scholarly journals Valuation Survey of EQ-5D-Y Based on the International Common Protocol: Development of a Value Set in Japan

2021 ◽  
pp. 0272989X2110018
Author(s):  
Takeru Shiroiwa ◽  
Shunya Ikeda ◽  
Shinichi Noto ◽  
Takashi Fukuda ◽  
Elly Stolk
Keyword(s):  
Discrete Choice ◽  
Health Care Policy ◽  
Mixed Logit ◽  
Health State ◽  
Choice Data ◽  
Social Functions ◽  
Value Set ◽  
A Value ◽  
Value Sets ◽  
Utility Scale

Background EQ-5D-Y is a preference-based measure for children and adolescents (aged 8–15 y). This is the first study to develop an EQ-5D-Y value set for converting EQ-5D-Y responses to index values. Methods We recruited 1047 respondents (aged 20–79 y) from the general population, stratified by gender and age group, in 5 Japanese cities. All data were collected through face-to-face surveys. Respondents were asked to value EQ-5D-Y states for a hypothetical 10-y-old child from a proxy perspective using composite time tradeoff (cTTO) and a discrete choice experiment (DCE). The discrete choice data were analyzed using a mixed logit model. Latent DCE values were then converted to a 0 (death)/1 (full health) scale by mapping them to the cTTO values. Results The mean observed cTTO value of the worst health state [33333] was 0.20. Analysis of the DCE data showed that the coefficients of the domains related to mental functions (“Having pain or discomfort” and “Feeling worried, sad, or unhappy”) were larger than those for the domains related to physical and social functions. By converting latent DCE values to a utility scale, we constructed a value set for EQ-5D-Y. No inconsistencies were observed. The minimum predicted score was 0.288 [33333], and the second-best score was 0.957 [12111]. Conclusion A value set for EQ-5D-Y was successfully constructed. This is the first survey of an EQ-5D-Y value set. Interpreting the differences between EQ-5D-Y and EQ-5D-5L value sets is a future task with implications for health care policy.

scholarly journals The Online Elicitation of Personal Utility Functions (OPUF) tool: a new method for valuing health states

2022 ◽  
Vol 7 ◽  
pp. 14
Author(s):  
Paul Schneider ◽  
Ben van Hout ◽  
Marike Heisen ◽  
John Brazier ◽  
Nancy Devlin
Keyword(s):  
Utility Functions ◽  
Health State ◽  
Group Level ◽  
Health States ◽  
Online Tool ◽  
Value Set ◽  
Individual Level ◽  
Personal Utility ◽  
Value Sets ◽  
The Individual

Introduction Standard valuation methods, such as TTO and DCE are inefficient. They require data from hundreds if not thousands of participants to generate value sets. Here, we present the Online elicitation of Personal Utility Functions (OPUF) tool; a new type of online survey for valuing EQ-5D-5L health states using more efficient, compositional elicitation methods, which even allow estimating value sets on the individual level. The aims of this study are to report on the development of the tool, and to test the feasibility of using it to obtain individual-level value sets for the EQ-5D-5L. Methods We applied an iterative design approach to adapt the PUF method, previously developed by Devlin et al., for use as a standalone online tool. Five rounds of qualitative interviews, and one quantitative pre-pilot were conducted to get feedback on the different tasks. After each round, the tool was refined and re-evaluated. The final version was piloted in a sample of 50 participants from the UK. A demo of the EQ-5D-5L OPUF survey is available at: https://eq5d5l.me Results On average, it took participants about seven minutes to complete the OPUF Tool. Based on the responses, we were able to construct a personal EQ-5D-5L value set for each of the 50 participants. These value sets predicted a participants' choices in a discrete choice experiment with an accuracy of 80%. Overall, the results revealed that health state preferences vary considerably on the individual-level. Nevertheless, we were able to estimate a group-level value set for all 50 participants with reasonable precision. Discussion We successfully piloted the OPUF Tool and showed that it can be used to derive a group-level as well as personal value sets for the EQ-5D-5L. Although the development of the online tool is still in an early stage, there are multiple potential avenues for further research.

scholarly journals Crosswalk EQ-5D-5L Value Set for Slovenia

2020 ◽  
Vol 59 (3) ◽  
pp. 189-194 ◽  
Author(s):  
Valentina Prevolnik Rupel ◽  
Marko Ogorevc
Keyword(s):  
General Population ◽  
Health State ◽  
Health States ◽  
Value Set ◽  
Euroqol Group ◽  
A Value ◽  
Distribution Of Values ◽  
Severe Health State ◽  
The Mean ◽  
Informational Value

AbstractIntroductionDue to the availability of the EQ-5D-5L instrument official translation into Slovenian its use is widespread in Slovenia. However, the health profiles obtained in many studies cannot be ascribed their appropriate values as the EQ-5D-5L value set does not yet exist in Slovenia. Our aim was to estimate an interim EQ-5D-5L value set for Slovenia using the crosswalk methodology developed by the EuroQol Group on the basis of the EQ-5D-3L Slovenian TTO value set. Our secondary aim was to compare the interim values obtained with the EQ-5D-3L Slovenian values.MethodsTo obtain a Slovenian interim EQ-5D-5L value set, we applied the crosswalk methodology developed by the EuroQol Group to the Slovenian EQ-5D-3L TTO value set. We examined the differences between values by comparing the mean 3L and 5L value scores and the distribution of values across all respondents.ResultsBy definition, 3-level and 5-level versions have the same range (from 1 to −0.495) and a health state coded 22222 in the 3-level version corresponds to 33333 in the 5-level version. While the addition of a “slight” severity level (22222) in the 5-level version has a low informational value, the addition of a “severe” health state (44444) covers larger range of the scale. The 5-level version results in fewer health states being valued below 0 and above 0.8.ConclusionThe EQ-5D-5L value set, based on the crosswalk methodology, should be used until a value set for the EQ-5D-5L is derived from preferences elicited directly from a representative sample of the Slovenian general population.

scholarly journals Setting Dead at Zero: Applying Scale Properties to the QALY Model

2018 ◽  
Vol 38 (6) ◽  
pp. 627-634 ◽  
Author(s):  
Bram Roudijk ◽  
A. Rogier T. Donders ◽  
Peep F.M. Stalmeier
Keyword(s):  
Decision Making ◽  
Research Question ◽  
Health Utility ◽  
Discrete Choice Experiments ◽  
Health State ◽  
Ratio Scale ◽  
Health States ◽  
A Value ◽  
Scale Properties ◽  
Utility Scale

Introduction. Scaling severe states can be a difficult task. First, the method of measurement affects whether a health state is considered better or worse than dead. Second, in discrete choice experiments, different models to anchor health states on 0 (dead) and 1 (perfect health) produce varying amounts of health states worse than dead. Research Question. Within the context of the quality-adjusted life year (QALY) model, this article provides insight into the value assigned to dead and its consequences for decision making. Our research questions are 1) what are the arguments set forth to assign dead the number 0 on the health–utility scale? And 2) what are the effects of the position of dead on the health–utility scale on decision making? Methods. A literature review was conducted to explore the arguments set forth to assign dead a value of 0 in the QALY model. In addition, scale properties and transformations were considered. Results. The review uncovered several practical and theoretical considerations for setting dead at 0. In the QALY model, indifference between 2 health episodes is not preserved under changes of the origin of the duration scale. Ratio scale properties are needed for the duration scale to preserve indifferences. In combination with preferences and zero conditions for duration and health, it follows that dead should have a value of 0. Conclusions. The health–utility and duration scales have ratio scale properties, and dead should be assigned the number 0. Furthermore, the position of dead should be carefully established, because it determines how life-saving and life-improving values are weighed in cost–utility analysis.

scholarly journals Valuing EQ-5D-Y-3L Health States Using a Discrete Choice Experiment: Do Adult and Adolescent Preferences Differ?

2021 ◽  
pp. 0272989X2199960
Author(s):  
David J. Mott ◽  
Koonal K. Shah ◽  
Juan Manuel Ramos-Goñi ◽  
Nancy J. Devlin ◽  
Oliver Rivero-Arias
Keyword(s):  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Mixed Logit ◽  
Self Care ◽  
Attribute Importance ◽  
Health States ◽  
Two Samples ◽  
Value Sets ◽  
Anxiety Depression

Background An important question in the valuation of children’s health is whether the preferences of younger individuals should be captured within value sets for measures that are aimed at them. This depends on whether younger individuals can complete valuation exercises and whether their preferences differ from those of adults. This study compared the preferences of adults and adolescents for EQ-5D-Y-3L health states using latent scale values elicited from a discrete choice experiment (DCE). Methods An online DCE survey, comprising 15 pairwise choices, was provided to samples of UK adults and adolescents (aged 11–17 y). Adults considered the health of a 10-year-old child, whereas adolescents considered their own health. Mixed logit models were estimated, and comparisons were made using relative attribute importance (RAI) scores and a pooled model. Results In total, 1000 adults and 1005 adolescents completed the survey. For both samples, level 3 in pain/discomfort was most important, and level 2 in self-care the least important, based on the relative magnitudes of coefficients. The RAI scores (normalized on self-care) indicated that adolescents gave less weight relative to adults to usual activities (1.18 v. 1.51; P < 0.05), pain/discomfort (1.77 v. 3.12; P < 0.01), and anxiety/depression (1.64 vs. 2.65; P < 0.01). The pooled model indicated evidence of differences between the two samples in both levels in pain/discomfort and anxiety/depression. Limitations The perspective of the DCE task differed between the 2 samples, and no data were collected to anchor the DCE data to generate value sets. Conclusions Adolescents could complete the DCE, and their preferences differed from those of adults taking a child perspective. It is important to consider whether their preferences should be incorporated into value sets.

scholarly journals Exploring Differences between TTO and DCE in the Valuation of Health States

2016 ◽  
Vol 37 (3) ◽  
pp. 273-284 ◽  
Author(s):  
Angela Robinson ◽  
Anne E. Spencer ◽  
José Luís Pinto-Prades ◽  
Judith A. Covey
Keyword(s):  
Discrete Choice ◽  
Discrete Choice Experiments ◽  
Matching Task ◽  
Health State ◽  
Choice Data ◽  
Health States ◽  
The United Kingdom ◽  
Recent Interest ◽  
Matching Procedure ◽  
Health State Utility Values

There is recent interest in using discrete choice experiments (DCEs) to derive health state utility values, and results can differ from time tradeoff (TTO). Clearly, DCE is “choice based,” whereas TTO is generally considered a “matching” task. We explore whether procedural adaptations to the TTO, which make the method more closely resemble a DCE, make TTO and choice converge. In particular, we test whether making the matching procedure in TTO less “transparent” to the respondent reduces disparities between TTO and DCE. We designed an interactive survey that was hosted on the Internet, and 2022 interviews were achieved in the United Kingdom in a representative sample of the population. We found a marked divergence between TTO and DCE, but this was not related to the “transparency” of the TTO procedure. We conclude that a difference in the error structure between TTO and choice and that factors other than differences in utility are affecting choices is driving the divergence. The latter has fundamental implications for the way choice data are analyzed and interpreted.

scholarly journals Interval Continuous Plant Identification from Value Sets

2012 ◽  
Vol 2012 ◽  
pp. 1-32
Author(s):  
R. Hernández ◽  
J. A. García ◽  
C. Mañoso
Keyword(s):  
Plant Identification ◽  
Value Set ◽  
A Value ◽  
Complete Arc ◽  
Continuous Plant ◽  
Value Sets

This paper shows how to obtain the values of the numerator and denominator Kharitonov polynomials of an interval plant from its value set at a given frequency. Moreover, it is proven that given a value set, all the assigned polynomials of the vertices can be determined if and only if there is a complete edge or a complete arc lying on a quadrant. This algorithm is nonconservative in the sense that if the value-set boundary of an interval plant is exactly known, and particularly its vertices, then the Kharitonov rectangles are exactly those used to obtain these value sets.

United States Utility Algorithm for the EORTC QLU-C10D, a Multiattribute Utility Instrument Based on a Cancer-Specific Quality-of-Life Instrument

2021 ◽  
pp. 0272989X2110035
Author(s):  
Dennis A. Revicki ◽  
Madeleine T. King ◽  
Rosalie Viney ◽  
A. Simon Pickard ◽  
Rebecca Mercieca-Bebber ◽  
...  
Keyword(s):  
Quality Of Life ◽  
United States ◽  
Emotional Functioning ◽  
Health State ◽  
Life Questionnaire ◽  
Multiattribute Utility ◽  
Value Set ◽  
Eortc Qlq C30 ◽  
Eortc Qlq

Background The EORTC QLU-C10D is a multiattribute utility measure derived from the cancer-specific quality-of-life questionnaire, the EORTC QLQ-C30. The QLU-C10D contains 10 dimensions (physical, role, social and emotional functioning, pain, fatigue, sleep, appetite, nausea, bowel problems). The objective of this study was to develop a United States value set for the QLU-C10D. Methods A US online panel was quota recruited to achieve a representative sample for sex, age (≥18 y), race, and ethnicity. Respondents undertook a discrete choice experiment, each completing 16 choice-pairs, randomly assigned from a total of 960 choice-pairs. Each pair included 2 QLU-C10D health states and duration. Data were analyzed using conditional logistic regression, parameterized to fit the quality-adjusted life-year framework. Utility weights were calculated as the ratio of each dimension-level coefficient to the coefficient for life expectancy. Results A total of 2480 panel members opted in, 2333 (94%) completed at least 1 choice-pair, and 2273 (92%) completed all choice-pairs. Within dimensions, weights were generally monotonic. Physical functioning, role functioning, and pain were associated with the largest utility weights. Cancer-specific dimensions, such as nausea and bowel problems, were associated with moderate utility decrements, as were general issues such as problems with emotional functioning and social functioning. Sleep problems and fatigue were associated with smaller utility decrements. The value of the worst health state was 0.032, which was slightly greater than 0 (equivalent to being dead). Conclusions This study provides the US-specific value set for the QLU-C10D. These estimated health state scores, based on responses to the EORTC QLQ-C30 questionnaire, can be used to evaluate the cost-utility of oncology treatments.

scholarly journals Estimating an EQ-5D-3L Value Set for Romania Using Time Trade-Off

2021 ◽  
Vol 18 (14) ◽  
pp. 7415
Author(s):  
Marian Sorin Paveliu ◽  
Elena Olariu ◽  
Raluca Caplescu ◽  
Yemi Oluboyede ◽  
Ileana-Gabriela Niculescu-Aron ◽  
...  
Keyword(s):  
Health Technology Assessment ◽  
Technology Assessment ◽  
Health Technology ◽  
Assessment Process ◽  
Computer Assisted ◽  
Health State ◽  
Health States ◽  
Value Set ◽  
Health Technologies ◽  
Related Quality

Objective: To provide health-related quality of life (HRQoL) data to support health technology assessment (HTA) and reimbursement decisions in Romania, by developing a country-specific value set for the EQ-5D-3L questionnaire. Methods: We used the cTTO method to elicit health state values using a computer-assisted personal interviewing approach. Interviews were standardized following the most recent version of the EQ-VT protocol developed by the EuroQoL Foundation. Thirty EQ-5D-3L health states were randomly assigned to respondents in blocks of three. Econometric modeling was used to estimate values for all 243 states described by the EQ-5D-3L. Results: Data from 1556 non-institutionalized adults aged 18 years and older, selected from a national representative sample, were used to build the value set. All tested models were logically consistent; the final model chosen to generate the value set was an interval regression model. The predicted EQ-5D-3L values ranged from 0.969 to 0.399, and the relative importance of EQ-5D-3L dimensions was in the following order: mobility, pain/discomfort, self-care, anxiety/depression, and usual activities. Conclusions: These results can support reimbursement decisions and allow regional cross-country comparisons between health technologies. This study lays a stepping stone in the development of a health technology assessment process more driven by locally relevant data in Romania.

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