SF-6Dv2 preference value set for health utility in food allergy

A novel approach for health state valuation: Multiple bounded dichotomous choice compared to the traditional standard gamble

OBJECTIVE

to assess the feasibility of a new stated preference approach, the multiple bounded dichotomous choice (MBDC), designed to generate value sets for preference-based measurement of health-related quality of life.

METHODS

MBDC and standard gamble (SG) tasks were completed to derive SF-6Dv2 value sets from a sample of the general population in Quebec, Canada. Participants were randomized between the two approaches: 6 health states were evaluated in SG and 11 health states in MBDC. Several models were used to estimate data in each approach, and the preferred models were chosen by using mean absolute error (MAE), logical consistency of parameters, and significance levels. Results of MBDC were compared with SG in terms of acceptability (self-reported difficulty and quality levels in answering, and completion time), consistency (monotonicity of model coefficients), accuracy (standard errors), dimensions coefficient magnitude, correlation between the value sets estimated, and the range of estimated values. The intra-class correlation coefficient (ICC) was computed to assess value sets' consistency.

RESULTS

Out of 655 individuals who completed MBDC tasks and 828 who completed SG tasks, a total of 585 participants for MBDC and 714 for SG tasks were included for analysis. The preferred models for both approaches were GLS Tobit. No significant difference was observed in self-reported difficulties and qualities in answers among approaches, but MBDC had less excluded participants and was less prone to report difficulties in answering. Additionally, completion time in the MBDC group was significantly lower (99.80 vs 68.12 s). Most standard errors in the MBDC were lower than those in SG, and the number of non-significant parameters was also lower. The range of utility values generated by MBDC tended to be wider (-0.372 to 1) than those generated by the SG (-0.137 to 1) and the number of worse-than-dead states in MBDC (0.91%) was higher than for SG (0.08%). The Pain dimension was identified as the most significant, while the Vitality dimension showed the lowest significant decrement. Both approaches exhibited a tendency to overestimate severe health state values and underestimate better health state values. The correlation and ICC between the two value sets were 0.937 and 0.983, respectively.

CONCLUSION

Based on empirical evidence, it can be inferred that the MBDC method is not only feasible but also holds the potential to generate meaningful and well-informed preference data from respondents. This approach can be used to derive a value set for preference-based instrument.

Valuing the SF-6Dv2 in the capital of Iran using a discrete choice experiment with duration

BACKGROUND

The second version of the Short-Form 6-Dimension (SF-6Dv2) classification system has recently been developed. The objective of this study was to develop a value set for SF-6Dv2 based on the societal preferences of a general population in the capital of Iran.

METHODS

A representative sample of the capital of Iran (n = 3061) was recruited using a stratified multistage quota sampling technique. Face-to-face interviews were conducted using binary choice sets from the international valuation protocol of the discrete choice experiment with duration. The conditional logit was used to estimate the final value set, and a latent class model was employed to assess heterogeneity of preferences.

RESULTS

Coefficients generated from the models were logically consistent and significant. The best model was the one that included an additional interaction term for cases where one or more dimensions reached their most severe levels. It provides a value set with logical consistent coefficients and the lowest percentage of worse than death health states. Predicted values for the SF-6Dv2 were within the range of - 0.796-1. Pain dimension had the largest impact on utility decrement, whereas vitality had the least impact. The presence of preference heterogeneity was evident, and the Bayesian Information Criterion indicated the optimal fit for a latent class model with two classes.

CONCLUSION

This study provided the SF-6Dv2 value set for application in the context of Iran. This value set will facilitate the use of the SF-6Dv2 instrument in health economic evaluations and clinical settings.

Discrete Choice Experiments in Health State Valuation: A Systematic Review of Progress and New Trends

BACKGROUND

Discrete choice experiments (DCEs) are increasingly used in health state valuation studies.

OBJECTIVE

This systematic review updates the progress and new findings of DCE studies in the health state valuation, covering the period since the review of June 2018 to November 2022. The review reports the methods that are currently being used in DCE studies to value health and study design characteristics, and, for the first time, reviews DCE health state valuation studies published in the Chinese language.

METHODS

English language databases PubMed and Cochrane, and Chinese language databases Wanfang and CNKI were searched using the self-developed search terms. Health state valuation or methodology study papers were included if the study used DCE data to generate a value set for a preference-based measure. Key information extracted included DCE study design strategies applied, methods for anchoring the latent coefficient on to a 0-1 QALY scale and data analysis methods.

RESULTS

Sixty-five studies were included; one Chinese language publication and 64 English language publications. The number of health state valuation studies using DCE has rapidly increased in recent years and these have been conducted in more countries than prior to 2018. Wide usage of DCE with duration attributes, D-efficient design and models accounting for heterogeneity has continued in recent years. Although more methodological consensus has been found than in studies conducted prior to 2018, this consensus may be driven by valuation studies for common measures with an international protocol (the 'model' valuation research). Valuing long measures with well-being attributes attracted attention and more realistic design strategies (e.g., inconstant time preference, efficient design and implausible states design) were identified. However, more qualitative and quantitative methodology study is still necessary to evaluate the effect of those new methods.

CONCLUSIONS

The use of DCEs in health state valuation continues to grow dramatically and the methodology progress makes the method more reliable and pragmatic. However, study design is driven by international protocols and method selection is not always justified. There is no gold standard for DCE design, presentation format or anchoring method. More qualitative and quantitative methodology study is recommended to evaluate the effect of new methods before researchers make methodology decisions.

Mapping the Food Allergy Quality of Life Questionnaire Parent Form onto the Short-Form Six-Dimensions version 2

BACKGROUND

The Food Allergy Quality of Life Questionnaire Parent Form (FAQLQ-PF) is the most widely used quality of life questionnaire in food allergy. The objective of this study was to develop a mapping algorithm to convert FAQLQ-PF scores into health state utilities.

METHODS

The Short-Form Six-Dimensions version 2 (SF-6Dv2) and FAQLQ-PF questionnaires were collected from an academic center oral immunotherapy referral cohort. Utility estimates were derived from the SF-6Dv2 using the food allergy preference set. Candidate mapping algorithm models were developed using seven regression methods starting from either the total average score, the average scores of each of the three domains or the individual item scores of FAQLQ-PF. The process was repeated twice, including only section A, common to all age groups, or including all age-applicable sections of the FAQLQ-PF. The mean absolute error (MAE) and root mean squared error (RMSE) were used to select the best fitting model. An independent cohort from a previous national online survey was used for external validation.

RESULTS

In the index cohort, 1000 of 1257 respondents had completed both questionnaires. The lowest MAE (0.0791) and RMSE (0.1020) were recorded when entering individual item scores in a categorical regression model. The model including only FAQLQ-PF section A was found to be most consistent when tested in the external validation cohort (n = 248) (MAE of 0.0898).

CONCLUSION

The FAQLQ-PF was mapped onto SF-6Dv2 utilities with good predictive accuracy in two independent cohorts. This will enable calculation of health utility for cost-effectiveness analyses in food allergy.

Variations in Patients' Overall Assessment of Their Health Across and Within Disease Groups Using the EQ-5D Questionnaire: Protocol for a Longitudinal Study in the Swedish National Quality Registers

BACKGROUND

EQ-5D is one of the most commonly used questionnaires to measure health-related quality of life. It is included in many of the Swedish National Quality Registers (NQRs). EQ-5D health states are usually summarized using "values" obtained from members of the general public, a majority of whom are healthy. However, an alternative, which remains to be studied in detail, is the potential to use patients' self-reported overall health on the visual analog scale (VAS) as a means of capturing experience-based perspective.

OBJECTIVE

The aim of this study is to assess EQ VAS as a valuation method with an experience-based perspective through comparison of its performance across and within patient groups, and with that of the general population in Sweden.

METHODS

Data on nearly 700,000 patients from 12 NQRs covering a variety of diseases/conditions and nearly 50,000 individuals from the general population will be analyzed. The EQ-5D-3L data from the 12 registers and EQ-5D-5L data from 2 registers will be used in the analyses. Longitudinal studies of patient-reported outcomes among different patient groups will be conducted in the period from baseline to 1-year follow-up. Descriptive statistics and analyses comparing EQ-5D dimensions and observed self-assessed EQ VAS values across and within patient groups will be performed. Comparisons of the change in health state and observed EQ VAS values at 1-year follow-up will also be undertaken. Regression models will be used to assess whether EQ-5D dimensions predict observed EQ VAS values to investigate patient value sets in each patient group. These will be compared across the patient groups and with the existing Swedish experience-based VAS and time trade-off value sets obtained from the general population.

RESULTS

Data retrieval started in May 2019 and data of patients in the 12 NQRs and from the survey conducted among the general population have been retrieved. Data analysis is ongoing on the retrieved data.

CONCLUSIONS

This research project will provide information on the differences across and within patient groups in terms of self-reported health status through EQ VAS and comparison with the general population. The findings of the study will contribute to the literature by exploring the potential of self-assessed EQ VAS values to develop value sets using an experience-based perspective.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04359628; https://clinicaltrials.gov/ct2/show/NCT04359628.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/27669.

Analysis of SF-6D Health State Utility Scores: Is Beta Regression Appropriate?

Background: Typically, modeling of health-related quality of life data is often troublesome since its distribution is positively or negatively skewed, spikes at zero or one, bounded and heteroscedasticity. Objectives: In the present paper, we aim to investigate whether Bayesian beta regression is appropriate for analyzing the SF-6D health state utility scores and respondent characteristics. Methods: A sample of 126 Lebanese members from the American University of Beirut valued 49 health states defined by the SF-6D using the standard gamble technique. Three different models were fitted for SF-6D via Bayesian Markov chain Monte Carlo (MCMC) simulation methods. These comprised a beta regression, random effects and random effects with covariates. Results from applying the three Bayesian beta regression models were reported and compared based on their predictive ability to previously used linear regression models, using mean prediction error (MPE), root mean squared error (RMSE) and deviance information criterion (DIC). Results: For the three different approaches, the beta regression model was found to perform better than the normal regression model under all criteria used. The beta regression with random effects model performs best, with MPE (0.084), RMSE (0.058) and DIC (−1621). Compared to the traditionally linear regression model, the beta regression provided better predictions of observed values in the entire learning sample and in an out-of-sample validation. Conclusions: Beta regression provides a flexible approach to modeling health state values. It also accounted for the boundedness and heteroscedasticity of the SF-6D index scores. Further research is encouraged.