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Objective To investigate the responsiveness of and correlation between the EQ-5D-5L and the QOLIE-31P in patients with epilepsy, and develop a mapping function to predict EQ-5D-5L values based on the QOLIE-31P for use in economic evaluations. Methods The dataset was derived from two clinical trials, the ZMILE study in the Netherlands and the SMILE study in the UK. In both studies, patients’ quality of life using the EQ-5D-5L and QOLIE-31P was measured at baseline and 12 months follow-up. Spearman’s correlations, effect sizes (EF) and standardized response means (SRM) were calculated for both the EQ-5D-5L and QOLIE-31P domains and sub scores. Mapping functions were derived using ordinary least square (OLS) and censored least absolute deviations models. Results A total of 509 patients were included in this study. Low to moderately strong significant correlations were found between both instruments. The EQ-5D-5L showed high ceiling effects and small EFs and SRMs, whereas the QOLIE-31P did not show ceiling effects and also showed small to moderate EFs and SRMs. Results of the different mapping functions indicate that the highest adjusted R 2 we were able to regress was 0.265 using an OLS model with squared terms, leading to a mean absolute error of 0.103. Conclusions Results presented in this study emphasize the shortcomings of the EQ-5D-5L in epilepsy and the importance of the development of condition-specific preference-based instruments which can be used within the QALY framework. In addition, the usefulness of the constructed mapping function in economic evaluations is questionable. Electronic supplementary material The online version of this article (doi:10.1007/s10198-017-0928-0) contains supplementary material, which is available to authorized users.

Objectives Some argue that generic preference-based measures (PBMs) are not sensitive to certain disease-specific improvements. To overcome this problem, new condition-specific PBMs (CS-PBMs) are being developed, but it is not yet clear how such measures compare with existing generic PBMs. Method We generated CS-PBMs from three condition-specific questionnaires (Health Assessment Questionnaire for arthritis, Quality of Life Questionnaire for Cancer 30 for cancer, and Multiple Sclerosis Impact Scale 29 for multiple sclerosis). First, the questionnaires were reduced in content, and then, a time trade-off study was conducted in the general public (N = 402) to obtain weights associated with the dimensions and levels of the new questionnaire. Finally, we compared utilities obtained by using the CS-PBMs with utilities obtained by using the EuroQol five-dimensional (EQ-5D) questionnaire in four data sets. Results Utility values generated by the CS-PBMs were higher than those of the EQ-5D questionnaire. The Health Assessment Questionnaire–based measure for arthritis proved to be insensitive to comorbidities. Measures based on the Multiple Sclerosis Impact Scale 29 and the Quality of Life Questionnaire for Cancer 30 discriminated comorbidities and side effect equally well as the EQ-5D questionnaire and were more sensitive than the EQ-5D questionnaire for mild impairments. Conclusions The introduction of PBMs that are specific to a certain disease may have the merit of sensitivity to disease-specific effects of interventions. That gain, however, is traded off to the loss of comparability of utility values and, in some cases, insensitivity to side effects and comorbidity. The use of a CS-PBM for cost-utility analysis is warranted only under strict conditions.

Objectives: The Medical Outcomes Study HIV Health Survey (MOS-HIV) is frequently used in HIV clinical trials; however, scores generated from the MOS-HIV are not suited for cost-effectiveness analyses as they do not assign utility values to health states. Our objective was to estimate and externally validate several mapping algorithms to predict Health Utilities Index Mark 3 (HUI3) and EQ-5D-3L utility values from the MOS-HIV. Methods: We developed and validated mapping algorithms using data from two HIV clinical trials. Data from the first trial (n = 367) formed the estimation data set for the HUI3 (4,610 observations) and EQ-5D-3L (4,662 observations) mapping algorithms; data from the second trial (n = 168) formed the HUI3 (1,135 observations) and EQ-5D-3L (1,152 observations) external validation data set. We compared ordinary least squares (OLS) models of increasing complexity with the more flexible two-part, beta regression, and finite mixture models. We assessed model performance using mean absolute error (MAE) and mean squared error (MSE). Results: The OLS model that used MOS-HIV dimension scores along with squared terms gave the best HUI3 predictions (mean observed 0.84; mean predicted 0.80; MAE 0.0961); the finite mixture model gave the best EQ-5D-3L predictions (mean observed 0.90; mean predicted 0.88; MAE 0.0567). All models produced higher prediction errors at the lower end of the HUI3 and EQ-5D-3L score ranges (

The validity of the SF-6D, a preference-based measure of health-related quality of life, is not well explored in the context of spinal cord injury (SCI). The aim of this analysis was to assess appropriate measurement properties of the SF-6D in a sample of individuals living with SCI.Longitudinal data from the Rick Hansen Spinal Cord Injury Registry were used. Responses to the 36-item short-form health survey were transformed into SF-6D utility scores. We investigated practicality, floor and ceiling effects, and responsiveness to change. Responsiveness to change was explored using three different anchors that reflected changes in self-reported health, functional independence, and life satisfaction. Discriminative validity was assessed by ten a priori defined hypotheses, with a distinction made between 'strong' and 'weak' hypotheses.Three hundred and fifty-eight individuals with SCI were included in this analysis. Practicality was deemed acceptable based on a completion rate of 94 %. The SF-6D showed low responsiveness to detect important health changes over time, and differences in responsiveness were found between individuals with paraplegia and tetraplegia. All five strong hypotheses and three weak hypotheses were confirmed.The SF-6D demonstrated good practicality and discriminative validity in this sample. The failure to detect self-reported and clinically important health changes requires further consideration. Comparative performance of the SF-6D (i.e., how the SF-6D performs against other preference-based measures) is unknown in the SCI context and requires further research.

Objective Resource allocation informed by cost-utility analysis requires that the benefits be comparable across patient groups and interventions. One option is to recommend the use of one generic utility measure, but this raises the issue of comparability when the preferred measure is inappropriate or unavailable. Many cancer trials do not include generic measures such as the EuroQol five-dimensional (EQ-5D) questionnaire and instead include condition-specific measures and use these to generate utility estimates. We analyze the comparability of generic, condition-specific, and mapped utility values for a multiple myeloma cancer patient data set. Methods Generic EQ-5D, condition-specific EORTC-8D, and EQ-5D utility values mapped from the EORTC QLQ-C30 were compared by using psychometric and statistical analysis to determine discrimination across severity groups, responsiveness, and agreement. Results Generic, condition-specific, and mapped utility estimates were responsive over time and show discriminative validity. The EQ-5D had higher responsiveness and detected a greater change across severity groups and treatment periods than did the EORTC-8D but has a higher proportion of responses at full health (12.8%). Differences in the EQ-5D and the EORTC-8D were due at least in part to differences in the classification system. Mapped EQ-5D estimates had a smaller SD and do not reflect the severe range of health states reported by using the EQ-5D. Conclusions Our findings suggest that condition-specific EORTC-8D or mapped EQ-5D utility estimates are broadly comparable to directly obtained EQ-5D utilities for a multiple myeloma patient data set. However, EORTC-8D estimates captured changes in quality of life for patients in mild health states that were not captured by the EQ-5D, but estimated lower utility gains than did the use of the EQ-5D directly.

Objectives: SF-6D is a preference-based measure of health developed to estimate utility values from the SF-36. The aim of this study was to estimate a weighting system for the SF-6D health states representing the preferences of a sample of the Southern Brazilian general population. Methods: A sample of 248 health states defined by the SF-6D was valued by a sample of the southern Brazilian population using the standard gamble. Mean and individual level multivariate regression models were fitted to the standard gamble valuation data to estimate preference weights for all SF-6D health states. The models were compared with those estimated in the UK study. Results: Five hundred twenty-eight participants were interviewed, but 58 (11%) were excluded for failing to value the worst state. Data from 469 subjects producing 2696 health states valuations were used in the regression analysis. In contrast to the best performing model for the UK data, the best performing model for the Brazilian data was a random effects model using only the main effects variables, highlighting the importance of adopting a country-specific algorithm to derive SF-6D health states values. Inconsistent coefficients were merged to produce the final recommended model, which has all significant coefficients and a mean absolute difference between observed and predicted standard gamble values of 0.07. Conclusions: The results provide the first population-based value set for Brazil for SF-6D health states, making it possible to generate quality-adjusted life years for cost-utility studies using regional data. Besides, utility weights derived using the preferences of a sample from a southern Brazilian population can be derived from existing SF-36 data sets.

Objectives This study establishes the South Korean population-based preference weights for EQ-5D based on values elicited from a representative national sample using the time trade-off (TTO) method. Methods The data for this paper came from a South Korean EQ-5D valuation study where 1307 representative respondents were invited to participate and a total of 101 health states defined by the EQ-5D descriptive system were directly valued. Both aggregate and individual level modeling were conducted to generate values for all 243 health states defined by EQ-5D. Various regression techniques and model specifications were also examined in order to produce the best fit model. Final model selection was based on minimizing the difference between the observed and estimated value for each health state. Results The N3 model yielded the best fit for the observed TTO value at the aggregate level. It had a mean absolute error of 0.029 and only 15 predictions out of 101 had errors exceeding 0.05 in absolute magnitude. Conclusions The study successfully establishes South Korean population-based preference weights for the EQ-5D. The value set derived here is based on a representative population sample, limiting the interpolation space and possessing better model performance. Thus, this EQ-5D value set should be given preference for use with the South Korean population.

Objective: Recent years have seen increasing interest in the use of ordinal methods to elicit health state utility values as an alternative to conventional methods such as standard gamble and time trade-off. However, in order to use these health state values in cost effectiveness analysis using cost per quality adjusted life year (QALY) analysis, these values must be anchored on the full health-dead scale. This study addresses this challenge and examines how rank and discrete choice experiment data can be used to elicit health state utility values anchored on the full health-dead scale and compares the results to time trade-off (TTO) results. Methods: Two valuation studies were conducted using identical methods for two health state classification systems: asthma and overactive bladder. Each valuation study involved interviews of 300 members of the general population using ranking and TTO plus a postal survey using discrete choice experiment sent to all consenting interviewees and a "cold" sample of the general population who were not interviewed. Results: Overall DCE produced different results from ranking and time trade-off, whereas ranking produced similar results to TTO in one study, but not the other. Conclusions: Ordinal methods offer a promising alternative to conventional cardinal methods of standard gamble and TTO. However, the results do not appear to be robust across different health state classification systems and potentially different medical conditions. There remains a large and important research agenda to address.

ObjectiveExisting estimates of human immunodeficiency virus (HIV)-related health state utilities are inadequate for comparing alternative treatments on the basis of regimen-specific attributes such as dosing requirements or tolerability. The objective of this study was to examine the marginal impact of dosing, adverse events (AEs), and other factors on patients' health state utilities.MethodsTreatment naive and experienced HIV patients participating in five open-label trials of highly active antiretroviral therapy (HAART) completed the 36-Item Short Form Health Survey (SF-36) instrument at various time points. SF-36 responses were converted to utilities using a previously reported algorithm. Expected utilities were estimated as a function of patient demographics, regimen attributes, disease status, and AEs using a mixed-effects maximum likelihood model. Mean utilities for five HIV health states were derived from predicted patient utilities.ResultsNegative predictors of utility included greater age (−0.001), prior acquired immune deficiency syndrome-defining events (−0.036), female gender (−0.038), and injection drug use (−0.056; P < 0.01 for all). Utility also depended on CD4+ cell count (P < 0.01), but not the presence of undetectable viral load. Regimen attributes were marginally associated with changes in utility. Depression was associated with the largest decrease in utility (−0.054, P < 0.001) among the AEs examined. Using the model to generate predicted utilities from the sample provided mean estimates ranging from 0.742 (SD 0.058) to 0.798 (0.052) for CD4+ counts between 0 and 99 and ≥500 cells/mm3, respectively.ConclusionsHIV patients' health-related quality of life may be substantially affected by clinically relevant patient-, disease-, and treatment-related factors, such as injection drug use, disease status, food/drink restrictions, and AEs.

Objective: To set out the methodological process for using Rasch analysis alongside traditional psychometric methods in the development of a health state classification that is amenable to valuation. Methods: The overactive bladder questionnaire is used to illustrate a four step process for deriving a reduced health state classification from an existing nonpreference based health related quality of life instrument. Step I excludes items that do not meet the initial validation process and step II uses criteria based on Rasch analysis and psychometric testing to select the final items for the health state classification. In step III, item levels are examined and Rasch analysis is used to explore the possibility of reducing the number of item levels. Step IV repeats steps I to III on alternative data sets in order to validate the selection of items for the health state classification. Conclusions: The techniques described enable the construction of a health state classification amenable for valuation exercises that will allow the derivation of preference weights. Thus, the health related quality of life of patients with conditions, like overactive bladder, can be valued and quality adjustment weights such as quality adjusted life years derived.

This paper presents a systematic review of current practice in mapping between nonpreference based measures and generic preference-based measures. It reviews the studies identified by a systematic search of the published literature and the grey literature. This review seeks to address the feasibility and overall validity of this approach, the circumstances when it should be considered and to bring together any lessons for future mapping studies.

The majority of quality of life instruments are not preference-based measures and so cannot be used within cost utility analysis. The Asthma Quality of Life Questionnaire (AQLQ) is one such instrument. The aim of this study was to develop a health state classification that is amenable to valuation from the AQLQ. Rasch models were applied to samples of responders to the AQLQ with the aim of i) selecting a number of items for a preference based utility measure (AQL-5D), ii) reducing the number of levels for each item to a more manageable number of levels for establishing AQL-5D. Selection of items for the evaluation survey was supported with conventional psychometric criteria for item selection (feasibility, internal consistency, floor and ceiling effects, responsiveness and regression against overall health). The role of Rasch analysis in reducing the number of item levels to a preconceived target number of levels proved unsuccessful. However, Rasch analysis proved to be a useful tool in assisting in the initial process of selecting items from an existing HRQL instrument in the construction of AQL-5D. The method is recommended for use alongside conventional psychometric testing to aid in the development of preference-based measures.