How Prevalent Are Implausible EQ-5D-5L Health States and How Do They Affect Valuation? A Study Combining Quantitative and Qualitative Evidence

Valuation of Multi-Dimensional Health States With a Bolt-On: Is There a Shortcut?

OBJECTIVES

To use the EQ-5D questionnaire with bolt-on dimensions in economic evaluation studies, new value sets are needed. In this study, we explored the feasibility of a new approach called the scaling factor model, which estimates bolt-on value sets using estimated EQ-5D dimensional weights.

METHODS

We designed a 2-arm study, inviting university students to value health states with and without bolt-on items using the composite time trade-off method. We selected 25 health states from an orthogonal array and added the 5 mildest EQ-5D states in the design. In arm 1, EQ-5D without self-care and standard EQ-5D states were valued, and in arm 2, standard EQ-5D states and EQ-5D with vision were valued. By arm, we compared the mean observed values of health states with and without bolt-on item. Next, by arm, we estimated value sets for the EQ-5D with bolt-on states using both standard model and scaling factor model. Model performances were compared in terms of prediction accuracy and correlation with likelihood-based mean values.

RESULTS

Adding a five-level bolt-on to EQ-5D resulted in statistically lower values. This effect was consistent across 2 arms and bolt-on items. The scaling factor models outperformed the standard models in all statistics.

CONCLUSIONS

The scaling factor model offers a methodologically viable and low-cost option for producing value sets for EQ-5D supplemented with bolt-on items. Future studies should further test this method using other bolt-on items and more relevant study populations.

Evaluation of EuroQol Valuation Technology (EQ-VT) Designs to Generate National Value Sets: Learnings from the Development of an EQ-5D Value Set for India Using an Extended Design (DEVINE) Study

INTRODUCTION

Countries develop their EQ-5D-5L value sets using the EuroQol Valuation Technology (EQ-VT) protocol. This study aims to assess if extension in the conventional EQ-VT design can lead to development of value sets with improved precision.

METHODS

A cross-sectional survey was undertaken in a representative sample of 3,548 adult respondents, selected from 5 different states of India using a multistage stratified random sampling technique. A novel extended EQ-VT design was created that included 18 blocks of 10 health states, comprising 150 unique health states and 135 observations per health state. In addition to the standard EQ-VT design, which is based on 86 health states and 100 observations per health state, 3 extended designs were assessed for their predictive performance. The extended designs were created by 1) increasing the number of observations per health state in the design, 2) increasing the number of health states in the design, and 3) implementing both 1) and 2) at the same time. Subsamples of the data set were created for separate designs. The root mean squared error (RMSE) and mean absolute error (MAE) were used to measure the predictive accuracy of the conventional and extended designs.

RESULTS

The average RMSE and MAE for the standard EQ-VT design were 0.055 and 0.041, respectively, for the 150 health states. All 3 types of design extensions showed lower RMSE and MAE values as compared with the standard design and hence yielded better predictive performance. RMSE and MAE were lowest (0.051 and 0.039, respectively) for the designs that use a greater number of health states. Extending the design with inclusion of more health states was shown to improve the predictive performance even when the sample size was fixed at 1,000.

CONCLUSION

Although the standard EQ-VT design performs well, its prediction accuracy can be further improved by extending its design. The addition of more health states in EQ-VT is more beneficial than increasing the number of observations per health state.

HIGHLIGHTS

The EQ-5D-5L value sets are developed using the standardized EuroQol Valuation Technology (EQ-VT) protocol. This is the first study to empirically assess how much can be gained from extending the standard EQ-VT design in terms of sample size and/or health states. It not only presents useful insights into the performance of the standard design of the EQ-VT but also tests the potential extensions in the standard EQ-VT design in terms of increasing the health states to be directly valued as well as the number of observations recorded to predict the utility value of each of these health states.The study demonstrates that the standard EQ-VT design performs good, and an extension in the design of the standard EQ-VT can lead to further improvement in its performance. The addition of more health states in EQ-VT is more beneficial than increasing the number of observations per health state. Extending the design with inclusion of more health states marginally improves the predictive performance even when the sample size was fixed at 1,000.The findings of the study will streamline the systematic process for generating precise EQ-5D-5L value sets, thus facilitating the conduct of credible, transparent, and robust outcome valuation in health technology assessments.

Cross-Attribute Level Effects Models for Modeling Modified 5-Level Version of EQ-5D Health State Values: Is Less Still More?

OBJECTIVES

Cross-attribute level effects (CALE) model has demonstrated better predictive accuracy for out-of-sample health states than the conventional additive main-effects model in cross-validation analysis of the 5-level version of EQ-5D (EQ-5D-5L) composite time trade-off (cTTO) datasets. In this study, we aimed to further test the performance of CALE model using a different design and modified EQ-5D-5L states.

METHODS

A total of 29 EQ-5D-5L self-care bolt-off states, 30 EQ-5D-5L states, and 31 EQ-5D-5L vision bolt-on states were selected from the same orthogonal array. A total of 600 university students were interviewed face-to-face to value a subset of these health states using the cTTO method. For each type of health state, we fitted both the conventional main-effects model and the CALE model. Predictive accuracy was assessed in a series of cross-validation analysis using the leave-one-state-out method.

RESULTS

Overall, the CALE model outperformed the conventional model for each of the 3 types of health states in predicting the cTTO values of out-of-sample health states. The prediction accuracy of using the CALE model improved with the number of dimensions in health states, for example, the MAE decreased about 24%, 67%, and 77% for the EQ-5D-5L self-care bolt-off, EQ-5D-5L, and EQ-5D-5L vision bolt-on states, respectively, when using CALE models.

CONCLUSION

Our study supported the strengths of the CALE model for modelling the utility values of both original and modified EQ-5D-5L health states. Investigators with limited resources may consider using the CALE model to lower the costs for their valuation studies for EQ-5D-5L or similar health state descriptive systems.

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

OBJECTIVES

Our 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 (DCE_TTO) in China.

METHODS

A 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 DCE_TTO 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 DCE_TTO. 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.

RESULTS

In 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 DCE_TTO, 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 DCE_TTO was - 0.535 to 1, with a higher WTD of 8.50%. DCE_TTO presented minor non-monotonicity 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 DCE_TTO 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.

CONCLUSIONS

The Chinese value set for the SF-6Dv2 was established based on the TTO approach, but the DCE_TTO also performed well. Minor issues of non-monotonicity did present for DCE_TTO.

Do Rural Residents in China Understand EQ-5D-5L as Intended? Evidence From a Qualitative Study

BACKGROUND

The 5-level EQ-5D (EQ-5D-5L) has been increasingly used in China to measure the health status of the general population and patients. However, its content validity among rural residents in China has not been formally evaluated. This qualitative study aims to assess the content validity of EQ-5D-5L among rural Chinese.

METHODS

Participants were recruited from four regions (North, South, East and West) across China. Eligible participants were those living in the rural area in last three years and making a living by agricultural operations. Semi-structured interviews were conducted. Interview transcripts were analysed to assess the comprehensibility, relevance, clarity and comprehensiveness.

RESULTS

Sixty-two participants were included, comparable to the national figures regarding age, sex and education. For comprehensibility, participants could understand the 'mobility', 'self-care' and 'usual activities' domains well, but some reported confusions in 'pain/discomfort' (n = 42) and 'anxiety/depression' (n = 35). Some also reported difficulties in understanding anxiety (n = 6) and depression (n = 9), possibly due to the formal wording used. For relevance, all domains were reported as health-related and participants' responses were based on their own health. For clarity, all could distinguish the five levels, but suggestions on reducing response levels and alternative wording for 'slight' were raised. For comprehensiveness, two aspects (fatigue/energy and appetite) were raised beyond the EQ-5D-5L domains. The 'mobility' domain was selected as the most important and 'anxiety/depression' as the least important.

CONCLUSION

Rural Chinese reported problems on the content validity of Chinese EQ-5D-5L. It might be sensible to consider some revisions to make it more understandable for rural residents.

Testing the "Nonstopping" Time Trade-Off: Is It Better Than the Composite Time Trade-Off?

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.

Implausible States: Prevalence of EQ-5D-5L States in the General Population and Its Effect on Health State Valuation

The EQ-5D is made up of health state dimensions and levels, in which some combinations seem less "plausible" than others. If "implausible" states are used in health state valuation exercises, then respondents may have difficulty imagining them, causing measurement error. There is currently no standard solution: some valuation studies exclude such states, whereas others leave them in. This study aims to address 2 gaps in the literature: 1) to propose an evidence-based set of the least prevalent two-way combinations of EQ-5D-5L dimension levels and 2) to quantify the impact of removing perceived implausible states from valuation designs. For the first aim, we use data from 2 waves of the English General Practitioner Patient Survey (n = 1,639,453). For the second aim, we remodel a secondary data set of a Discrete Choice Experiment (DCE) with duration that valued EQ-5D-5L and compare across models that drop observations involving different health states: 1) implausible states as defined in the literature, 2) the least prevalent states identified in stage 1, and 3) randomly select states, alongside 4) a model that does not drop any observations. The results indicate that two-way combinations previously thought to be implausible actually exist among the general population; there are other combinations that are rarer, and removing implausible states from an experimental design of a DCE with duration leads to value sets with potentially different characteristics depending on the criterion of implausible states. We advise against the routine removal of implausible states from health state valuation studies.