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

Valuation of Multidimensional 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.

Exploring non-iterative time trade-off methods for valuation of EQ-5D-5L health states

INTRODUCTION

The composite time trade-off (cTTO) method is used as the primary method for valuing EQ-5D-5L health states, but it requires intensive interviewer training and stringent quality control, which increases the burden of conducting cTTO studies. In this study, two non-iterative variants of the TTO method, non-stopping TTO (nTTO) and open-ended TTO (oTTO), were tested head-to-head with the cTTO method aiming to reduce the administration burden.

METHODS

31 EQ-5D-5L health states from an orthogonal array was selected and valued by a general public sample in China. Data were collected by 7 interviewers with all interviewers performed an equal number of interviews using all three TTO methods. We compared the value distribution, logical consistency, administration burden, and modeling performance of these three TTO methods.

RESULTS

In total, 422 participants participated in the valuation interviews, with 139 using the nTTO method, 140 using the oTTO method, and 143 using the cTTO method. Both oTTO and nTTO methods saved around 10 min for conducting an interview. The mean values of three methods were similar with each method showed different characteristics in their value distributions. cTTO outperformed the other two methods in terms of modeling performance.

DISCUSSION

Both non-iterative TTO methods showed potential for valuing EQ-5D health states, although their data distributions and modeling performance were inferior to the cTTO method. The results of this study showed the potential of these two alternative non-iterative TTO methods.

Valuation of EQ-5D-5L health states from cancer patients' perspective: a feasibility study

OBJECTIVES

To assess the feasibility of estimating an EQ-5D-5L value set using a small study design in cancer patients and to compare the EQ-5D-5L values based on the preferences of cancer patients with those of the general public.

METHODS

Patients with clinically diagnosed cancers were recruited from two hospitals in Shanghai, China. In face-to-face interviews using the EQ-PVT survey, health states were valued by cancer patients using both cTTO and DCE methods. cTTO data was modelled alone or jointly with DCE data. Forty-eight models using different model specifications (cross-attribute level effect [CALE] and additive models), random/fixed effects model assumptions, data heteroscedasticity and censoring were estimated. The best performed model was identified in terms of monotonicity of estimated model coefficients and out-of-sample prediction accuracy.

RESULTS

Data collected from 221 cancer patients who participated in the study were included. The hybrid CALE model using both TTO and DCE data performed best in terms of prediction accuracy (Lin's concordance coefficient = 0.989; root mean squared error = 0.058) and suggested that pain/discomfort and anxiety/depression were the most undesirable health problems. Compared to values based on general Chinese public's health preferences, the values based on cancer patients' preferences were much higher and lower for health states characterized by extreme mobility problems and severe/extreme pain or discomfort, respectively.

CONCLUSION

This study demonstrated the feasibility of using a small design to develop EQ-5D-5L value sets based on cancer patients' health preferences. Since there were signs of differences between preferences of patients and general population, it may be valuable to develop patient-specific value sets and use them in clinical decision making and economic evaluations.

Creating a Multiply Imputed Value Set for the EQ-5D-5L in Canada: State-Level Misspecification Terms Are Needed to Characterize Parameter Uncertainty Correctly

BACKGROUND

Parameter uncertainty in EQ-5D-5L value sets often exceeds the instrument's minimum important difference, yet this is routinely ignored. Multiple imputation (MI) accounts for parameter uncertainty in the value set; however, no valuation study has implemented this methodology. Our objective was to create a Canadian MI value set for the EQ-5D-5L, thus enabling users to account for parameter uncertainty in the value set.

METHODS

Using the Canadian EQ-5D-5L valuation study (N = 1,073), we first refit the original model followed by models with state-level misspecification. Models were compared based on the adequacy of 95% credible interval (CrI) coverage for out-of-sample predictions. Using the best-fitting model, we took 100 draws from the posterior distribution to create 100 imputed value sets. We examined how much the standard error of the estimated mean health utilities increased after accounting for parameter uncertainty in the value set by using the MI and original value sets to score 2 data sets: 1) a sample of 1,208 individuals from the Canadian general public and 2) a sample of 401 women with breast cancer.

RESULTS

The selected model with state-level misspecification outperformed the original model (95% CrI coverage: 94.2% v. 11.6%). We observed wider standard errors for the estimated mean utilities on using the MI value set for both the Canadian general public (MI: 0.0091; original: 0.0035) and patients with breast cancer (MI: 0.0169; original: 0.0066).

DISCUSSION AND CONCLUSIONS

We provide 1) the first MI value sets for the EQ-5D-5L and 2) code to construct MI value sets while accounting for state-level model misspecification. Our study suggests that ignoring parameter uncertainty in value sets leads to falsely narrow SEs.

HIGHLIGHTS

Value sets for health state utility instruments are estimated subject to parameter uncertainty; this parameter uncertainty may exceed the minimum important difference of the instrument, yet it is not fully captured using current methods.This study creates the first multiply imputed value set for a multiattribute utility instrument, the EQ-5D-5L, to fully capture this parameter uncertainty.We apply the multiply imputed value set to 2 data sets from 1) the Canadian general public and 2) women with invasive breast cancer.Scoring the EQ-5D-5L using a multiply imputed value set led to wider standard error estimates, suggesting that the current practice of ignoring parameter uncertainty in the value set leads to falsely low standard errors.Our work will be of interest to methodologists and developers of the EQ-5D-5L and users of the EQ-5D-5L, such as health economists, researchers, and policy makers.