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Typically, models that were used for health state valuation data have been parametric. Recently, many researchers have explored the use of nonparametric Bayesian methods in this field. In this article, we report on the results from using a nonparametric model to predict a Bayesian short-form 6-dimension (SF-6D) health state valuation algorithm along with estimating the effect of the individual characteristics on health state valuations. A sample of 126 Lebanese members from the American University of Beirut valued 49 SF-6D health states using the standard gamble technique. Results from applying the nonparametric model were reported and compared with those obtained using a standard parametric model. The covariates' effect on health state valuations was also reported. The nonparametric Bayesian model was found to perform better than the parametric model at (1) predicting health state values within the full estimation data and in an out-of-sample validation in terms of mean predictions, root mean squared error, and the patterns of standardized residuals and (2) allowing for the covariates' effect to vary by health state. The findings also suggest a potential age effect with some gender effect. The nonparametric model is theoretically more flexible and produces better utility predictions from the SF-6D than previously used classical parametric model. In addition, the Bayesian model is more appropriate to account the covariates' effect. Further research is encouraged. • This article demonstrates that the nonparametric Bayesian method is a superior modeling choice, with the potential ultimate goal being to disseminate by how much so to encourage researchers to use this modeling choice over a parametric approach. • This article also demonstrates that the nonparametric Bayesian approach is a powerful method for handling limited data. • The superiority of the nonparametric Bayesian approach may prove to be important in practice and hence highly likely to be the first choice for applied researchers. [ABSTRACT FROM AUTHOR]

The SF-6Dv1 is a preference-based measure derived from the SF-36 for use in quality-adjusted life-year estimation for cost-utility analysis. Country-specific value sets for SF-6Dv1 are needed to reflect societal preferences but none are available for Lebanon and other Arabic countries. This study aimed to generate a value set for SF-6Dv1 for Lebanon and to compare results with the UK set. A sample of 249 health states defined by the SF-6Dv1 were valued by a representative sample of 577 members of the Lebanon general population, using standard gamble. Several multivariate regression models at mean and individual level were fitted to estimate utilities for all SF-6Dv1 states with selection of best fitting models based on predictive ability, consistency, and model fit. The best fitting models were compared with those fitted in the UK study. Data from 553 eligible respondents providing 3308 valuations were used for the analysis. Lebanese values were consistently higher than UK values, indicating differences in preferences, and there were no negative values. The random effects model using only main effects was the best performing model. There were inconsistencies in 2 dimensions, thereby consistent models were estimated with values ranging from 0.367 to 1. The results are consistent with the UK results. This study provides the first population-based value set for SF-6Dv1 health states for Lebanon, making it possible to generate quality-adjusted life-years for cost-utility analysis studies. The potential for applications of a standardized utility measure is enormous both in Lebanon and all Arab countries. • This study is the first population-based value set for SF-6Dv1 health states in Lebanon, and its availability will enable calculating quality-adjusted life-years and conducting cost utility analysis studies. • This research would serve as a block in building infrastructure for conducting local cost-effectiveness analysis and advancing the establishment of health technology assessment practices in Lebanon to support resources allocation decisions. • The potential for applications of a standardized utility measure is enormous both in Lebanon and the rest of Arab countries. [ABSTRACT FROM AUTHOR]

Abstract Background Experimental studies to develop valuations of health state descriptive systems like EQ-5D or SF-6D need to be conducted in different countries, because social and cultural differences are likely to lead to systematically different valuations. There is a scope utilize the evidence in one country to help with the design and the analysis of a study in another, for this to enable the generation of utility estimates of the second country much more precisely than would have been possible when collecting and analyzing the country’s data alone. Methods We analyze SF-6D valuation data elicited from representative samples corresponding to the Hong Kong (HK) and United Kingdom (UK) general adult populations through the use of the standard gamble technique to value 197 and 249 health states respectively. We apply a nonparametric Bayesian model to estimate a HK value set using the UK dataset as informative prior to improve its estimation. Estimates are compared to a HK value set estimated using HK values alone using mean predictions and root mean square error. Results The novel method of modelling utility functions permitted the UK valuations to contribute significant prior information to the Hong Kong analysis. The results suggest that using HK data alongside the existing UK data produces HK utility estimates better than using the HK study data by itself. Conclusion The promising results suggest that existing preference data could be combined with valuation study in a new country to generate preference weights, making own country value sets more achievable for low and middle income countries. Further research is encouraged.

Abstract Background Valuations of health state descriptors such as EQ-5D or SF6D have been conducted in different countries. There is a scope to make use of the results in one country as informative priors to help with the analysis of a study in another, for this to enable better estimation to be obtained in the new country than analyzing its data separately. Methods Data from 2 EQ-5D valuation studies were analyzed using the time trade-off technique, where values for 42 health states were devised from representative samples of the UK and US populations. A Bayesian non-parametric approach has been applied to predict the health utilities of the US population, where the UK results were used as informative priors in the model to improve their estimation. Results The findings showed that employing additional information from the UK data helped in the production of US utility estimates much more precisely than would have been possible using the US study data alone. Conclusion It is very plausible that this method would serve useful in countries where the conduction of large evaluation studies is not very feasible.

Background: Conventionally, models used for health state valuation data have been parametric. Recently, a number of researchers have investigated the use of non-parametric Bayesian methods in this area. Objectives: In this paper, we present a non-parametric Bayesian model to estimate a preference-based index for a five-dimensional health state classification, namely EQ-5D. Methods: A sample of 2997 members of the UK general population valued 43 health states selected from a total of 243 health states defined by the EQ-5D using time trade-off technique. Findings from non-parametric modelling are reported in this paper and compared to previously used parametric estimations. The impact of respondent characteristics on health state valuations is also reported. Results: The non-parametric models were found to be better at predicting scores in populations with different distributions of characteristics than observed in the survey sample. Additionally, non-parametric models were found to be better at allowing for the impact of respondent characteristics to vary by health state. The results show an important age effect with sex having some effect. Conclusion: The non-parametric Bayesian models provide more realistic and better utility estimates from the EQ-5D than previously used parametric models have done. Furthermore, the model is more flexible in estimating the impact of covariates. [ABSTRACT FROM AUTHOR]

Valuations of health state descriptors such as the generic EuroQol five-dimensional (EQ-5D) or the six-dimensional short form (SF-6D) have been conducted in different countries. There is a scope to make use of the results in one country as informative priors to help with the analysis of a study in another, for this to enable better estimation to be obtained in the new country than analysing its data separately. This article analyses data from 2 EQ-5D valuation studies where, using similar time trade-off protocols, values for 42 common health states were elicited from representative samples of the US and UK general adult populations. We apply a nonparametric Bayesian method to improve the accuracy of predictions of the US population utility function where the UK results were used as informative priors. The results suggest that drawing extra information from the UK data produces a better estimation of the US population utility than analysing its data separately. The implications of these results will be extremely crucial in countries where valuation studies are limited. [ABSTRACT FROM AUTHOR]

Background: Valuations of health state descriptors such as EQ-5D or SF6D have been conducted in different countries. There is a scope to make use of the results in one country as informative priors to help with the analysis of a study in another, for this to enable better estimation to be obtained in the new country than analyzing its data separately.Methods: Data from 2 EQ-5D valuation studies were analyzed using the time trade-off technique, where values for 42 health states were devised from representative samples of the UK and US populations. A Bayesian non-parametric approach has been applied to predict the health utilities of the US population, where the UK results were used as informative priors in the model to improve their estimation.Results: The findings showed that employing additional information from the UK data helped in the production of US utility estimates much more precisely than would have been possible using the US study data alone.Conclusion: It is very plausible that this method would serve useful in countries where the conduction of large evaluation studies is not very feasible. [ABSTRACT FROM AUTHOR]

Abstract: Background: Conventionally, parametric models were used for health state valuation data. Recently, researchers started to explore the use of nonparametric Bayesian methods in this area. Objectives: We present a nonparametric Bayesian model to estimate a preference-based index for two condition-specific five-dimensional health state classifications, one for asthma (five-dimensional Asthma Quality of Life Utility Index) and the other for overactive bladder (five-dimensional Overactive Bladder Quality of Life-Utility Index). Methods: Samples of 307 and 311 members of the UK general population valued 99 health states selected from a total of 3125 health states defined by each of the measures using the time trade-off technique. The article presents the results of the nonparametric model and compares it with the original model estimated using a conventional parametric random-effects model. The different methods are compared theoretically and in terms of empirical performance across the two data sets. It also reports the effect of respondent characteristics on health state valuations. Results: The nonparametric models were found to be better at predicting health state values within the estimation sample than without in terms of root mean square error and the patterns of standardized residuals. Some respondent characteristics were found to explain variation in health state values, but these did not have a significant effect on the health states values when estimates were adjusted for sample differences with the general population. Conclusions: The nonparametric Bayesian models are theoretically more appropriate than previously used parametric models and provide better utility estimates from the two condition-specific measures. Furthermore, the model is more flexible in estimating the effect of covariates. [Copyright &y& Elsevier]

Abstract: Objectives: There is interest in the extent to which valuations of health may differ between different countries and cultures, but few studies have compared preference values of health states obtained in different countries. The present study applies a nonparametric model to estimate and compare two HK and UK standard gamble values for six-dimensional health state short form (derived from short-form 36 health survey) (SF-6D) health states using Bayesian methods. Methods: The data set is the HK and UK SF-6D valuation studies in which two samples of 197 and 249 states defined by the SF-6D were valued by representative samples of the HK and UK general populations, respectively, both using the standard gamble technique. We estimated a function applicable across both countries that explicitly accounts for the differences between them, and is estimated using the data from both countries. Results: The results suggest that differences in SF-6D health state valuations between the UK and HK general populations are potentially important. In particular, the valuations of Hong Kong were meaningfully higher than those of the United Kingdom for most of the selected SF-6D health states. The magnitude of these country-specific differences in health state valuation depended, however, in a complex way on the levels of individual dimensions. Conclusions: The new Bayesian nonparametric method is a powerful approach for analyzing data from multiple nationalities or ethnic groups to understand the differences between them and potentially to estimate the underlying utility functions more efficiently. [Copyright &y& Elsevier]

Abstract: This paper presents a novel approach to model health state valuations using inverse probability weighting techniques. Our approach makes no assumption on the distribution of health state values, accommodates covariates in a flexible way, eschews parametric assumptions on the relationship between the outcome and the covariates, allows for an undetermined amount of heterogeneity in the estimates and it formally tests and corrects for sample selection biases. The proposed model is semi-parametrically estimated and it is illustrated with health state valuation data collected for Spain using the SF-6D descriptive system. Estimation results indicate that the standard regression model underestimates the utility loss that the Spanish general population assigns to departures from full health, particularly so for severe departures. [Copyright &y& Elsevier]

This article reports the application of a recently described approach to modeling health state valuation data and the impact of the respondent characteristics on health state valuations. The approach applies a nonparametric model to estimate a Bayesian Health Utilities Index Mark 2 (HUI 2) health state valuation algorithm. The data set is the UK HUI 2 valuation study where a sample of 51 states defined by the HUI 2 was valued by a sample of the UK general population using standard gamble. The article reports the application of the nonparametric model and compares it to the original model estimated using a conventional parametric random effects model. Advantages of the nonparametric model are that it can be used to predict scores in populations with different distributions of characteristics than observed in the survey sample and that it allows for the impact of respondent characteristics to vary by health state. The results suggest an important age effect with sex, having some effect, but the remaining covariates having no discernable effect. The article discusses the implications of these results for future applications of the HUI 2 and further work in this field. [ABSTRACT FROM AUTHOR]

This paper reports on the findings from applying a new approach to modelling health state valuation data. The approach applies a nonparametric model to estimate SF-6D health state utility values using Bayesian methods. The data set is the UK SF-6D valuation study where a sample of 249 states defined by the SF-6D (a derivative of the SF-36) was valued by a representative sample of the UK general population using standard gamble. The paper presents the results from applying the nonparametric model and comparing it to the original model estimated using a conventional parametric random effects model. The two models are compared theoretically and in terms of empirical performance. The paper discusses the implications of these results for future applications of the SF-6D and further work in this field. [Copyright &y& Elsevier]

Abstract: It has long been recognised that respondent characteristics can impact on the values they give to health states. This paper reports on the findings from applying a non-parametric approach to estimate the covariates in a model of SF-6D health state values using Bayesian methods. The data set is the UK SF-6D valuation study, where a sample of 249 states defined by the SF-6D (a derivate of the SF-36) was valued by a sample of the UK general population using standard gamble. Advantages of the nonparametric model are that it can be used to predict scores in populations with different distributions of characteristics and that it allows for an impact to vary by health state (whilst ensuring that full health passes through unity). The results suggest an important age effect, with sex, class, education, employment and physical functioning probably having some effect, but the remaining covariates having no discernable effect. Adjusting for covariates in the UK sample made little difference to mean health state values. The paper discusses the implications of these results for policy. [Copyright &y& Elsevier]

Abstract: This paper reports the results of a study that used discrete choice experiment (DCE) methodology to estimate quality weights for a social care outcome measure. To reflect different states of need, a five-dimensional profile measure was developed. Experimental design techniques were used to derive a sample of states for which preferences were elicited. The DCE approach was employed to elicit values and regression techniques used to estimate a model that could predict index scores for all 243 possible outcomes. The utility model, estimated on the basis of 297 responses, had good test and retest reliability and converged with preferences elicited from a rating exercise. [Copyright &y& Elsevier]

This paper reports on the findings of a study to derive a preference-based measure of health from the SF-36 for use in economic evaluation. The SF-36 was revised into a six-dimensional health state classification called the SF-6D. A sample of 249 states defined by the SF-6D have been valued by a representative sample of 611 members of the UK general population, using standard gamble. Models are estimated for predicting health state valuations for all 18,000 states defined by the SF-6D. The econometric modelling had to cope with the hierarchical nature of the data and its skewed distribution. The recommended models have produced significant coefficients for levels of the SF-6D, which are robust across model specification. However, there are concerns with some inconsistent estimates and over prediction of the value of the poorest health states. These problems must be weighed against the rich descriptive ability of the SF-6D, and the potential application of these models to existing and future SF-36 data set. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Preference-based measures of health-related quality-of-life including, but not limited to, the EQ-5D, HUI2 and the SF-6D have been increasingly used in calculations of quality-adjusted life years for cost effectiveness analyses. However, the uncertainty around the measures' value sets is commonly ignored in economic evaluation. There are several types of uncertainties, including methodological, structural, and parameter uncertainties, with the latter being the focus of this review paper. The objective is to highlight the gap in the literature regarding the existence of uncertainty in the value sets, focusing mainly on the EQ-5D and SF-6D. To the best of the authors' knowledge, this is the first systematic review revolving around uncertainty. After searching extensively for studies involving uncertainties in all preference-based measures, the results showed that uncertainty has been approached through different means, while parameter uncertainty has been ignored in most, if not all, cases. These findings suggest that uncertainty should be accounted for when using preference-based measures in economic evaluations. Ignoring this additional information could impact misleadingly on policy decisions.

Background: Experimental studies to develop valuations of health state descriptive systems like EQ-5D or SF-6D need to be conducted in different countries, because social and cultural differences are likely to lead to systematically different valuations. There is a scope utilize the evidence in one country to help with the design and the analysis of a study in another, for this to enable the generation of utility estimates of the second country much more precisely than would have been possible when collecting and analyzing the country's data alone.Methods: We analyze SF-6D valuation data elicited from representative samples corresponding to the Hong Kong (HK) and United Kingdom (UK) general adult populations through the use of the standard gamble technique to value 197 and 249 health states respectively. We apply a nonparametric Bayesian model to estimate a HK value set using the UK dataset as informative prior to improve its estimation. Estimates are compared to a HK value set estimated using HK values alone using mean predictions and root mean square error.Results: The novel method of modelling utility functions permitted the UK valuations to contribute significant prior information to the Hong Kong analysis. The results suggest that using HK data alongside the existing UK data produces HK utility estimates better than using the HK study data by itself.Conclusion: The promising results suggest that existing preference data could be combined with valuation study in a new country to generate preference weights, making own country value sets more achievable for low and middle income countries. Further research is encouraged. [ABSTRACT FROM AUTHOR]

Objectives: This article reports on the findings from applying a recently described approach to modeling health state valuation data and the impact of the respondent characteristics on health state valuations. The approach applies a nonparametric model to estimate a Bayesian six-dimensional health state short form (derived from short-form 36 health survey) health state valuation algorithm., Methods: A sample of 197 states defined by the six-dimensional health state short form (derived from short-form 36 health survey)has been valued by a representative sample of the Hong Kong general population by using standard gamble. The article reports the application of the nonparametric model and compares it to the original model estimated by using a conventional parametric random effects model. The two models are compared theoretically and in terms of empirical performance., Results: Advantages of the nonparametric model are that it can be used to predict scores in populations with different distributions of characteristics than observed in the survey sample and that it allows for the impact of respondent characteristics to vary by health state (while ensuring that full health passes through unity). The results suggest an important age effect with sex, having some effect, but the remaining covariates having no discernible effect., Conclusions: The nonparametric Bayesian model is argued to be more theoretically appropriate than previously used parametric models. Furthermore, it is more flexible to take into account the impact of covariates., (Copyright © 2013, International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc.)