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Purpose To provide the first Norwegian EQ-5D-5L and EQ VAS population norms for the adult general population. Methods Postal survey of a random sample of 12,790 Norwegians identified through the National Registry of the Norwegian Tax Administration. Norms, weighted for Norwegian general population characteristics, are shown for the five EQ-5D-5L dimensions, EQ-5D index, and EQ VAS scores for seven age categories, females, males, and education level. Results There were 3200 (25.9%) respondents to 12,263 correctly addressed questionnaires. The EQ-5D-5L dimensions, EQ VAS, and background questions were completed by 3120 (24.6%) respondents. The mean age (SD) was 50.9 (21.7) and range was 18–97 years. The youngest age group of 18–29 years and oldest of 80 years and over had the highest (n = 691) and lowest (n = 239) number of respondents, respectively. Compared to the general population, the respondents comprised a greater number of females, younger and older ages, and had a higher education level. 32% of respondents reported no health problems on the EQ-5D-5L. From the youngest to oldest age groups, there was a general decline in health as assessed by the EQ-5D-5L. The exception was for anxiety/depression, where the youngest age groups had the poorest health. Apart from self-care, women reported poorer health than men, as assessed by the EQ-5D-5L; EQ VAS scores were similar for men and women. Higher levels of health (EQ-5D index, EQ VAS scores) were found with increasing levels of education. Conclusion The population norms will improve interpretation of EQ-5D-5L and EQ VAS scores in Norwegian applications including clinical practice, clinical and health services research, and national quality registers where EQ-5D-5L is the most widely used patient-reported instrument.

Purpose To describe the health-related quality of life (HRQoL) of caregivers and survivors of transient ischaemic attack (TIA) and stroke during one year post discharge in comparison to age- and sex-matched population norms; and to analyse the association of initial stroke severity, measured by a routinely used stroke-specific scale, on subsequent HRQoL of caregivers and survivors. Methods Cohort of hospitalized patients with TIA and stroke discharged alive from a large university hospital in Norway, and their informal caregivers. Questionnaires at 3 and 12 months post discharge were filled out by caregivers (n = 320 and n = 326, respectively) and survivors (n = 368 and n = 383, respectively). Multivariable linear regression analyses tested associations between initial stroke severity (National Institutes of Health Stroke Scale, NIHSS) and HRQoL (EQ-5D-3L) in caregivers and survivors. Results Caregivers of survivors with TIA or stroke did not report lower HRQoL than matched norms. There was some evidence of an association of the NIHSS with caregiver HRQoL at 3 months only (age–sex-adjusted coefficient − 0.01, p = 0.008), however, this was attenuated after additional adjustments. Survivors with stroke, but not TIA, reported lower HRQoL than population norms at both time points. There was a negative association between higher NIHSS scores and survivors’ HRQoL; fully adjusted coefficient − 0.01 at both time points (p = 0.001). Conclusion The informal caregivers and survivors with TIA did not report lower than expected HRQoL. Increasing stroke severity was associated with decreasing HRQoL among survivors, but had limited predictive value among caregivers. Other factors may therefore be better indicators of ‘at risk’ caregivers.

Objectives The EQ-5D-5L valuation protocol recommends combining time trade-off (TTO) and discrete choice experiments (DCEs). DCEs that include a duration attribute (DCETTO) allow modeling on the quality-adjusted life-year scale. Because the choice sequence in a TTO can be construed as a series of DCETTO, we used data from a single TTO study to investigate the extent to which DCE values match TTO values when based on identical preferences. Methods In a TTO design in which a fixed set of choices were administered without termination at preference indifference, 202 individuals each valued 10 EQ-5D health states. From identified indifference points, we estimated three sets of TTO values: (i) plotting means and (ii) applying censored regressions at −1 and 1. Using all strict preferences, we (iii) estimated DCETTO values with a logit model and a bootstrap procedure. Results Estimated DCETTO and TTO values agreed well at the severe end of the quality-adjusted life-year scale, but with decreasing severity, DCETTO values were higher than TTO-values, with the difference peaking at 0.37 for the mildest health state. Left-censoring TTO values at −1 worsen the agreement for the worst health states and did not affect health states. Right censoring at 1 improved the agreement for mild states. Conclusions TTO and the DCETTO values estimated from the same preference data diverged, with increasing difference for milder health states. Although the values converged when applying censored regression at +1, we question the validity of this adjustment.

IntroductionNorway is one of several European countries that lacks a national value set and scoring algorithm for the EuroQol five dimensions (EQ-5D). Recent studies have found differences between countries in terms of health values or preferences for health states described by instruments such as the EQ-5D. The project aims to model a national value set for the five level version of the EQ-5D based on values elicited from a representative sample of the Norwegian adult general population in terms of region, age, sex and level of education. Using a sampling strategy supporting the collection of values for both hypothetical and experienced health states, the study will have the additional aim of assessing the feasibility of collecting experience-based values in accordance with the latest EQ-5D valuation study protocol, and comparing values with those given for hypothetical health states.Methods and analysisMultistage random sampling and quota-sampling will contribute to representativeness. To increase the number of valuations of experienced health states, those with less than perfect health will be oversampled, increasing the total number of interviews from 1000 to 1300–1500. The most recent EQ-5D valuation protocol will be followed which includes computer assisted face-to-face, one-to-one interviews and use of composite time trade-off and discrete choice experiments.Ethics and disseminationThe study has been reviewed and found to be outside of the scope of the ethics committee and thus not in need of ethical approval. The study findings will be disseminated through peer-reviewed publications, conference presentations and summaries for key stakeholders and partners in the field. The scoring algorithms will be available for widely used statistical software.

Purpose So far there is no Norwegian value algorithm to inform healthcare decision making. The 15D health state values estimated with the original 15D valuation procedure tend to be higher than the values of other generic preference-based health-related quality of life (HRQoL) instruments. The main purpose of this study was to use a new 15D valuation procedure to estimate Norwegian 15D health state values and to explore their empirical performance. Methods The visual analogue scale was used to collect 15D valuation data in a representative sample of the Norwegian general population. The new procedure used fewer valuation tasks and anchored the 15D health state values in an empirically assessed range. The Norwegian 15D health state values were compared to the values of five HRQoL instruments which were provided by Norwegian residents belonging to seven disease groups and a healthy population. Results The Norwegian 15D health state values ranged from 1 to − 0.52. Compared to 15D health state values estimated with the original procedure, the Norwegian 15D health state values were lower and more in line with values of other HRQoL instruments. Conclusions The new 15D valuation procedure is simpler, links the 15D health state values better to the requirements of the QALY model, and provides an empirically-based range. We recommend using the new valuation procedure in future 15D valuation studies, and the Norwegian health state values for use in 15D-based health economic analyses in Norway. Keywords Health-related quality of life 15D Visual analogue scale Value algorithm NORGES FORSKNINGSRÅD Prosjektkode: 213127 Prosjektkode: 229101

Objectives There are several issues of concern for the composite time trade-off (c-TTO) used to estimate EQ-5D-5L value sets. The “nonstopping” TTO (n-TTO) differs from the c-TTO mainly in 2 aspects: (1) n-TTO uses a standardized top-down or bottom-up routing; and (2) n-TTO continues when indifference is indicated by respondents. In this study, we aimed to test the feasibility of n-TTO and compared it with c-TTO. Methods The study was conducted in parallel in Norway and Singapore. In both countries, members of the general population were invited to complete 10 n-TTO or c-TTO tasks. Acceptability and feasibility of n-TTO were assessed using respondents’ feedback, administration time, and valuation success rate. Characteristics of the TTO data derived from c-TTO and n-TTO tasks were examined at task, individual, as well as group levels. Results In both Norway and Singapore, the success rate of n-TTO tasks was high (> 95%) and the n-TTO tasks took less time to complete than the c-TTO tasks, and there were fewer or smaller “spikes” in the distributions of n-TTO data compared to c-TTO data. In Norway, the individual-level n-TTO and c-TTO data had similar distribution and logical consistency profiles. In Singapore, n-TTO was inferior to c-TTO in logical consistency; however, a similar degree of nonmonotonicity was observed in modeling of n-TTO and c-TTO data. Conclusions The noniterative n-TTO appeared to be feasible and may generate data with better distribution in shorter time than c-TTO. Further research is needed to assess this new health-state valuation method.

Purpose Little is known about estimating utilities for comorbid (or ‘joint’) health states. Several joint health state prediction models have been suggested (for example, additive, multiplicative, best-of-pair, worst-of-pair, etc.), but no general consensus has been reached. The purpose of the study is to explore the relationship between health-related quality of life (HRQoL) and increasing numbers of diagnoses. Methods We analyzed a large dataset containing respondents’ ICD-9 diagnoses and preference-based HRQoL (EQ-5D and SF-6D). Data were stratified by the number of diagnoses, and mean HRQoL values were estimated. Several adjustments, accounting for the respondents’ age, sex, and the severity of the diagnoses, were carried out. Our analysis fitted additive and multiplicative models to the data and assessed model fit using multiple standard model selection methods. Results A total of 39,817 respondents were included in the analyses. Average HRQoL values were represented well by both linear and multiplicative models. Although results across all analyses were similar, adjusting for severity of diagnoses, age, and sex strengthened the linear model’s performance measures relative to the multiplicative model. Adjusted R2 values were above 0.99 for all analyses (i.e., all adjusted analyses, for both HRQoL instruments), indicating a robust result. Conclusions Additive and multiplicative models perform equally well within our analyses. A practical implication of our findings, based on the presumption that a linear model is simpler than an additive model, is that an additive model should be preferred unless there is compelling evidence to the contrary.

Background The EQ-5D-3 L instrument is a standardized questionnaire which was developed as a simple, generic measure of health for clinical and economic appraisal. To aid in the interpretation, scores are often compared with a normative group. The objectives of this study were 1) to provide population norms for the EQ-5D-3 L for Norway, and 2) to compare scores from postal and web surveys. Methods We conducted two surveys in samples that were aimed to be representative of the Norwegian general population: 1) a postal survey (n = 5000) and 2) a panel study with electronic data collection (n = 1936). For scoring the EQ-5D Index, we used the UK tariff. EQ-5D items were compared using multivariable ordinal logistic regression analysis and EQ-5D Index and EQ VAS scores using multivariable linear regression, adjusting for age, sex and education. Results In total 1131 (22.6%) responded to the postal survey and 977 (50.5%) to the web survey. The odds ratio (OR) for being in a higher score category on the Pain/Discomfort scale in the web survey was 1.25 (95%CI 1.04 to 1.50, p = 0.019) relative to the postal survey. The odds were similar in the other four dimensions. The EQ-5D Index and EQ VAS scores were similar in the postal and web surveys in the various strata according to age, sex and education, except for lower unadjusted and adjusted score for web respondents aged 41–50 years and for those with higher education (≥14 years) than postal respondents. Conclusions The distribution of scores for the EQ-5D descriptive system and its derived utility scores were rather similar in a postal survey and a panel web survey. Hence, these values were combined into a norm set for Norway.

BACKGROUND Variations in birth frequencies have an impact on activity planning in maternity wards. Previous studies of this phenomenon have commonly included elective births. A Danish study of spontaneous births found that birth frequencies were well modelled by a Poisson process. Somewhat unexpectedly, there were also weekly variations in the frequency of spontaneous births. Another study claimed that birth frequencies follow the Benford distribution. Our objective was to test these results. MATERIAL AND METHOD We analysed 50,017 spontaneous births at Akershus University Hospital in the period 1999-2014. To investigate the Poisson distribution of these births, we plotted their variance over a sliding average. We specified various Poisson regression models, with the number of births on a given day as the outcome variable. The explanatory variables included various combinations of years, months, days of the week and the digit sum of the date. RESULTS The relationship between the variance and the average fits well with an underlying Poisson process. A Benford distribution was disproved by a goodness-of-fit test (p < 0.01). The fundamental model with year and month as explanatory variables is significantly improved (p < 0.001) by adding day of the week as an explanatory variable. Altogether 7.5% more children are born on Tuesdays than on Sundays. The digit sum of the date is non-significant as an explanatory variable (p = 0.23), nor does it increase the explained variance. INERPRETATION: Spontaneous births are well modelled by a time-dependent Poisson process when monthly and day-of-the-week variation is included. The frequency is highest in summer towards June and July, Friday and Tuesday stand out as particularly busy days, and the activity level is at its lowest during weekends.

Background The 15D is a generic preference-based health-related quality-of-life instrument developed in Finland. Values for the 15D instrument are estimated by combining responses to three distinct valuation tasks. The impact of how these tasks are combined is relatively unexplored. Objectives To compare 15D valuation studies conducted in Norway and Finland in terms of scores assigned in the valuation tasks and resulting value algorithms, and to discuss the contributions of each task and the algorithm estimation procedure to observed differences. Methods Norwegian and Finnish scores from the three valuation tasks were compared using independent samples t tests and Lin concordance correlation coefficients. Covariance between tasks was assessed using Pearson product-moment correlations. Norwegian and Finnish value algorithms were compared using concordance correlation coefficients, total ranges, and ranges for individual dimensions. Observed differences were assessed using minimal important difference. Results Mean scores in the main valuation task were strikingly similar between the two countries, whereas the final value algorithms were less similar. The largest differences between Norway and Finland were observed for depression, vision, and mental function. Conclusions 15D algorithms are a product of combining scores from three valuation tasks by use of methods involving multiplication. This procedure used to combine scores from the three tasks by multiplication serves to amplify variance from each task. From relatively similar responses in Norway and Finland, diverging value algorithms are created. We propose to simplify the 15D algorithm estimation procedure by using only one of the valuation tasks.

Background The conventional method for modeling of the five-level EuroQol five-dimensional questionnaire (EQ-5D-5L) health state values in national valuation studies is an additive 20-parameter main-effects regression model. Statistical models with many parameters are at increased risk of overfitting—fitting to noise and measurement error, rather than the underlying relationship. Objectives To compare the 20-parameter main-effects model to simplified, nonlinear, multiplicative regression models in terms of how accurately they predict mean values of out-of-sample health states. Methods We used data from the Spanish, Singaporean, and Chinese EQ-5D-5L valuation studies. Four models were compared: an 8-parameter model with single parameter per dimension, multiplied by cross-dimensional parameters for levels 2, 3, and 4; 9- and 11-parameter extensions with handling of differences in the wording of level 5; and the "standard" additive 20-parameter model. Fixed- and random-intercept variants of all models were tested using two cross-validation methods: leave-one-out at the level of valued health states, and of health state blocks used in EQ-5D-5L valuation studies. Mean absolute error, Lin concordance correlation coefficient, and Pearson R between observed health state means and out-of-sample predictions were compared. Results Predictive accuracy was generally best using random intercepts. The 8-, 9-, and 11-parameter models outperformed the 20-parameter model in predicting out-of-sample health states. Conclusions Simplified nonlinear regression models look promising and should be investigated further using other EQ-5D-5L data sets. To reduce the risk of overfitting, cross-validation is recommended to inform model selection in future EQ-5D valuation studies.

Purpose EQ-5D tariffs are typically based on general population valuations studies, but whether valuations of experienced health (EH) or hypothetical health (HH) are more appropriate is disputed. Previous comparisons of valuations of EH and HH have focused on absolute differences in dimension-specific regression coefficients. We examined differences in the relative importance attributed to the EQ-5D dimensions between EH and HH valuations of EQ-5D states in the United States. Methods We used the regression model from the US EQ-5D valuation study on EH ratings from the 2000-2003 Medical Expenditure Panel Survey and on HH ratings from the US EQ-5D valuation study conducted in 2001. We then compared patterns in the relative magnitudes of coefficients that corresponded to the five dimensions. Results In the HH model, self-care and pain/discomfort were the most important dimensions, while usual activities were the least important. In the EH model, usual activities were the most important dimension, while self-care was one of the least important. Discussion The findings reveal considerable differences between stated preferences for HH and ratings of EH, particularly for self-care and usual activities. The findings accentuate the importance of the debate about which groups' values should be used in medical priority setting.

Background: In health economic analyses, health states are typically valued using instruments with few items per dimension. Due to the generic (and often reductionist) nature of such instruments, certain groups of respondents may experience challenges in describing their health state. This study is concerned with generic, preference-based health state instruments that provide information for decisions about the allocation of resources in health care. Unlike physical measurement instruments, preference-based health state instruments provide health state values that are dependent on how respondents interpret the items. This study investigates how individuals with spinal cord injury (SCI) interpret mobility-related items contained within six preference-based health state instruments. Methods: Secondary analysis of focus group transcripts originally collected in Vancouver, Canada, explored individuals’ perceptions and interpretations of mobility-related items contained within the 15D, Assessment of Quality of Life 8-dimension (AQoL-8D), EQ-5D-5L, Health Utilities Index (HUI), Quality of Well-Being Scale Self-Administered (QWB-SA), and the 36-item Short Form health survey version 2 (SF-36v2). Ritchie and Spencer’s ‘Framework Approach’ was used to perform thematic analysis that focused on participants’ comments concerning the mobility-related items only. Results: Fifteen individuals participated in three focus groups (five per focus group). Four themes emerged: wording of mobility (e.g., ‘getting around’ vs ‘walking’), reference to aids and appliances, lack of suitable response options, and reframing of items (e.g., replacing ‘walking’ with ‘wheeling’). These themes reflected item features that respondents perceived as relevant in enabling them to describe their mobility, and response strategies that respondents could use when faced with inaccessible items. Conclusion: Investigating perceptions to mobility-related items within the context of SCI highlights substantial variation in item interpretation across six preference-based health state instruments. Studying respondents’ interpretations of items can help to understand discrepancies in the health state descriptions and values obtained from different instruments. This line of research warrants closer attention in the health economics and quality of life literature.

Purpose Little is known about estimating utilities for comorbid (or ‘joint’) health states. Several joint health state prediction models have been suggested (for example, additive, multiplicative, best-of-pair, worst-of-pair, etc.), but no general consensus has been reached. The purpose of the study is to explore the relationship between health-related quality of life (HRQoL) and increasing numbers of diagnoses.

Background:Background: 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. Abstract: Objectives:Objectives: 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). Abstract: Methods:Methods: 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. Abstract: Results:Results: 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. Abstract: Conclusions:Conclusions: 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.

Health state values are by convention anchored to ‘perfect health’ and ‘death.’ Attitudes toward death may consequently influence the valuations. We used attitudes toward euthanasia (ATE) as a sub-construct for attitudes toward death. We compared the influence on values elicited with time trade-off (TTO), lead-time TTO (LT-TTO) and visual analogue scale (VAS).Since the ‘death’ anchor is most explicit in TTO, we hypothesized that TTO values would be most influenced by ATE. Respondents valued eight EQ-5D health states with VAS, then TTO (n = 328) or LT-TTO (n = 484). We measured ATE on a scale from −2 (fully disagree) to 2 (fully agree) and used multiple linear regressions to predict VAS, TTO, and LT-TTO values by ATE, sex, age, and education. A one-point increase on the ATE scale predicted a mean TTO value change of −.113 and LT-TTO change of −.072. Demographic variables, but not ATE, predicted VAS values. TTO appears to measure ATE in addition to preferences for health states. Different ways of incorporating death in the valuation may impact substantially on the resulting values. ‘Death’ is a metaphysically unknown concept, and implications of attitudes toward death should be investigated further to evaluate the appropriateness of using ‘death’ as an anchor.

Background EQ-5D valuation studies are usually performed using the time tradeoff (TTO) method, which is costly and time consuming. We focused on 2 properties that particularly characterize TTO: the initial choice task categorizing health states as better than death (BTD), worse than death (WTD), or equal to death (ETD), and unwillingness to trade (UTT) lifetime to improve health. The aim of this study was to estimate the value of the information to be gained from continuing the conventional TTO tasks beyond the initial question and the extent to which mean-based EQ-5D tariff values could be predicted through a simplified method of categorizing health states into BTD, WTD, ETD, and UTT. Methods We used data from the UK EQ-5D valuation study ( n = 2997). We designed an abbreviated system with only 4 values (collapsed TTO [cTTO]) based on the 4 response categories and assigned values as follows: WTD = −.5, ETD = 0, BTD = .5, and UTT = 1. Based on the mean cTTO scores for the valued health states, we created a regression-based cTTO tariff, which was compared with the conventional (full) TTO tariff (fTTO) by regressing 1) the fTTO means on cTTO means and 2) the fTTO tariff on the cTTO tariff. Results WTD values were unrelated to health state severity. Correlation between the means of fTTO and means of cTTO was >.999, and tariff values from fTTO correlated with tariff values from cTTO at r > .999. Conclusions Once respondents have classified health states as UTT, BTD, ETD, or WTD, the TTO procedure adds little further information to the tariff values. The WTD task fails to discriminate between good and bad health states. TTO valuation could likely be simplified using cTTO.

ObjectivesIn EuroQol five-dimensional questionnaire valuation studies, each participant typically assesses more than 10 hypothetical health states by using the time trade-off (TTO) method. We wanted to explore potential learning effects when using the TTO method, that is, whether the valuations were affected by the number of previously rated health states (the sequence number).MethodsWe included 3773 respondents from the US EQ-5D valuation study, each of whom valued 12 health states (plus unconscious) in random order. With linear regression, we used sequence number to predict mean and standard deviations across all health states. We repeated the analysis separately for TTO responses indicating a state better than death and a state worse than death. Each TTO value requires a specific number of choice iterations. To test whether respondents used fewer iterations with experience, we used linear regression with sequence number as the independent variable and number of iterations as the dependent variable.ResultsMean TTO values were fairly stable across the sequence number, but analyzing state better than death and state worse than death values separately revealed a tendency toward more extreme values: state better than death values increased by 0.02, while state worse than death values decreased by 0.21 (P < 0.0001) over the full sequence. The standard deviations increased slightly, while the number of choice iterations was the same over the sequence number. The findings were stable across the levels of health state severity, age, and sex.ConclusionsTTO values become more extreme with increasing experience. Because of the randomized valuation order, these effects do not bias specific health states; however, they reduce the overall validity and reliability of TTO values.

Purpose The de facto standard method for valuing EQ-5D health states is the time trade-off (TTO), an iterative choice procedure. The TTO requires a starting point (SP), an initial offer of time in full health which is compared to a fixed offer of time in impaired health. From the SP, the time in full health is manipulated until preferential indifference. The SP is arbitrary, but may influence respondents, an effect known as anchoring bias. The aim of the study was to explore the potential anchoring effect and its magnitude in TTO experiments. Methods A total of 1249 respondents valued 8 EQ-5D health states in a Web study. We used the lead time TTO (LT-TTO) which allows eliciting negative and positive values with a uniform method. Respondents were randomized to 11 different SPs. Anchoring bias was assessed using OLS regression with SP as the independent variable. In a secondary experiment, we compared two different SPs in the UK EQ-5D valuation study TTO protocol. Results A 1-year increase in the SP, corresponding to an increase in TTO value of 0.1, resulted in 0.02 higher recorded LT-TTO value. SP had little impact on the relative distance and ordering of the eight health states. Results were similar to the secondary experiment. Conclusion The anchoring effect may bias TTO values. In this Web-based valuation study, the observed anchoring effect was substantial. Further studies are needed to determine whether the effect is present in face-to-face experiments.

Background One method suggested for creating preference-based tariffs for the new five-level EuroQol five-dimensional (EQ-5D) questionnaire is combining time trade-off (TTO) and discrete choice exercises. Rank values from previous valuation studies can be used as proxies for discrete choice exercises. This study examined rank and TTO data to determine whether the methods differ in sensitivity to the EQ-5D questionnaire dimensions. Methods We used rank and TTO data for 42 EQ-5D questionnaire health states from the US and UK three-level EQ-5D questionnaire valuation studies, extracting overall ranks of mean TTO and mean rank values, ranging from 1 (best) to 42 (worst). We identified pairs of health states with reversed overall ranks between TTO and rank data and regressed overall rank differences (TTO – ranking) on dummy variables representing impairments on EQ-5D questionnaire dimensions. Results Forty-three (US) and 41 (UK) health state pairs displayed reversed rank order. Both US and UK regression models on rank differences indicated that respondents rated impairments involving pain/discomfort and anxiety/depression as relatively worse in TTO than in the ranking task. Discussion Different dimension sensitivity between TTO and ranking methods suggests that combining them could lead to inconsistent tariffs. Differences could be caused by respondents focusing on the first presented dimensions when ranking states or could be related to the longest endurable time for health states involving pain/discomfort or anxiety/depression. The observed differences call into question which method best represents the preferences of the population.

Background The EQ-5D is a generic health-related quality of life instrument (five dimensions with three levels, 243 health states), used extensively in cost-utility/cost-effectiveness analyses. EQ-5D health states are assigned values on a scale anchored in perfect health (1) and death (0). The dominant procedure for defining values for EQ-5D health states involves regression modeling. These regression models have typically included a constant term, interpreted as the utility loss associated with any movement away from perfect health. The authors of the United States EQ-5D valuation study replaced this constant with a variable, D1, which corresponds to the number of impaired dimensions beyond the first. The aim of this study was to illustrate how the use of the D1 variable in place of a constant is problematic. Methods We compared the original D1 regression model with a mathematically equivalent model with a constant term. Comparisons included implications for the magnitude and statistical significance of the coefficients, multicollinearity (variance inflation factors, or VIFs), number of calculation steps needed to determine tariff values, and consequences for tariff interpretation. Results Using the D1 variable in place of a constant shifted all dummy variable coefficients away from zero by the value of the constant, greatly increased the multicollinearity of the model (maximum VIF of 113.2 vs. 21.2), and increased the mean number of calculation steps required to determine health state values. Discussion Using the D1 variable in place of a constant constitutes an unnecessary complication of the model, obscures the fact that at least two of the main effect dummy variables are statistically nonsignificant, and complicates and biases interpretation of the tariff algorithm.