EQ-5D Value Sets Research Articles

The EQ-5D-5L valuation study for Trinidad and Tobago

PurposeThe 2016 EQ-5D-3L value set for Trinidad and Tobago (T&T) allows for the calculation of EQ-5D-5L values via the crosswalk algorithm. The 2016 value set was based on methods predating the EQ-VT protocol, now considered the gold standard for developing EQ-5D value sets. Furthermore, direct elicitation of EQ-5D-5L is preferred over crosswalked values. This study aimed to produce an EQ-5D-5L value set for T&T.MethodsA representative sample (age, sex, geography) of adults each completed 10 composite Time Trade-Off (cTTO) tasks and 12 Discrete Choice Experiment (DCE) tasks in face-to-face interviews. The cTTO data were analyzed using a Tobit model that corrects for heteroskedasticity. DCE data were analyzed using a mixed logit model. The cTTO and DCE data were combined in hybrid models.ResultsOne thousand and seventy-nine adults completed the valuation interviews. Among the modelling approaches that were explored, the hybrid heteroskedastic Tobit model produced all internally consistent, statistically significant coefficients, and performed best in terms of out-of-sample predictivity for single states. Compared to the existing EQ-5D-5L crosswalk set, the new value set had a higher number of negative values (236 or 7.6% versus 21 or 0.7%). The mean absolute difference was 0.157 and the correlation coefficient between the two sets was 0.879.ConclusionThis study provides a value set for the EQ-5D-5L for T&T using the EQ-VT protocol. We recommend this value set for QALY computations relating to T&T.

Quality of life and quality-adjusted life years after stroke in Sierra Leone.

Stroke is a leading cause of mortality and negatively affects health-related quality of life (HRQoL). HRQoL after stroke is understudied in Africa and there are no reports of quality-adjusted life years after stroke (QALYs) in African countries. We determined the impact of stroke on HRQoL after stroke in Sierra Leone. We calculated QALYs at 1 year post-stroke and determined sociodemographic and clinical variables associated with HRQoL and QALYs in this population. A prospective stroke register was established at the two-principal adult tertiary government hospitals in Freetown, Sierra Leone. Participants were followed up at 7, 90 days, and 1 year post-stroke to capture all-cause mortality and EQ-5D-3L data. QALYs were calculated at the patient level using EQ-5D-3L utility values and survival data from the register, following the area under the curve method. Utilities were based on the UK and Zimbabwe (as a sensitivity analysis) EQ-5D value sets, as there is no Sierra Leonean or West African value set. Explanatory models were developed based on previous literature to assess variables associated with HRQoL and QALYs at 1 year after stroke. To address missing values, Multiple Imputation by Chained Equations (MICE), with linear and logistic regression models for continuous and binary variables, respectively, were used. EQ-5D-3L data were available for 373/460 (81.1%), 360/367 (98.1%), and 299/308 (97.1%) participants at 7, 90 days, and 1 year after stroke. For stroke survivors, median EQ-5D-3L utility increased from 0.20 (95% CI: -0.16 to 0.59) at 7 days post-stroke to 0.76 (0.47 to 1.0) at 90 days and remained stable at 1 year 0.76 (0.49 to 1.0). Mean QALYs at 1 year after stroke were 0.28 (SD: 0.35) and closely associated with stroke severity. Older age, lower educational attainment, patients with subarachnoid hemorrhage and undetermined stroke types all had lower QALYs and lower HRQoL, while being the primary breadwinner was associated with higher HRQoL. Sensitivity analysis with the Zimbabwe value set did not significantly change regression results but did influence the absolute values with Zimbabwe utility values being higher, with fewer utility values less than 0. We generated QALYs after stroke for the first time in an African country. QALYs were significantly lower than studies from outside Africa, partially explained by the high mortality rate in our cohort. Further research is needed to develop appropriate value sets for West African countries and to examine QALYs lost due to stroke over longer time periods. The Stroke in Sierra Leone anonymized dataset is available on request to researchers, see data access section.

To what extent does the use of crosswalks instead of EQ-5D value sets impact reimbursement decisions?: a simulation study

PurposeInconsistent results have been found on the impact of using crosswalks versus EQ-5D value sets on reimbursement decisions. We sought to further investigate this issue in a simulation study.MethodsTrial-based economic evaluation data were simulated for different conditions (depression, low back pain, osteoarthritis, cancer), severity levels (mild, moderate, severe), and effect sizes (small, medium, large). For all 36 scenarios, utilities were calculated using 3L and 5L value sets and crosswalks (3L to 5L and 5L to 3L crosswalks) for the Netherlands, the United States, and Japan. Utilities, quality-adjusted life years (QALYs), incremental QALYs, incremental cost-effectiveness ratios (ICERs), and probabilities of cost-effectiveness (pCE) obtained from values sets and crosswalks were compared.ResultsDifferences between value sets and crosswalks ranged from −0.33 to 0.13 for utilities, from −0.18 to 0.13 for QALYs, and from −0.01 to 0.08 for incremental QALYs, resulting in different ICERs. For small effect sizes, at a willingness-to-pay of €20,000/QALY, the largest pCE difference was found for moderate cancer between the Japanese 5L value set and 5L to 3L crosswalk (difference = 0.63). For medium effect sizes, the largest difference was found for mild cancer between the Japanese 3L value set and 3L to 5L crosswalk (difference = 0.06). For large effect sizes, the largest difference was found for mild osteoarthritis between the Japanese 3L value set and 3L to 5L crosswalk (difference = 0.08).ConclusionThe use of crosswalks instead of EQ-5D value sets can impact cost–utility outcomes to such an extent that this may influence reimbursement decisions.

Do health preferences differ among Asian populations? A comparison of EQ-5D-5L discrete choice experiments data from 11 Asian studies

IntroductionMany countries have established their own EQ-5D value sets proceeding on the basis that health preferences differ among countries/populations. So far, published studies focused on comparing value set using TTO data. This study aims to compare the health preferences among 11 Asian populations using the DCE data collected in their EQ-5D-5L valuation studies.MethodsIn the EQ-VT protocol, 196 pairs of EQ-5D-5L health states were valued by a general population sample using DCE method for all studies. DCE data were obtained from the study PI. To understand how the health preferences are different/similar with each other, the following analyses were done: (1) the statistical difference between the coefficients; (2) the relative importance of the five EQ-5D dimensions; (3) the relative importance of the response levels.ResultsThe number of statistically differed coefficients between two studies ranged from 2 to 16 (mean: 9.3), out of 20 main effects coefficients. For the relative importance, there is not a universal preference pattern that fits all studies, but with some common characteristics, e.g. mobility is considered the most important; the relative importance of levels are approximately 20% for level 2, 30% for level 3, 70% for level 4 for all studies.DiscussionFollowing a standardized study protocol, there are still considerable differences in the modeling and relative importance results in the EQ-5D-5L DCE data among 11 Asian studies. These findings advocate the use of local value set for calculating health state utility.

Comparison of EQ-5D Values Sets Among South American Countries

ObjectiveSocial health preference sets are necessary for conducting health economic evaluations. Values from other countries are often used when local sets are not available, which may alter the results. We aimed to evaluate the degree of variability of currently available country-specific value sets of the EuroQol EQ-5D instrument from South American countries (SAC). MethodsWe selected EQ-5D value sets from SAC and 2 reference countries. We obtained crosswalk value sets for the countries that use the EQ-5D-3L instrument. We compared the value sets with the Kruskal-Wallis test and then carried out pairwise comparisons with the Sign test. We also assessed correlations among the countries’ value sets using the Spearman test. We calculated the absolute difference across countries for each health state, considering a difference of greater than 0.05 relevant. ResultsThe range of value sets varied greatly. The Peruvian value set had the widest range (1 to −1.076) and the lowest values (median: 0.055; interquartile range: −0.171 to 0.275). The Ecuadorian set had the highest values (median: 0.587; interquartile range: 0.443-0.704). The Peruvian value set also had the greatest proportion of health states (43.6%) with a negative value, and the Uruguayan set had the smallest proportion (0.9%). Differences among countries were significant in all cases, with the greatest difference between Ecuador and Peru (median difference: 0.495; 95% confidence interval: 0.515–0.528). ConclusionSocial health preference sets varied greatly among SAC. Using non-local values could distort resource allocation decisions; hence, we recommend that countries obtain and use local value sets.

PMS67 EXPERIENCE AND PREFERENCE-BASED EQ-5D-3L VALUE SET DERIVED FROM LONGITUDINAL DATA: RESULTS FROM THE INTERNATIONAL COSTS AND UTILITIES OF OSTEOPOROTIC FRACTURES STUDY (ICUROS)

Own health state utility values (HSUV, “experience-based utility”) is important for economic valuation and other purposes. There is only one preference-based EQ-5D value set derived from direct patient experience. Furthermore, the impact of interactions between EQ-5D dimensions on HSUV is poorly understood. Therefore we constructed a value set using direct patient experience and explored the impact of interactions between health states. The ICUROS was a multinational prospective observational study on the consequences of fragility fracture. In the study, patients who sustained a fragility fracture completed EQ-5D-3L and Time-trade-off (TTO) questionnaires for five time points: before fracture (recall), within two weeks after fracture, and at 4, 12 and 18 months after fracture. We derived a value set by fitting a random intercept linear model with TTO as the dependent variable and ‘moderate’ (Level 2, “L2”) or ‘severe’ impairment (Level 3, “L3”) in each EQ-5D dimension as dummy variables. We also explored the potential impact of interactions between impairment in dimensions using rigorous Lasso regression with cluster robust standard errors. In total 4,873 patients reported 17,779 EQ-5D and TTO pairs. Mean (SD) age at study enrolment was 71 (11) years and 81% were women. In the random intercept model, all coefficients had the expected sign and were statistically significant (p<0.001). The resulting value set was: 0.92 - 0.03xMobillityL2 - 0.08xMobillityL3 - 0.12xSelf-careL2 - 0.14xSelf-careL3 - 0.02xUsual ActivitiesL2 - 0.03xActivitiesL3 - 0.02xPain/DiscomfortL2 - 0.10xPain/DiscomfortL3 - 0.05xAnxiety/DepressionL2 ´- 0.11xAnxiety/DepressionL3. The Lasso regression indicated that interactions between mobility and self-care, anxiety/depression and self-care, and anxiety/depression and usual activities may be important. This experience-based EQ-5D-3L value set had consistently ordered and statistically significant coefficients, demonstrating face validity. Furthermore, the results show that explicit modelling of interactions may improve the predictive power of EQ-5D value sets. Compared to value sets based on hypothetical health, the decrements associated with impaired health were generally smaller.

Reducing Uncertainty in EQ-5D Value Sets: The Role of Spatial Correlation.

Scoring algorithms for the EQ-5D-3L are constructed subject to a large degree of uncertainty (a credible interval width of 0.152, which is significant in comparison to the reported minimal important differences). The purpose of this work is to explore modeling techniques that will reduce the extent of this uncertainty. We used the US valuation study data. A Bayesian approach was used to calculate predicted utilities and credible intervals. A spatial Gaussian correlation structure was used to model correlation among health states (HS), thus allowing directly valued HS to contribute to the predicted utility of nearby unvalued HS. Leave-one-out cross-validation was used to compare model performances. The average posterior standard deviation was 0.039 for the unvalued health states and 0.011 for the valued health states. Using cross-validation, the US D1 model had 31% coverage probability. Models with independent and Gaussian correlation had coverage probabilities of 95% and 93%, respectively. Moreover, the Gaussian correlation structure resulted in a 25.6% reduction in mean squared error (SE) and 13.2% reduction in mean absolute error (AE) compared to the independent correlation structure (mean SE of 0.00131 v. 0.00176 and mean AE of 0.02818 v. 0.03248). Uncertainty was substantially lower for the directly valued HS compared to unvalued HS, suggesting direct valuation of as many health states as possible. Incorporation of a spatial correlation significantly reduced uncertainty. Hence, we suggest incorporating this when constructing scoring algorithms.

Patient Experience-based Value Sets: Are They Stable?

Although societal preference weights are desirable to inform resource-allocation decision-making, patient experienced health state-based value sets can be useful for clinical decision-making, but context may matter. To estimate EQ-5D value sets using visual analog scale (VAS) ratings for patients undergoing knee replacement surgery and compare the estimates before and after surgery. We used the Patient Reported Outcome Measures data collected by the UK National Health Service on patients undergoing knee replacement from 2009 to 2012. Generalized least squares regression models were used to derive value sets based on the EQ-5D-3 level using a development sample before and after surgery, and model performance was examined using a validation sample. A total of 90,450 preoperative and postoperative valuations were included. For preoperative valuations, the largest decrement in VAS values was associated with the dimension of anxiety/depression, followed by self-care, mobility, usual activities, and pain/discomfort. However, pain/discomfort had a greater impact on VAS value decrement in postoperative valuations. Compared with preoperative health problems, postsurgical health problems were associated with larger value decrements, with significant differences in several levels and dimensions, including level 2 of mobility, level 2/3 of usual activities, level 3 of pain/discomfort, and level 3 of anxiety/depression. Similar results were observed across subgroups stratified by age and sex. Findings suggest patient experience-based value sets are not stable (ie, context such as timing matters). However, the knowledge that lower values are assigned to health states postsurgery compared with presurgery may be useful for the patient-doctor decision-making process.

Chinese Version of the EQ-5D Preference Weights: Applicability in a Chinese General Population.

ObjectivesThis study aimed to test the reliability, validity and sensitivity of Chinese version of the EQ-5D preference weights in Chinese general people, examine the differences between the China value set and the UK, Japan and Korea value sets, and provide methods for evaluating and comparing the EQ-5D value sets of different countries.MethodsA random sample of 2984 community residents (15 years or older) were interviewed using a questionnaire including the EQ-5D scale. Level of agreement, convergent validity, known-groups validity and sensitivity of the EQ-5D China, United Kingdom (UK), Japan and Korea value sets were determined.ResultsThe mean EQ-5D index scores were significantly (P<0.05) different among the UK (0.964), Japan (0.981), Korea (0.987), and China (0.985) weights. High level of agreement (intraclass correlations coefficients > 0.75) and convergent validity (Pearson’s correlation coefficients > 0.95) were found between each paired schemes. The EQ-5D index scores discriminated equally well for the four versions between levels of 10 known-groups (P< 0.05). The effect size and the relative efficiency statistics showed that the China weights had better sensitivity.ConclusionsThe China EQ-5D preference weights show equivalent psychometric properties with those from the UK, Japan and Korea weights while slightly more sensitive to known group differences than those from the Japan and Korea weights. Considering both psychometric and sociocultural issues, the China scheme should be a priority as an EQ-5D based measure of the health related quality of life in Chinese general population.

Quantifying Parameter Uncertainty in EQ-5D-3L Value Sets and Its Impact on Studies That Use the EQ-5D-3L to Measure Health Utility: A Bayesian Approach.

. Parameter uncertainty in EQ-5D value sets is routinely ignored. Sources of parameter uncertainty include uncertainty in the estimated regression coefficients of the scoring algorithm and uncertainty that arises from the need to use a nonsaturated functional form when creating the scoring algorithm. We hypothesize that this latter source is the major contributor to parameter uncertainty in the value sets. . We used data from the United States EQ-5D-3L valuation study to assess the extent of parameter uncertainty in the value set. We refitted the US scoring algorithm to quantify contributors to the mean square prediction errors and used a Bayesian approach to estimate the predictive distribution of the mean utilities. The impact of parameter uncertainty in the value set was assessed using survey data. . Parameter uncertainty in the estimated regression coefficients explained 16% of the mean squared prediction error; uncertainty in the functional form explained the remaining 84%. The median width of the 95% credible intervals for the mean utilities was 0.15. In estimating mean utility in our survey population, parameter uncertainty in the value set was responsible for 93% of the total variance, with sampling variation in the survey population being responsible for the remaining 7%. . EQ-5D-3L value sets are estimated subject to considerable parameter uncertainty; the median credible interval width is large compared with reported values of the minimum important difference for the EQ-5D-3L, which have been reported to be as small as 0.03. Other countries' scoring algorithms are based on smaller studies and are hence subject to greater uncertainty. This uncertainty should be accounted for when using EQ-5D health utilities in economic evaluations.

Mapping the Clinical Chronic Obstructive Pulmonary Disease Questionnaire onto Generic Preference-Based EQ-5D Values

ObjectivesTo develop a model to predict EuroQol five-dimensional questionnaire (EQ-5D) values from clinical chronic obstructive pulmonary disease (COPD) questionnaire (CCQ) scores. MethodsWe used data from three clinical trials (the Randomized Clinical Trial on Effectiveness of Integrated COPD Management in Primary Care [RECODE], the Assessment Of Going Home Under Early Assisted Discharge [GO-AHEAD], and the Health Status Guided COPD Care [MARCH]). Data were randomly split into an estimation sample and a validation sample. The conceptual similarity between patient-reported CCQ and preference-based EQ-5D scores was assessed using correlation and principal-component analysis. Different types of models were estimated with increasing complexity. We selected the final models on the basis of mean absolute error and root mean square error when comparing predicted and observed values from the same population (internal validity) and from different trial populations (external validity). We also developed models for different country-specific EQ-5D value sets. ResultsThe principal-component analysis showed that the CCQ domains functional state and mental state are associated with four dimensions of the EQ-5D. The EQ-5D dimension pain/discomfort formed a separate construct on which no CCQ item loaded. The mean observed EQ-5D values were not significantly different from the mean predicted EQ-5D values in internal validation samples but did significantly differ in external validation samples. The models underestimated EQ-5D values in milder health states and overestimated them in more severe health states. The predictive ability of the models was similar across different EQ-5D value sets. ConclusionsThe models can predict mean EQ-5D values that are similar to observed mean values in a similar population. The overestimating/underestimating of the low/high EQ-5D values, however, limits its use in Markov models. Therefore, mapping should be used cautiously.

Impact of Transformation of Negative Values and Regression Models on Differences Between the UK and US EQ-5D Time Trade-Off Value Sets

National EQ-5D value sets are developed because preferences for health may vary in different populations. UK values are lower than US values for most of the 243 possible EQ-5D health states. Although similar protocols were used for data collection, analytic choices regarding how to model values from the collected data may also influence national value sets. Participants in the UK and US studies assessed the same subset of 42 EQ-5D health states using the time trade-off (TTO) method. However, different methods were used to transform negative values to a range bounded by 0 and -1, and values for all 243 health states were estimated using two different regression models. The transformation of negative values is inconsistent with expected utility theory, and the choice of which transformation method to use lacks a theoretical foundation. Our objectives were to assess how much of the observed difference between the UK and US EQ-5D value sets may be explained by the choice of transformation method for negative values relative to the choice of regression model and the differences between elicited TTO values in the respective national studies (datasets). We applied both transformation methods and both regression models to each of the two datasets, resulting in eight comparable value sets. We arranged these value sets in pairs in which one source of difference (transformation method, regression model or dataset) was varied. For each of these paired value sets, we calculated the mean difference between the two matching values for each of the 243 health states. Finally, we calculated the mean utility gain for all possible transitions between pairs of EQ-5D health states within each value set and used the difference in transition scores as a measure of impact from changing transformation method, regression model or dataset. The mean absolute difference in values was 1.5 times larger when changing the transformation method than when using different datasets. The choice of transformation method had a 3.2 times larger effect on the mean health gain (transition score) than the choice of dataset. The mean health gain in the UK value set was 0.09 higher than in the US value set. Using the UK transformation method on the US dataset reduced this absolute difference to 0.02. The choice of regression model had little overall impact on the differences between the value sets. Most of the observed differences between the UK and US value sets were caused by the use of different transformation methods for negative values, rather than differences between the two study populations as reflected in the datasets. Changing the regression model had little impact on the differences between the value sets.

The EQ-5D health states value set for Slovenia

Background: Among other uses, the subjective valuation of health care is also used in economic analyses, through which we are trying to determine the relative cost effectiveness of health technologies. Since cost effectiveness studies are very demanding and take a relatively long time to give results, the parallel estimation of the values of health states would represent an additional and, above all, unnecessary burden. The values of health states are therefore calculated in a separate study. The primary objective of the study is the derivation of a value set for the EQ-5D defined health states for Slovenia while controlling contextual bias. Methods: According to our knowledge, spatial econometrics has never before been used for the purpose of estimating the EQ-5D valuation set. A value set for the EQ-5D health states for Slovenia is presented, based on data from research carried out in 2005. For calculating the value set, a spatial econometric estimator was chosen since it gave better results than the ordinary least squares (OLS) method. To control for contextual bias, a spatial variable was included in the model. Contextual bias has not been excluded from any of the calculated EQ-5D value sets. Results: The EQ-5D defined value set for Slovenia is logically consistent. The defined model has a very good fit. The comparison of models with and without the spatial variable showed that controlling for contextual bias by including the spatial variable improves the fit of the model and produces slightly better results. Conclusions: Due to the good fit of model and logically consistent valuations, the value set can be used in healthrelated economic and population analyses. Special attention is given to the exclusion of a contextual bias from the model to improve the fit of the model and change the priority settings.

Preliminary findings of an investigation into the relationship between national culture and EQ-5D value sets

Despite the growing importance of the EQ-5D descriptive system as a basis for the valuation of QALYs in cost-utility analysis, for most countries, there are no EQ-5D value sets. Researchers and policy makers wishing to use the EQ-5D descriptive system in a country for which there is no value set are advised to use one from a nearby or 'similar' population. Factors other than geographic proximity can affect the relative values of EQ-5D states. This study explores the links between national culture and EQ-5D value sets. Rank correlation analysis is used to explore relationships between the relative values of a set of EQ-5D states and dimensions of national culture. The latter are taken from Hofstede's framework which operationalizes national culture in five dimensions. For the data currently available (countries for which EQ-5D value sets and scores on dimensions of culture both exist), moderate and strong correlations were found between the culture dimension of power distance and individualism and the EQ-5D dimensions of pain/discomfort and anxiety/depression. Moderate correlations were also observed between the cultural dimension of masculinity and the EQ-5D dimensions of self care, usual activities and pain/discomfort. Uncertainty avoidance correlates with the EQ-5D dimension of anxiety/depression. The correlation patterns observed are generally consistent with a priori expectations based on the nature of the dimensions of culture and the EQ-5D model. This analysis demonstrates the potential of national culture in providing insight into the drivers of the relative values of EQ-5D dimensions for different countries and in informing decisions about which EQ-5D value sets to use in situations where one does not exist.

Utilities of the EQ-5D

Within the framework of economic evaluations, the transferability of utility scores between jurisdictions remains unclear. The EQ-5D is a generic instrument for measuring health-related quality of life in economic evaluations, which can be used for comparing utility scores across countries. At present, the EQ-5D has several national value sets or tariffs. Nevertheless, utility estimates from foreign studies are often used directly for cost-effectiveness estimates, without adapting by applying the appropriate national value set. It is unclear if this practice is advisable, due to dissimilarities between the national value sets. To examine the effects of differences in national EQ-5D value sets on absolute and marginal utilities of health states, and determine to what degree these differences can be explained by methodological factors. First, the relative importance of the EQ-5D domains for the utility estimates was compared across the 15 value sets. Second, two hypothetical health states for a depressed patient and a pain patient (21232 and 33321) were selected for additional analysis, by comparing the utilities as scored by the value sets. The marginal influence of a one-level deterioration in a domain of these health states on the utility estimate was then determined. Third, the differences between the value sets were examined in more detail by using multilevel analysis to examine the role of methodological differences in the valuation studies. Differences can be perceived between the national value sets of the EQ-5D in the preferences for the domains. The utilities of the two hypothetical health states show that the value sets differ substantially. Furthermore, the differences between the marginal values of the deteriorations are large, which can be explained partly by the type of valuation method. Other methodological differences also influence the value sets. All results indicate that the differences between the EQ-5D value sets are considerable and should not be ignored. The differences can largely be explained by methodological differences in the valuation studies. The remaining differences may reflect cultural dissimilarities between countries. Therefore, further research should focus on investigating the transferability of utilities across countries or agreeing on a standard to perform valuation studies. For the time being, transferring utilities from one country to another without any adjustment is not advisable.

Präferenzbasierte Lebensqualitätsmessung: Der EQ-5D Fragebogen

The EQ-5D is a standardised preference-based instrument to measure health-related quality of life (HRQoL). It provides a simple descriptive profile and a one-dimensional index score for health status. Designed for self-completion by respondents, the EQ-5D can be used in population health surveys, clinical studies and economic evaluation of health care. The EQ-5D descriptive system consists of five dimensions (mobility, self-care, usual activities, pain/discomfort and anxiety/depression), each of which comprises three levels (no problems, some or moderate problems, extreme problems or unable). This information can be used as an EQ-5D health profile for individuals or groups. Moreover, the descriptive information can be converted into a one-dimensional index score, using scoring functions based on value sets elicited from general population samples. Sets of values are available for several countries, including the United Kingdom, the United States and Germany. A comparison of different value sets suggests that values are related to cultural differences. Thus, where EQ-5D value sets for the relevant population exist then this set should be used index scores for use in national health-economic evaluation studies this country-specific value sets can be applied to provide EQ-5D preference weights for use in health-economic evaluation studies. Recently, the EuroQol Group derived a European value set, based on data collected in six Western European countries (Finland, Germany, The Netherlands, Spain, Sweden and the UK). Scores based on the European scoring function can be used in multi-national studies.

The Transformation of Utilities for Health States Worse Than Death

Utilities for health are measured on an interval scale, where 1 refers to full health and 0 refers to death. No theoretical lower boundary on the utilities for states worse than death exists. As a consequence, negative values receive greater weight in the calculation of mean utilities. To avoid this, negative values often are bound at -1. The objective of this study was to compare the effect of 3 methods to bound negative values at -1 on the estimation of EQ-5D value sets: truncation, monotonic, and linear transformation. Data of the Dutch EQ-5D valuation study were used. A total of 298 respondents directly valued 17 EQ-5D health states using the time trade-off (TTO) method. Random effects regression analysis was used to interpolate TTO values for all possible EQ-5D states. In the regression analysis the dependent variable is 1 minus the TTO value and the independent variables describe the health state. Two widely used models to estimate EQ-5D value were applied after truncation of negative values and monotonic and linear transformation of negative values. Both models also were estimated on medians. Truncation of negative values gave the largest mean absolute error (MAE); the linear transformation resulted in the smallest MAE. When medians were used for estimation, the MAEs were comparable with the estimation on means. The choice of a method to bound negative values is arbitrary and affects the resulting value set. For the estimation of EQ-5D value sets from a societal perspective the use of medians should be considered.