have substantial policy implications.

Reporting Formative Qualitative Research to Support the Development of Quantitative Preference Study Protocols and Corresponding Survey Instruments: Guidelines for Authors and Reviewers.

BACKGROUND: Formative qualitative research is foundational to the methodological development process of quantitative health preference research (HPR). Despite its ability to improve the validity of the quantitative evidence, formative qualitative research is underreported. OBJECTIVE: To improve the frequency and quality of reporting, we developed guidelines for reporting this type of research. The guidelines focus on formative qualitative research used to develop robust and acceptable quantitative study protocols and corresponding survey instruments in HPR. METHODS: In December 2018, a steering committee was formed as a means to accumulate the expertise of the HPR community on the reporting guidelines (21 members, seven countries, multiple settings and disciplines). Using existing guidelines and examples, the committee constructed, revised, and refined the guidelines. The guidelines underwent beta testing by three researchers, and further revisions to the guidelines were made based on their feedback as well as on comments from members of the International Academy of Health Preference Research (IAHPR) and the editorial board of The Patient: Patient-Centered Outcomes Research. RESULTS: The guidelines have five components: introductory material (4 domains), methods (12), results/findings (2), discussion (2), and other (2). They are concordant with existing guidelines, published examples, beta-testing results, and expert comments. CONCLUSIONS: Publishing formative qualitative research is a necessary step toward strengthening the foundation of any quantitative study, enhancing the relevance of its preference evidence. The guidelines should aid researchers, reviewers, and regulatory agencies and promote transparency within HPR more broadly.

Does Device or Connection Type Affect Health Preferences in Online Surveys?

BACKGROUND AND OBJECTIVE: Recent evidence has shown that online surveys can reliably collect preference data, which markedly decrease the cost of health preference studies and expand their representativeness. As the use of mobile technology continues to grow, we wanted to examine its potential impact on health preferences. METHODS: Two recently completed discrete choice experiments using members of the US general population (n = 15,292) included information on respondent device (cell phone, tablet, Mac, PC) and internet connection (business, cellular, college, government, residential). In this analysis, we tested for differences in respondent characteristics, participation, response quality, and utility values for the 5-level EQ-5D (EQ-5D-5L) by device and connection. RESULTS: Compared to Mac and PC users, respondents using a cell phone or tablet had longer completion times and were significantly more likely to drop out during the surveys (p < 0.001). Tablet users also demonstrated more logical inconsistencies (p = 0.05). Likewise, respondents using a cellular internet connection exhibit significantly less consistency in their health preferences. However, matched samples for tablets and cell phones produced similar EQ-5D-5L utility values (mean differences < 0.06 on a quality-adjusted life-year [QALY] scale for all potential health states). CONCLUSION: Allowing respondents to complete online surveys using a cell phone or tablet or over a cellular connection substantially increases the diversity of respondents and the likelihood of obtaining a representative sample, as many individuals have cell phones but not a computer. While the results showed systematic variability in participation and response quality by device and connection type, this study did not show any meaningful changes in utility values.

The Value Employees Place on Health Insurance Plans: A Discrete-Choice Experiment.

BACKGROUND: The federally-facilitated Health Insurance Marketplace-also known as the Health Insurance Exchange-was designed as a tool to help people purchase insurance plans, yet many Americans remain uninsured, partially due to rising premiums. One possible strategy to stabilize its premiums is to encourage healthier people to purchase their plans through the Marketplace instead of through their employers. OBJECTIVE: This study examined the values that single adults with employer-based coverage place on health insurance plan attributes using a discrete-choice experiment (DCE). METHODS: As part of an online survey, each respondent completed 28 paired comparisons trading off four attributes: source of coverage, plan type, monthly out-of-pocket premium, and quality of coverage. RESULTS: Based on our results (N = 2207), single employees slightly preferred their employer over the Marketplace as a source of coverage (0.726 odds ratio; p value < 0.01). Single employees would be willing to switch to the Marketplace for a US$25 reduction in monthly premiums. Preferred Provider Organization (PPO) plans were overwhelmingly preferred over all other plan types, especially compared to Fee-for-Service (FFS) plans (4.230 odds ratio; p value < 0.01). The predicted probability that a health insurance plan from the Marketplace would be chosen ranged from 42 to 43.7%. CONCLUSION: This study demonstrated that a US$25 subsidy or providing slightly more generous coverage (Bronze-Silver) would motivate employees to purchase PPO plans through the Marketplace, potentially improving its risk pooling, reducing employers' administrative burden, and enhancing labor mobility.

Quality-Adjusted Life-Years without Constant Proportionality.

BACKGROUND: A quality-adjusted life-year is a common unit of measurement in health valuation. Under its constant proportionality assumption, the value of a quality-adjusted life span is defined as the product of preference weight and life span. OBJECTIVES: To empirically identify an alternative functional relationship between life span and value by relaxing the constant proportionality assumption. METHODS: Using an online survey, 5367 respondents completed 30 to 40 paired comparisons where each involved a choice between two health outcomes: one with a longer life span and health problems (five-level EuroQol five-dimensional questionnaire) and the other with a shorter life span and no problems (time trade-off pair). Using 2670 pairs, a saturated model with indicator variables for 27 life spans and 90 health problems of varying duration and severity was estimated by maximum likelihood. Its coefficients empirically illustrate the relationship between life span and value on a quality-adjusted life-year scale. RESULTS: The results reject constant proportionality (P < 0.01) and support the use of a power function to describe the relationship between life span and value, namely, value = preference weight × life spanß. The estimate of power (ß = 0.415; 95% confidence interval 0.41-0.42) appears to depend on whether life span was expressed in a temporal unit of days (0.403), weeks (0.509), months (0.541), or years (0.654). CONCLUSIONS: Raising life span to a power less than 1 implies decreasing marginal value of life span and greatly improved model fit, and confirms previous violations of proportionality. This power function may replace conventional assumptions in health valuation studies. Nevertheless, governmental agencies may favor a longer time horizon than that of the general population.

Examining the Association Between Maternal Smoking During Pregnancy and Child Behavior Problems Using Quality-Adjusted Life Years.

Objectives Examining the association between maternal smoking and losses in childhood health-related quality of life due to behavior problems provides parents and policymakers another tool for the valuation of smoking cessation during pregnancy. Methods Using the National Longitudinal Survey of Youth 1979 Child and Young Adult data, this study retrospectively examined a cohort of 4114 women and 8668 children. In addition to questions focusing on maternal smoking and general demographics, each survey included the Behavior Problems Index (BPI), a 28-item questionnaire with six subscales measuring childhood behavior problems (antisocial behavior, anxiousness/depression, headstrongness, hyperactivity, immature dependency, and peer conflict/social withdrawal). Responses to the BPI, completed by mothers with children ages 4-14, were summarized on a QALY scale using published preference weights. Results Children whose mothers smoked during pregnancy experience additional QALY losses of 0.181, on average, per year due to increased behavior problems. Boys suffered larger QALY losses associated with maternal smoking (0.242) compared to girls (0.119; p value = .021), regardless of age. Moreover, heavier smoking during pregnancy (i.e., 1 or more packs/day) was associated with larger QALY losses (0.282; p-value < .001). Conclusions for Practice These findings illustrate the burden of maternal smoking during pregnancy on child health, namely behavioral problems. The losses in QALYs may be incorporated into economic evaluations for smoking cessation interventions during pregnancy. Future research will investigate how maternal smoking following childbirth is associated with child QALYs.

Handling Data Quality Issues to Estimate the Spanish EQ-5D-5L Value Set Using a Hybrid Interval Regression Approach.

BACKGROUND: The Spanish five-level EuroQol five-dimensional questionnaire (EQ-5D-5L) valuation study was the first to use the EuroQol Valuation Technology protocol, including composite time trade-off (C-TTO) and discrete choice experiments (DCE). In this study, its investigators noticed that some interviewers did not fully explain the C-TTO task to respondents. Evidence from a follow-up study in 2014 confirmed that when interviewers followed the protocol, the distribution of C-TTO responses widened. OBJECTIVES: To handle the data quality issues in the C-TTO responses by estimating a hybrid interval regression model to produce a Spanish EQ-5D-5L value set. METHODS: Four different models were tested. Model 0 integrated C-TTO and DCE responses in a hybrid model and models 1 to 3 altered the interpretation of the C-TTO responses: model 1 allowed for censoring of the C-TTO responses, whereas model 2 incorporated interval responses and model 3 included the interviewer-specific protocol violations. For external validation, the predictions of the four models were compared with those of the follow-up study using the Lin's concordance correlation coefficient. RESULTS: This stepwise approach to modeling C-TTO and DCE responses improved the concordance between the valuation and follow-up studies (concordance correlation coefficient: 0.948 [model 0], 0.958 [model 1], 0.952 [model 2], and 0.989 [model 3]). We recommend the estimates from model 3, because its hybrid interval regression model addresses the data quality issues found in the valuation study. CONCLUSIONS: Protocol violations may occur in any valuation study; handling them in the analysis can improve external validity. The resulting EQ-5D-5L value set (model 3) can be applied to inform Spanish health technology assessments.

Choice Defines QALYs: A US Valuation of the EQ-5D-5L.

BACKGROUND: The 5-level version of the EQ-5D (EQ-5D-5L) was introduced as an improvement on the original 3-level version (EQ-5D-3L). To date, 6 country-specific value sets have been published for EQ-5D-5L and 9 US value sets have been published for other instruments. Our aims were to (1) produce EQ-5D-5L values on a quality-adjusted life year (QALY) scale from the perspective of US adults and (2) compare them with US EQ-5D-3L values and the other country-specific EQ-5D-5L values. METHODS: In 2016, 8222 US respondents from all 50 states and Washington, DC completed an online survey including a discrete choice experiment with 20 paired comparisons. Each comparison asked respondents, "Which do you prefer?" regarding a pair of alternatives described using EQ-5D-5L and lifespan attributes. On the basis of more than 50 choices on each of the 3160 pairs, we estimated EQ-5D-5L values on a QALY scale and compared them with the US EQ-5D-3L values and the other country-specific EQ-5D-5L values. RESULTS: Ranging from -0.287 (55555) to 0.992 (11121) on a QALY scale, the estimated EQ-5D-5L values were similar to the US EQ-5D-3L values. Compared with the US EQ-5D-3L values, the values exhibited greater sensitivity and specificity and higher correlation with the EQ-5D-5L values of other countries, particularly England. CONCLUSIONS: Like previous US valuation studies, this study produced nationally representative EQ-5D-5L values on a QALY scale. The results further demonstrate the advantages of the EQ-5D-5L over its 3-level predecessor as a preference-based summary measure of health-related quality of life from the perspective of US adults.

Choice Defines Value: A Predictive Modeling Competition in Health Preference Research.

OBJECTIVE: To identify which specifications and approaches to model selection better predict health preferences, the International Academy of Health Preference Research (IAHPR) hosted a predictive modeling competition including 18 teams from around the world. METHODS: In April 2016, an exploratory survey was fielded: 4074 US respondents completed 20 out of 1560 paired comparisons by choosing between two health descriptions (e.g., longer life span vs. better health). The exploratory data were distributed to all teams. By July, eight teams had submitted their predictions for 1600 additional pairs and described their analytical approach. After these predictions had been posted online, a confirmatory survey was fielded (4148 additional respondents). RESULTS: The victorious team, "Discreetly Charming Econometricians," led by Michal Jakubczyk, achieved the smallest χ2, 4391.54 (a predefined criterion). Its primary scientific findings were that different models performed better with different pairs, that the value of life span is not constant proportional, and that logit models have poor predictive validity in health valuation. CONCLUSIONS: The results demonstrated the diversity and potential of new analytical approaches in health preference research and highlighted the importance of predictive validity in health valuation.

Comparing and transforming PROMIS utility values to the EQ-5D.

PURPOSE: Summarizing patient-reported outcomes (PROs) on a quality-adjusted life year (QALY) scale is an essential component to any economic evaluation comparing alternative medical treatments. While multiple studies have compared PRO items and instruments based on their psychometric properties, no study has compared the preference-based summary of the EQ-5D-3L and Patient Reported Outcomes Measurement Information System (PROMIS-29) instruments. As part of this comparison, a major aim of this manuscript is to transform PROMIS-29 utility values to an EQ-5D-3L scale. METHODS: A nationally representative survey of 2623 US adults completed the 29-item PROMIS health profile instrument (PROMIS-29) and the 3-level version of the EQ-5D instrument (EQ-5D-3L). Their responses were summarized on a health utility scale using published estimates. Using regression analysis, PROMIS-29 and EQ-5D-3L utility weights were compared with each other as well as with self-reported general health. RESULTS: PROMIS-29 utility weights were much lower than the EQ-5D-3L weights. However, a correlation coefficient of 0.769 between the utility values of the two instruments suggests that the main discordance is simply a difference in scale between the measures. It is also possible to map PROMIS-29 utility weights onto an EQ-5D-3L scale. EQ-5D-3L losses equal .1784 × (PROMIS-29 Losses).7286. CONCLUSIONS: The published estimates of the PROMIS-29 produce lower utility values than many other health instruments. Mapping the PROMIS-29 estimates to an EQ-5D-3L scale alleviates this issue and allows for a more straightforward comparison between the PROMIS-29 and other common health instruments.

Further evidence on EQ-5D-5L preference inversion: a Brazil/U.S. collaboration.

PURPOSE: A preference inversion occurs when "worse" health (instead of "better" health) along a scale or score is preferred. Our aim was to confirm past findings of EQ-5D-5L preference inversions among English-speaking respondents and to explore inversions among Portuguese-speaking respondents. Anecdotal evidence suggests that inversions may be more common in the Portuguese version, where the translation of the fourth level "severely" (gravemente) means "gravely." METHODS: Through an infusion clinic in Tampa, Florida, United States and a cardiology clinic in Rio de Janeiro, Brazil, 740 respondents completed a tablet-based survey, which included the EQ-5D-3L and -5L followed by paired comparisons designed to assess preference inversions between the fourth and fifth levels of each of the five domains: Mobility (MO), Self-Care (SC), Usual Activity (UA), Pain/Discomfort (PD), and Anxiety/Depression (AD). An example from the AD dimension would be: "Which do you prefer? Starting today, 30 days with health problems: Severely anxious or depressed or Extremely anxious or depressed" (i.e., Level 4 AD vs. Level 5 AD). RESULTS: In the English-speaking respondents, preference inversion was only observed to a substantial extent in the AD dimension (U.S. N = 470; 7% MO, 14% SC, 14% UA, 20% PD, and 45% AD). Inversions were more common among the Portuguese-speaking respondents (Brazil N = 270; 11% MO, 32% SC, 35% UA, 49% PD, and 65% AD). Specifically, 44 out of 68 Brazilian respondents (65%) preferred "extremamente" (Level 5 AD) over "gravemente ansioso (a) ou deprimido (a)" (Level 4 AD). CONCLUSIONS: This evidence confirms previous findings for the U.S. English version of the EQ-5D-5L and led to a relabeling on the Portuguese version. It demonstrates the usefulness of collaboration between psychometric, econometric, and linguistic experts in developing the wording for and translating preference-based measures of health-related quality of life. Further research may explore inversions in other translations. The authors recommend that preference inversion tests should be included in the development and translation process.