Estimating health state utility values for comorbid health conditions: a synopsis of the current evidence base

Background: Analysts frequently estimate the health state utility values (HSUVs) for combined health conditions (CHCs) using data from cohorts with single health conditions. The methods used to estimated the HSUVs can produce very different results and there is currently no consensus on the most appropriate technique that should be used. Objective: To conduct a detailed critical review of existing empirical literature to gain an understanding of the reasons for differences in results and identify where uncertainty remains that may be addressed by further research. Results: Of the eleven studies identified, ten assessed the additive method, ten the multiplicative method, seven the minimum method, and three the combination model. Two studies evaluated just one of the techniques while the others compared results generated using two or more. The range of the HSUVs can influence general findings and methods are sometimes compared using descriptive statistics that may not be appropriate for assessing predictive ability. None of the proposed methods gave consistently accurate results across the full range of possible HSUVs and the values assigned to normal health influence the accuracy of the methods. Conclusions: While there is no unequivocal evidence for supporting one particular method, the combination linear model appeared to give more accurate results in the studies reviewed. However, before a method can be recommended, research is required in datasets covering the full range of the preference-based indices and health conditions typically defined in decision analytic models. The methods used to assess performance and the statistics used when reporting results require improvement in general

Tails from the Peak District: adjusted censored mixture models of EQ-5D health state utility values

Health state utility data generated using the EQ-5D instrument are typically right bounded at one with a substantial gap to the next set of observations, left bounded by some negative value, and are multi modal. These features present challenges to the estimation of the e¤ect of clinical and socioeconomic characteristics on health utilities. We present an adjusted censored model and then use this in a flexible, mixture modelling framework to address these issues. We demonstrate superior performance of this model compared to linear regression and Tobit censored regression using a dataset from repeated observations of patients with rheumatoid arthritis. We �nd that three latent classes are appropriate in estimating EQ-5D from function, pain and sociodemographic factors. Analysis of utility data should apply methods that recognise the distributional features of the data

Tails from the Peak District: adjusted censored mixture models of EQ-5D health state utility values

Health state utility data generated using the EQ-5D instrument are typically right bounded at one with a substantial gap to the next set of observations, left bounded by some negative value, and are multi modal. These features present challenges to the estimation of the e¤ect of clinical and socioeconomic characteristics on health utilities. We present an adjusted censored model and then use this in a flexible, mixture modelling framework to address these issues. We demonstrate superior performance of this model compared to linear regression and Tobit censored regression using a dataset from repeated observations of patients with rheumatoid arthritis. We �nd that three latent classes are appropriate in estimating EQ-5D from function, pain and sociodemographic factors. Analysis of utility data should apply methods that recognise the distributional features of the data

Tails from the Peak District: adjusted censored mixture models of EQ-5D health state utility values

Health state utility data generated using the EQ-5D instrument are typically right bounded at one with a substantial gap to the next set of observations, left bounded by some negative value, and are multi modal. These features present challenges to the estimation of the e¤ect of clinical and socioeconomic characteristics on health utilities. We present an adjusted censored model and then use this in a flexible, mixture modelling framework to address these issues. We demonstrate superior performance of this model compared to linear regression and Tobit censored regression using a dataset from repeated observations of patients with rheumatoid arthritis. We �nd that three latent classes are appropriate in estimating EQ-5D from function, pain and sociodemographic factors. Analysis of utility data should apply methods that recognise the distributional features of the data

EQ-5D-5L versus 3L: the impact on cost-effectiveness

Objectives To model the relationship between EQ-5D-3L and EQ-5D-5L and examine how differences impact on cost-effectiveness in case studies. Methods We used two datasets that included both EQ-5D-3L and EQ-5D-3L from the same respondents. The EuroQoL dataset (n=3551) included patients with different diseases and a healthy cohort. The National Databank (NDB) dataset included patients with rheumatoid disease (n=5205). We estimated a system of ordinal regressions in each dataset using copula models, to link responses to the 3L instrument to 5L and its tariff, and vice versa. Results were applied to nine cost-effectiveness studies. Results Best-fitting models differed between EuroQoL and NDB datasets in terms of the explanatory variables, copulas and coefficients. In both cases the coefficients of the covariates and latent factor between -3L and -5L were significantly different, indicating that the two instruments are not a uniform realignment of the response levels for most dimensions. In the case studies, moving from 3L to 5L caused a decrease of up to 87% in incremental QALYs gained from effective technologies in almost all cases. ICERs increased, often substantially. Conversely, one technology with a significant mortality gain saw increased incremental QALYs. Conclusion 5L shifts mean utility scores up the utility scale towards full health and compresses them into a smaller range, compared to -3L. Improvements in quality of life are valued less using 5L than with 3L. 3L and 5L can produce substantially different estimates of cost effectiveness. There is no simple proportional adjustment that can be made to reconcile these differences

Mapping to Estimate Health-State Utility from Non–Preference-Based Outcome Measures: An ISPOR Good Practices for Outcomes Research Task Force Report

Economic evaluation conducted in terms of cost per quality-adjusted life-year (QALY) provides information that decision makers find useful in many parts of the world. Ideally, clinical studies designed to assess the effectiveness of health technologies would include outcome measures that are directly linked to health utility to calculate QALYs. Often this does not happen, and even when it does, clinical studies may be insufficient for a cost-utility assessment. Mapping can solve this problem. It uses an additional data set to estimate the relationship between outcomes measured in clinical studies and health utility. This bridges the evidence gap between available evidence on the effect of a health technology in one metric and the requirement for decision makers to express it in a different one (QALYs). In 2014, ISPOR established a Good Practices for Outcome Research Task Force for mapping studies. This task force report provides recommendations to analysts undertaking mapping studies, those that use the results in cost-utility analysis, and those that need to critically review such studies. The recommendations cover all areas of mapping practice: the selection of data sets for the mapping estimation, model selection and performance assessment, reporting standards, and the use of results including the appropriate reflection of variability and uncertainty. This report is unique because it takes an international perspective, is comprehensive in its coverage of the aspects of mapping practice, and reflects the current state of the art. Keywords: economic evaluation, health utility, mapping, quality of life

The cost-effectiveness of sequences of biological disease-modifying antirheumatic drug treatment in England for patients with rheumatoid arthritis who can tolerate methotrexate.

Objective: To ascertain whether strategies of treatment with a biological diseasemodifying antirheumatic drug (bDMARD) were cost-effective in an English setting. Results are presented for those patients with moderate-to-severe rheumatoid arthritis (RA) and those with severe RA. Methods: An economic model to assess the cost-effectiveness of seven bDMARDs was developed. A systematic literature review and network meta-analysis was undertaken to establish relative clinical effectiveness. The results together with estimates of: Health Assessment Questionnaire (HAQ) score following European League Against Rheumatism response; annual costs, and utility, per HAQ band; trajectory of HAQ for patients on bDMARDs; and trajectory of HAQ for patients on non-biologic therapy (NBT) were used to populate the model. Results were presented as those associated with the strategy with the median cost-effectiveness. Supplementary analyses were undertaken assessing the change in cost-effectiveness where only patients with the most severe prognoses on NBT were provided with bDMARD treatment. The cost per QALY values were compared with reported thresholds from the National Institute for Health and Care Excellence of £20,000 to £30,000. Results: In the primary analyses, the cost per QALY of a bDMARD strategy was £41,600 for patients with severe RA and £51,100 for those with moderate-to-severe RA. Under the supplementary analyses the cost per QALY fell to £25,300 for those with severe RA and to £28,500 for those with moderate-to-severe RA. Conclusion: The cost-effectiveness of bDMARDs in RA in England is questionable and only meets current accepted levels in subsets of patients with the worst prognoses

Valuing health at the end of life: an exploratory preference elicitation study

Background: In January 2009, the National Institute for Health and Clinical Excellence (NICE) issued supplementary advice to its Appraisal Committees to be taken into account when appraising life‐ extending ‘end of life’ treatments. This indicated that if certain criteria are met, it may be appropriate to recommend the use of end of life treatments that offer an extension to life even if their reference case incremental cost effectiveness ratios exceed the upper end of the range normally considered acceptable. However, the public consultation carried out by NICE revealed concerns that there is little evidence to support the premise that society is prepared to fund life‐ extending end of life treatments that would not meet the cost effectiveness criteria used for other treatments. Objective: The aim of this exploratory study is to design and pilot an approach to examining whether the policy of giving higher priority to life‐extending end of life treatments (as specified by NICE) than to other types of treatment is consistent with the preferences of members of the general public. Methods: The survey used five scenarios to address the research question posed, each of which involved asking respondents to choose which of two hypothetical patients they would prefer to treat, assuming that the health service has enough funds to treat one but not both of them. The various scenarios were designed so as to control for age‐ and time‐related preferences, and to examine the trade‐off between giving life‐extending and quality of life‐improving treatments to end of life patients. Follow‐up questions were used to elicit additional information about the reasons for respondents’ choices. The survey was administered using face‐to‐face interviews. Results: Interviews were completed by a convenience sample of 21 members of non‐academic staff and postgraduate students at the University of Sheffield. The majority of respondents chose to treat the patient who is closest to their expected death or whose disease progression has been most sudden. Very few respondents expressed indifference or unwillingness to choose between the two patients. Discussion: The most common driver of respondents’ choices was a concern about how much time patients have to ‘prepare for death’, which indicates, prima facie, support for NICE’s end of life policy. However, the ways in which information on patient age and the timing of treatment affected responses was not straightforward and in some cases contrary to the authors’ conjectured predictions. The results also suggest that improving quality of life is at least as, if not more, important than extending life in the end of life scenario