Time Trade-off Data Research Articles

Preference-based utility weights for the Individualized Neuromuscular Quality of Life Questionnaire (INQoL), with a focus on non-dystrophic myotonia (NDM).

The Individualized Neuromuscular Quality of Life Questionnaire (INQoL) is used to measure quality of life in neuromuscular disorders such as non-dystrophic myotonia (NDM). Here we report methods to estimate utilities, with a focus on NDM, from this questionnaire based on two preference elicitation exercises. Eight items from the INQoL were selected with input from three neuromuscular disorder clinical experts with expertise in treating NDM. A discrete choice experiment (DCE) survey of UK general public respondents (n = 508) described outcomes defined by the INQoL items. The same 8 items were also valued using time trade-off (TTO) face-to-face interviews (n = 200). A hybrid regression modelling approach combined both datasets to inform the utility weights. Hybrid modelling of DCE and TTO data in conjunction improved out-of-sample predictive accuracy. The selected INQoL utility model indicates substantial disutility associated with all eight dimensions of health, with the greatest losses associated with subjective items such as pain and depression. The hybrid modelling approach allows us to combine data from the two methodologies and maximize the information from each to inform the utility weights for the INQoL. The TTO is the more conventional valuation method, but combined with the larger DCE study produced better descriptive coverage. This is a relatively novel method for estimating weights which we think is particularly well suited to economic evaluations of orphan drugs.

Prognostic Information on Progression to Dementia: Quantification of the Impact on Quality of Life.

The increasing interest in early identification of people at risk of developing dementia, has led to the development of numerous models aimed at estimating the likelihood of progression from mild cognitive impairment (MCI) to dementia. It is important to study both the need for and possible outcomes related with such prediction models, including the impact of risk predictions on perceived quality of life (QoL). This study aimed to quantify the impact that receiving a risk prediction on progression from MCI to dementia has on QoL. A Discrete Choice Experiment (DCE) and Time Trade Off (TTO) study were performed. Participants completed choice tasks related to dementia prognosis while imagining having MCI. We collected DCE data by an online survey, and TTO data via videoconferencing interviews. DCE data were analyzed using a mixed multinomial logit model and were anchored to a health state utility scale using mean observed TTO valuations. 296 people participated in the DCE and 42 in the TTO. Moderate and high predicted dementia risks were associated with decrements in utility (-0.05 and -0.18 respectively), compared to no prognostic information. Low predicted risk was associated with an increase in utility (0.06), as well as the availability of medication or lifestyle interventions (0.05 and 0.13 respectively). This study shows a significant impact of dementia risk predictions on QoL and highlights the importance of caution when sharing information about expected MCI disease courses.

Valuing selected WAItE health states using the Time Trade-Off methodology: findings from an online interviewer-assisted remote survey

PurposeThe Weight-Specific Adolescent Instrument for Economic Evaluation (WAItE) is a physical weight-specific patient reported outcome measure for use in adolescence. The purpose of this study was to use the Time Trade-Off (TTO) methodology, administered using an online interviewer-assisted remote survey, to obtain utility values for several health states from the WAItE descriptive system from a sample of the UK adult general population.MethodsThe adult sample was gathered using a market research company and a sample of local residents. All participants completed the same interviewer-assisted remote survey, which included rating WAItE states of varying impairment using the TTO.Results42 adults completed the survey. Utility values were gathered for four health states, ranging from low impairment to the most severe health from the WAItE descriptive system (the Pits state). Consistent orderings of the WAItE health states were observed; the health state with the lowest level of impairment was valued highest and the Pits state was valued lowest. Several respondents (n = 7, 17%) considered the Pits state to be worse than death; however, the mean value of this health state was 0.23.ConclusionsThe utility value of the Pits state relative to death generated from this study will be used to anchor latent values for WAItE health states generated from a Discrete Choice Experiment onto the 0 = death, 1 = full health Quality Adjusted Life Year (QALY) scale as part of a valuation study for the WAItE in the UK population. This study also provides further evidence that interviewer-assisted digital studies are feasible for collecting TTO data.

Eliciting a value set for the Swedish Capability-Adjusted Life Years instrument (CALY-SWE)

PurposeOur aim was to elicit a value set for Capability-Adjusted Life Years Sweden (CALY-SWE); a capability-grounded quality of life instrument intended for use in economic evaluations of social interventions with broad consequences beyond health.MethodsBuilding on methods commonly used in the quality-adjusted life years EQ-5D context, we collected time-trade off (TTO) and discrete choice experiment (DCE) data through an online survey from a general population sample of 1697 Swedish participants. We assessed data quality using a score based on the severity of inconsistencies. For generating the value set, we compared different model features, including hybrid modeling of DCE and TTO versus TTO data only, censoring of TTO answers, varying intercept, and accommodating for heteroskedasticity. We also assessed the models’ DCE logit fidelity to measure agreement with potentially less-biased DCE data. To anchor the best capability state to 1 on the 0 to 1 scale, we included a multiplicative scaling factor.ResultsWe excluded 20% of the TTO answers of participants with the largest inconsistencies to improve data quality. A hybrid model with an anchor scale and censoring was chosen to generate the value set; models with heteroskedasticity considerations or individually varying intercepts did not offer substantial improvement. The lowest capability weight was 0.114. Health, social relations, and finance and housing attributes contributed the largest capability gains, followed by occupation, security, and political and civil rights.ConclusionWe elicited a value set for CALY-SWE for use in economic evaluations of interventions with broad social consequences.

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

ObjectivesTo compare anchored discrete choice experiment (DCE) utility values using own versus others’ time trade-off (TTO) responses in the valuation of SF-6Dv2. MethodsA representative sample of the general population was recruited in China. Through face-to-face interviews, both DCE and TTO data were collected from a randomly selected half of the respondents (own TTO sample), whereas only TTO data were collected from the other half (others’ TTO sample). Conditional logit model was used to estimate DCE latent utilities. Three anchoring methods, including using the observed and the modeled TTO values for the worst state, and mapping DCE values onto TTO, were used to scale the latent utilities to health utilities. Prediction accuracy was assessed using intraclass correlation coefficient, mean absolute difference, and root mean squared difference compared with the mean observed TTO values between the anchoring results using the own versus others’ TTO data. ResultsDemographic characteristics were comparable between the own TTO sample (n = 252) and the others’ TTO sample (n = 251). The mean (SD) observed TTO value for the worst state was −0.259 (0.591) for the own TTO sample and −0.236 (0.616) for the others’ TTO sample. Anchoring DCE using own TTOs consistently showed a better prediction accuracy than using others’ TTOs across the 3 anchoring methods in terms of the intraclass correlation coefficient (0.835-0.873 vs 0.771-0.804), mean absolute difference (0.127-0.181 vs 0.146-0.203), and root mean squared difference (0.164-0.237 vs 0.192-0.270). ConclusionWhen anchoring DCE-derived latent utilities onto the health utility scale, respondents’ own TTO data would be preferred to TTO data obtained from a different sample.

Efficient Designs for Valuation Studies That Use Time Tradeoff (TTO) Tasks to Map Latent Utilities from Discrete Choice Experiments to the Interval Scale: Selection of Health States for TTO Tasks.

In eliciting utilities to value multiattribute utility instruments, discrete choice experiments (DCEs) administered online are less costly than interviewer-facilitated time tradeoff (TTO) tasks. DCEs capture utilities on a latent scale and are often coupled with a small number of TTO tasks to anchor utilities to the interval scale. Given the costly nature of TTO data, design strategies that maximize value set precision per TTO response are critical. Under simplifying assumptions, we expressed the mean square prediction error (MSE) of the final value set as a function of the number of TTO-valued health states and the variance of the states' latent utilities. We hypothesized that even when these assumptions do not hold, the MSE 1) decreases as increases while holding fixed and 2) decreases as increases while holding fixed. We used simulation to examine whether there was empirical support for our hypotheses a) assuming an underlying linear relationship between TTO and DCE utilities and b) using published results from the Dutch, US, and Indonesian EQ-5D-5L valuation studies. Simulation set (a) supported the hypotheses, as did simulations parameterized using valuation data from Indonesia, which showed a linear relationship between TTO and DCE utilities. The US and Dutch valuation data showed nonlinear relationships between TTO and DCE utilities and did not support the hypotheses. Specifically, for fixed , smaller values of reduced rather than increased the MSE. Given that, in practice, the underlying relationship between TTO and DCE utilities may be nonlinear, health states for TTO valuation should be placed evenly across the latent utility scale to avoid systematic bias in some regions of the scale. Valuation studies may feature a large number of respondents completing discrete choice tasks online, with a smaller number of respondents completing time tradeoff (TTO) tasks to anchor the discrete choice utilities to an interval scale.We show that having each TTO respondent complete multiple tasks rather than a single task improves value set precision.Keeping the total number of TTO respondents and the number of tasks per respondent fixed, having 20 health states directly valued through TTO leads to better predictive precision than valuing 10 health states directly.If DCE latent utilities and TTO utilities follow a perfect linear relationship, choosing the TTO states to be valued by weighting on the 2 ends of the latent utility scale leads to better predictive precision than choosing states evenly across the latent utility scale.Conversely, if DCE latent utilities and TTO utilities do not follow a linear relationship, choosing the states to be valued using TTO evenly across the latent utility scale leads to better predictive precision than weighted selection does.In the context of valuation of the EQ-5D-Y-3L, we recommend valuing 20 or more health states using TTO and placing them evenly across the latent utility scale.

Estimating an EQ-5D-Y-3L Value Set for Indonesia by Mapping the DCE onto TTO Values.

Methods for estimating health values in adult populations are well developed, but lag behind in children. The EuroQol standard protocol to arrive at value sets for the youth version of the EQ-5D-Y-3L combines discrete choice experiments with ten composite time trade-off values. Whether ten composite time trade-off values are sufficient remains to be seen and this is one of the reasons the protocol allows for experimental expansion. In this study, 23 health states were administered for the composite time trade-off. This methodological research is embedded in a study aimed at generating a representative value set for EQ-5D-Y-3L in Indonesia. A representative sample of 1072 Indonesian adults each completed 15 discrete choice experiment choice pairs via face-to-face interviews. The discrete choice experiment responses were analysed using a mixed-logit model. To anchor the discrete choice experiment values onto the full health-dead quality-adjusted life-year scale, composite time trade-off values were separately obtained from 222 adults living in Java for 23 EQ-5D-Y-3L states. The derived latent discrete choice experiment values were mapped onto the mean observed composite time trade-off values to create a value set for the EQ-5D-Y-3L. Linear and non-linear mapping models were explored to estimate the most efficient and valid model for the value set. Coefficients obtained from the choice model were consistent with the monotonic structure of the EQ-5D-Y-3L instrument. The composite time trade-off data showed non-linearity, as the values for the two worst states being evaluated were much lower than predicted by a standard linear model estimated over all composite time trade-off data. Thus, the non-linear mapping strategies with a power term outperformed the linear mapping in terms of mean absolute error. The final model gave a value range from 1.000 for full health (11111) to -0.086 for the worst health state (33333). Values were most affected by pain/discomfort and least by self-care. This article presents the first EQ-5D-Y-3L value set for Indonesia based on the stated preferences of adults asked to consider their views about a 10-year-old child. Mapping the mixed-logit discrete choice experiment model with the inclusion of a power term (without a constant) allowed us to generate a consistent value set for Indonesian youth. Our findings support the expansion of the composite time trade-off part of the EQ-5D-Y valuation study design and show that it would be wise to account for possible non-linearities in updates of the design.

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

ObjectivesThis study aimed to introduce a parsimonious modeling approach that enables the estimation of interaction effects in health state valuation studies. MethodsInstead of supplementing a main-effects model with interactions between each and every level, a more parsimonious optimal scaling approach is proposed. This approach is based on the mapping of health state levels onto domain-specific continuous scales. The attractiveness of health states is then determined by the importance-weighted optimal scales (ie, main effects) and the interactions between these domain-specific scales (ie, interaction effects). The number of interaction terms only depends on the number of health domains. Therefore, interactions between dimensions can be included with only a few additional parameters. The proposed models with and without interactions are fitted on 3 valuation data sets from 2 different countries, that is, a Dutch latent-scale discrete choice experiment (DCE) data set with 3699 respondents, an Australian time trade-off data set with 400 respondents, and a Dutch DCE with duration data set with 788 respondents. ResultsImportant interactions between health domains were found in all 3 applications. The results confirm that the accumulation of health problems within health states has a decreasing marginal effect on health state values. A similar effect is obtained when so-called N3 or N5 terms are included in the model specification, but the inclusion of 2-way interactions provides superior model fits. ConclusionsThe proposed interaction model is parsimonious, produces estimates that are straightforward to interpret, and accommodates the estimation of interaction effects in health state valuation studies with realistic sample size requirements. Not accounting for interactions is shown to result in biased value sets, particularly in stand-alone DCE with duration studies.

A Value Set for the EQ-5D-Y-3L in the Netherlands.

Background and ObjectiveThere is increasing interest in preference-accompanied measures of health for paediatric populations. The child-friendly EQ-5D version, EQ-5D-Y-3L, is one such instrument, but the lack of a Dutch value set prevents its use in economic evaluations of healthcare interventions in the Netherlands. This study aims at covering this gap by collecting preferences using a standardised protocol for deriving EQ-5D-Y-3L value sets.MethodsComposite time trade-off data were collected using videoconferencing interviews, with each respondent completing ten composite time trade-off tasks. Discrete choice experiment data were collected using an online survey, with respondents each completing 15 paired comparisons. Respondents completed these tasks considering what they prefer for a hypothetical 10-year-old child. Discrete choice experiment data were analysed using a ten-parameter mixed-logit model and anchored to the quality-adjusted life-year scale using the mean observed composite time trade-off values.ResultsThe study collected preferences for 197 respondents using composite time trade-off and for 959 respondents using a discrete choice experiment. The discrete choice experiment sample was representative for the Dutch population in terms of age and sex. The level 3 weight for pain/discomfort was the largest, followed by feeling worried, sad or unhappy, usual activities, mobility and self-care. Health state values ranged between −0.218 and 1.ConclusionsThis study generated a Dutch value set for the EQ-5D-Y-3L, which can be used for the computation of quality-adjusted life-years for economic evaluations of healthcare interventions in paediatric populations.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40273-022-01192-0.

Developing the EQ-5D-5L Value Set for Uganda Using the 'Lite' Protocol.

ObjectiveA ‘lite’ version of the EQ-5D-5L valuation protocol, which requires a smaller sample by collecting more data from each participant, was proposed and used to develop an EQ-5D-5L value set for Uganda.MethodsAdult respondents from the general Ugandan population were quota sampled based on age and sex. Eligible participants were asked to complete 20 composite time trade-off tasks in the tablet-assisted personal interviews using the offline EuroQol Portable Valuation Technology software under routine quality control. No discrete choice experiment task was administered.The composite time trade-off data were modelled using four additive and two multiplicative regression models. Model performance was evaluated based on face validity, prediction accuracy in cross-validation and in predicting mild health states. The final value set was generated using the best-performing model.ResultsA representative sample (N = 545) participated in this study. Responses to composite time trade-off tasks from 492 participants were included in the primary analysis. All models showed face validity and generated comparable prediction accuracy. The Tobit model with constrained intercepts and corrected for heteroscedasticity was considered the preferred model for the value set on the basis of better performance. The value set ranges from − 1.116 (state 55555) to 1 (state 11111) with ‘pain/discomfort’ as the most important dimension.ConclusionsThis is the first EQ-5D-5L valuation study using a ‘lite’ protocol involving composite time trade-off data only. Our results suggest its feasibility in resource-constrained settings. The established EQ-5D-5L value set for Uganda is expected to be used for economic evaluations and decision making in Uganda and the East Africa region.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40273-021-01101-x.

Health preferences and preventive care utilisation: How EQ-5D-5L health preferences may affect uptake

Despite the economic and health benefits of preventive care being well established, the uptake of many cost-effective preventive services remains lower than desired in many cases, especially among specific sub-populations. The value an individual places on health can influence their uptake of preventive care. One way to capture the value an individual places on health and future health status is to examine their health preferences. This study used a novel use of EQ-5D-5L health preferences to determine if health preferences are associated with the uptake of a range of preventive care services, including a cancer screening, blood pressure check, cholesterol check, blood test and urine test. We collected EQ-5D-5L composite time trade-off data in 2018/2019 on 242 respondents residing in Ireland. We estimated an initial tobit model to predict an individual’s health preference to capture health preferences as a regressor. We then estimated a bivariate probit model to examine the uptake of each preventive service and GP use. Each model controlled for health preferences, education, sex, type of health coverage, self-reported health, employment status, age and marital status. Health preferences are a significant determinant of all five preventive services while controlling for other covariates. The results shows that the higher an individual values good health, the more likely they are to avail of preventive care. Health preferences can be noted as a potential determinant of preventive care use that could guide policy responses seeking to increase demand-side factors for preventive care uptake.

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

ObjectivesOur objective was to generate a value set for the SF-6Dv2 using time trade-off (TTO) and a discrete-choice experiment with a duration dimension (DCETTO) in China.MethodsA large representative sample of the Chinese general population was recruited from eight provinces/municipalities in China, stratified by age, sex, education level, and proportion of urban/rural residence. Respondents completed eight TTO tasks and ten DCETTO tasks during face-to-face interviews. Ordinary least squares (OLS), random-effects, fixed-effects, and Tobit models were used for TTO data, and conditional logit and mixed logit models were used for DCETTO. The monotonicity of model coefficients and the consistency of the predicted values according to intraclass correlation coefficient (ICC), mean absolute difference (MAD), and mean squared difference (MSD) were compared between the two approaches.ResultsIn total, 3320 respondents (50.3% male; range 18–90 years) were recruited. The random-effects model and the conditional logit model were preferred for the TTO and DCETTO, respectively. The TTO values ranged from − 0.277 to 1, with 927 (4.94%) states considered as worse than dead (WTD). The corresponding range for DCETTO was − 0.535 to 1, with a higher WTD of 8.50%. DCETTO presented minor non­monotonicity with the coefficients in two dimensions. Values from the two approaches were highly consistent (ICC 0.9804, MAD 0.0588, MSD 0.0055), albeit those with DCETTO were slightly lower than those with TTO. The value set generated by TTO was preferred given the better monotonicity and the statistical significance of coefficients.ConclusionsThe Chinese value set for the SF-6Dv2 was established based on the TTO approach, but the DCETTO also performed well. Minor issues of non­monotonicity did present for DCETTO.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40273-020-00997-1.

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

To explore the acceptability, consistency, and accuracy of eliciting health state utility values using discrete choice experiment (DCE) and DCE with life duration dimension (DCETTO) as compared with conventional time trade-off (TTO) by using the SF-6Dv2. During face-to-face interviews, a representative sample of the general population in Tianjin, China, completed 8 TTO tasks and 10 DCE/DCETTO tasks, with the order of TTO and DCE/DCETTO being randomized. The fixed-effect model and conditional logit models were used for TTO and DCEs data estimation, respectively. Acceptability was assessed by self-reported difficulties in understanding/answering. Consistency was observed by the monotonicity of model coefficients. Accuracy was evaluated by investigating differences between observed and predicted TTO values using intraclass correlation coefficient, mean absolute difference, and root mean square difference. A total of 503 respondents (53.7% male; range, 18-86 years) were included, with comparable characteristics between respondents who completed DCE (N= 252) and DCETTO (N= 251). No significant difference was observed in self-reported difficulties among 3 approaches. The monotonicity of coefficients could not be achieved for 2 DCE approaches, even when combining the inconsistent levels. The health state utility values generated by DCE were generally higher than those generated by TTO, whereas DCETTO was lower than TTO. The TTO had a better prediction accuracy than the DCEs. Two DCE approaches are feasible for eliciting health state utility values; however, they are not considered to be easier to understand/answer than TTO. There are systematic differences in the health state utility values generated by 3 approaches. The issue of non-monotonicity from 2 DCE approaches remains a concern.

Using Both Time Tradeoff and Discrete Choice Experiments in Valuing the EQ-5D: Impact of Model Misspecification on Value Sets.

Introduction. The EQ-5D-5L valuation protocol contains both time tradeoff (TTO) tasks and discrete choice experiments (DCE), raising the question of how to best use these in creating a value set. The hybrid model, which combines TTO and DCE data, has emerged as a commonly used approach. However, this model assumes independence among responses from the same individual, a linear relationship between TTO and DCE utilities, and, in many implementations, homoscedastic residuals. The aims of this study are to examine alternatives to these assumptions and determine the impact of misspecification on value sets. Methods. We performed a simulation study, parameterized using the US EQ-5D-5L valuation study, to assess the impact of model misspecification. We simulated TTO and DCE data with nonlinear relationships between TTO and DCE utilities, heteroscedastic errors, and correlated responses. Simulated data were analyzed using hybrid models with and without heteroscedasticity, Tobit models with and without heteroscedasticity, a latent class model, and a mixed model. Results. Mean absolute errors (MAEs) for correctly specified models were <0.05, whereas models that incorrectly assumed a linear relationship between TTO and DCE utilities or homoscedasticity of TTO responses featured states with an MAE >0.1. When a linear relationship between TTO and DCE utilities held, using both TTO and DCE data under correct specification yielded smaller MAEs compared with using TTO data alone but yielded larger MAEs when a linear relationship did not hold. Mistakenly assuming homoscedasticity led to increased MAEs, whereas ignoring dependence did not. Conclusions. Because heteroscedasticity in TTO utilities and nonlinear associations between DCE and TTO utilities have been noted, we recommend careful assessment of scedasticity and linearity to ascertain the suitability of a hybrid model.

A French Value Set for the EQ-5D-5L.

ObjectiveThe objective of this study was to develop a French value set for the EQ-5D-5L, for academic and clinical research, and for regulatory requirements for price-setting of drugs and medical devices.MethodThis study used the standardized valuation protocol developed by EuroQol, using computer-assisted personal interview software. A representative sample of 1048 French residents were interviewed by a market research company, under the supervision of the research team. Health states were valued using composite time trade-off and a discrete choice experiment. Modeling was used to create values for the 3125 possible health states. The composite time trade-off data were modeled using a Tobit model with censored observations at −1 and correcting for heteroscedasticity. A conditional logit model was used for the discrete choice results, and both models were combined using a hybrid model. An adjusted hybrid model was tested to correct for imbalance in the sample on age and sex compared with the general population. A comparison with the 3-level (3L) value set was performed.ResultsThe adjusted model was preferred to comply with the representativeness of the general population. It provided a value set for which all coefficients were logically consistent. Values ranged from − 0.525 to 1. The distribution of values presented a shift towards higher values versus the 3L value set. Ranking of dimensions changed. Pain and discomfort and mobility were the dimensions with the highest potential for disutility compared with mobility and self-care for the 3L instrument.ConclusionsThis study provides a value set based on societal preferences of the French population, using an improved descriptive instrument of health-related quality-of-life health states. It will contribute to improve the quality of cost-effectiveness analysis in the French context and help stimulate disease-specific quality-of-life references for academic-, institutional-, and industry-promoted studies.Electronic supplementary materialThe online version of this article (10.1007/s40273-019-00876-4) contains supplementary material, which is available to authorized users.

Toward a Smaller Design for EQ-5D-5L Valuation Studies

BackgroundTo construct an EQ-5D-5L value set, the EuroQol Group developed a standard protocol named EuroQol Valuation Technology (EQ-VT), prescribing the valuation of 86 health states utilizing the composite time trade-off (cTTO) approach, and subsequently modeled the observed values to yield values for all 3125 states. ObjectiveA recent study demonstrated that a 25-state orthogonal design could provide as accurate predictions as the EQ-VT design applying visual analogue scale data. We aimed to test that design using time trade-off (TTO) data. MethodWe collected TTO values utilizing EQ-VT, orthogonal, and D-efficient designs. The EQ-VT design included 86 health states distributed over 3 blocks of 30 states with some duplicates. The orthogonal and D-efficient designs each comprised 1 block of 30 states. A total of 525 university students were asked to value a random block of health states using EQ-PVT (a PowerPoint replica of EQ-VT software), which generated 100 observations per health state in all 3 designs. We modeled data by design and compared the root mean square error (RMSE) between observed and predicted values within and across the designs. ResultsThe EQ-VT design had the lowest RMSE of 0.052; the RMSEs for the orthogonal and the D-efficient designs were 0.066 and 0.063, respectively. RMSE results between designs differed for more severe health states. Some coefficients differed between designs. ConclusionSmaller designs did not lead to significant increases in prediction errors when modeling TTO data (measuring 0.01 on a utility scale). Resource-constrained countries may use small designs for valuation studies, especially when other types of preference data, such as those from discrete choice experiments, are collected and modeled jointly.

Cultural Values: Can They Explain Differences in Health Utilities between Countries?

Introduction. Health utilities are widely used in health care. The distributions of utilities differ between countries; some countries more often report worse than dead health states, while mild states are valued more or less the same. We hypothesize that cultural values explain these country-related utility differences. Research Question. What is the effect of sociodemographic background, methodological factors, and cultural values on differences in health utilities? Methods and Analyses. Time tradeoff data from 28 EQ-5D valuation studies were analyzed, together with their sociodemographic variables. The dependent variable was , the utility difference between mild and severe states. Country-specific cultural variables were taken from the World Values Survey. Multilevel models were used to analyze the effect of sociodemographic background, methodology (3L v. 5L), and cultural values on . Intraclass correlation (ICC) for country variation was used to assess the impact of the predicting variables on the variation between countries. Results. Substantial variation in was found between countries. Adding cultural values did not reduce ICCs for country variation. Sociodemographic background variables were only weakly associated with and did not affect the ICC. was 0.118 smaller for EQ-5D-5L studies. Discussion. varies between countries. These differences were not explained by national cultural values. In conclusion, despite correction for various variables, utility differences between countries remain substantial and unexplained. This justifies the use of country-specific value sets for instruments such as the EQ-5D.

Latent Class Models Reveal Poor Agreement between Discrete-Choice and Time Tradeoff Preferences.

Background. In health economics, there has been interest in using discrete-choice experiments (DCEs) to derive preferences for health states in lieu of previously established approaches like time tradeoff (TTO). We examined whether preferences elicited through DCEs are associated and agree with preferences elicited through TTO tasks. Methods. We used data from 1073 respondents to the Canadian EQ-5D-5L valuation study. Multivariate mixed-effects models specified a common likelihood for the TTO and discrete-choice data, with separate but correlated random effects for the TTO and DCE data, for each of the 5 EQ-5D-5L dimensions. Multivariate latent class models allowed separate but associated latent classes for the DCE and TTO data. Results. Correlation between the random effects for the 2 tasks ranged from -0.12 to 0.75, with only pain/discomfort and anxiety/depression having at least a 50% posterior probability of strong (>0.6) correlation. Latent classes for the TTO and DCE data both featured 1 latent class capturing participants attaching large disutilities to pain/discomfort, another capturing participants attaching large disutility to anxiety/depression, and the third class capturing the remainder. Agreement in class membership was poor (κ coefficient: 0.081; 95% credible interval, 0.033-0.13). Fewer respondents expressed strong disutilities for problems with anxiety/depression or pain/discomfort in the TTO than the DCE data (17% v. 55%, respectively). Conclusions. Stated preferences using TTO and DCEs show association across dimensions but poor agreement at the level of individual health states within respondents. Joint models that assume agreement between DCE and TTO have been used to develop national value sets for the EQ-5D-5L. This work indicates that when combining data from both techniques, methods requiring association but not agreement are needed.

Valuation of EQ-5D-5L health states: a comparison of seven Asian populations

ABSTRACTObjectives: To compare the time trade-off (TTO) utility values of EQ-5D-5L health states elicited from different general populations in Asia.Methods: We analyzed the TTO data from seven Asian EQ-5D-5L valuation studies in which utility values of 86 EQ-5D-5L health states were elicited from general population samples. An eight-parameter multiplicative regression model including five dimension parameters (mobility [MO], self-care, usual activities [UA], pain/discomfort, anxiety/depression) and three level parameters (level 2 [L2], level 3 [L3], and level 4 [L4]) was used to model the data from each of the populations. The model coefficients were compared to understand how the valuations of EQ-5D-5L health states differ.Results: For dimension parameters, Korea and Indonesia generally had the highest and lowest values among the populations, respectively; UA and MO commonly had the highest and lowest values among the parameters, respectively. For level parameters, Singapore and Korea generally had the highest and lowest values, respectively; L2 showed less variance compared to L3 and L4. Koreans, Indonesians, and Singaporeans appeared to have different health preferences compared with other populations.Conclusion: Utility values of EQ-5D-5L health states differ among Asian populations, suggesting that each health system should establish and use its own value set.

Valuation of Health States Considered to Be Worse Than Death—An Analysis of Composite Time Trade-Off Data From 5 EQ-5D-5L Valuation Studies

ObjectivesTo evaluate the discriminative ability of negative values measured in 5-level EuroQol 5-dimensional questionnaire (EQ-5D-5L) value set studies. MethodsThis is a secondary analysis of EQ-5D-5L value set studies from Singapore, the Netherlands, China, Thailand, and Canada in which health state values were elicited from a general population sample using a composite time trade-off (TTO) method. Mean values were calculated for health states with same severity. The association between the mean values and severity was evaluated using Pearson correlation (r). A linear mixed model using severity as the fixed effect was fitted for values. The analyses were performed separately for positive values (from a conventional TTO for health states considered “better than death”) and negative values (from a lead time TTO for health states considered “worse than death”). ResultsIn Singapore (N = 1000; negative values 32.6%), the mean decreased with severity from 0.89 to 0.21 for positive values and increased with severity from −0.98 to −0.89 for negative values. The correlation between values and severity was much lower for negative values (r = −0.016) than for positive values (r = −0.614). The coefficient of severity in the linear mixed model for negative values was much smaller (coefficient = −0.009; pseudo-R2 < 0.001) compared with the model for positive values (coefficient = −0.041; pseudo-R2 = 0.337). Results using data sets from the other countries were similar. ConclusionsNegative values are not associated with severity of health states in EQ-5D-5L valuation studies, suggesting poor discriminative ability of the lead time TTO method in valuing health states considered worse than death.